solve the pdf concate error

2024-10-13 07:36:36 +00:00
255 changed files with 2318 additions and 36333 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,6 +0,0 @@
-.venv
-.github
-.vscode
-gpt_log
-tests
-README.md
--- a/.github/workflows/build-with-all-capacity-beta.yml
+++ b/.github/workflows/build-with-all-capacity-beta.yml
@@ -1,14 +1,14 @@
 # https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-latex-arm
+name: build-with-all-capacity-beta

 on:
  push:
    branches:
-      - "master"
+      - 'master'

 env:
  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_with_latex_arm
+  IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta

 jobs:
  build-and-push-image:
@@ -18,17 +18,11 @@ jobs:
      packages: write

    steps:
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
      - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3

      - name: Log in to the Container registry
-        uses: docker/login-action@v3
+        uses: docker/login-action@v2
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
@@ -41,11 +35,10 @@ jobs:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}

      - name: Build and push Docker image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v4
        with:
          context: .
          push: true
-          platforms: linux/arm64
-          file: docs/GithubAction+NoLocal+Latex
+          file: docs/GithubAction+AllCapacityBeta
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-chatglm.yml
+++ b/.github/workflows/build-with-chatglm.yml
@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: build-with-chatglm
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_chatglm_moss
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          push: true
+          file: docs/GithubAction+ChatGLM+Moss
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-jittorllms.yml
+++ b/.github/workflows/build-with-jittorllms.yml
@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: build-with-jittorllms
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_jittorllms
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          push: true
+          file: docs/GithubAction+JittorLLMs
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/conda-pack-windows.yml
+++ b/.github/workflows/conda-pack-windows.yml
@@ -1,56 +0,0 @@
-name: Create Conda Environment Package
-
-on:
-  workflow_dispatch:
-
-jobs:
-  build:
-    runs-on: windows-latest
-    
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
-      
-    - name: Setup Miniconda
-      uses: conda-incubator/setup-miniconda@v3
-      with:
-        auto-activate-base: true
-        activate-environment: ""
-        
-    - name: Create new Conda environment
-      shell: bash -l {0}
-      run: |
-        conda create -n gpt python=3.11 -y
-        conda activate gpt
-        
-    - name: Install requirements
-      shell: bash -l {0}
-      run: |
-        conda activate gpt
-        pip install -r requirements.txt
-        
-    - name: Install conda-pack
-      shell: bash -l {0}
-      run: |
-        conda activate gpt
-        conda install conda-pack -y
-        
-    - name: Pack conda environment
-      shell: bash -l {0}
-      run: |
-        conda activate gpt
-        conda pack -n gpt -o gpt.tar.gz
-      
-    - name: Create workspace zip
-      shell: pwsh
-      run: |
-        mkdir workspace
-        Get-ChildItem -Exclude "workspace" | Copy-Item -Destination workspace -Recurse
-        Remove-Item -Path workspace/.git* -Recurse -Force -ErrorAction SilentlyContinue
-        Copy-Item gpt.tar.gz workspace/ -Force
-    
-    - name: Upload packed files
-      uses: actions/upload-artifact@v4
-      with:
-        name: gpt-academic-package
-        path: workspace
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -7,7 +7,7 @@
 name: 'Close stale issues and PRs'
 on:
  schedule:
-    - cron: '*/30 * * * *'
+    - cron: '*/5 * * * *'

 jobs:
  stale:
@@ -19,6 +19,7 @@ jobs:
    steps:
      - uses: actions/stale@v8
        with:
-          stale-issue-message: 'This issue is stale because it has been open 100 days with no activity. Remove stale label or comment or this will be closed in 7 days.'
+          stale-issue-message: 'This issue is stale because it has been open 100 days with no activity. Remove stale label or comment or this will be closed in 1 days.'
          days-before-stale: 100
-          days-before-close: 7
+          days-before-close: 1
+          debug-only: true
--- a/.gitignore
+++ b/.gitignore
@@ -161,7 +161,3 @@ temp.*
 objdump*
 *.min.*.js
 TODO
-experimental_mods
-search_results
-gg.docx
-unstructured_reader.py
--- a/27
+++ b/27
@@ -3,38 +3,37 @@
 # - 如何构建: 先修改 `config.py`， 然后 `docker build -t gpt-academic . `
 # - 如何运行(Linux下): `docker run --rm -it --net=host gpt-academic `
 # - 如何运行(其他操作系统，选择任意一个固定端口50923): `docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic `
+FROM python:3.11

-FROM ghcr.io/astral-sh/uv:python3.12-bookworm

 # 非必要步骤，更换pip源 （以下三行，可以删除）
 RUN echo '[global]' > /etc/pip.conf && \
    echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
    echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf

-# 语音输出功能（以下1,2行更换阿里源，第3,4行安装ffmpeg，都可以删除）
-RUN sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources && \
-    sed -i 's/security.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources && \
-    apt-get update
+
+# 语音输出功能（以下两行，第一行更换阿里源，第二行安装ffmpeg，都可以删除）
+RUN UBUNTU_VERSION=$(awk -F= '/^VERSION_CODENAME=/{print $2}' /etc/os-release); echo "deb https://mirrors.aliyun.com/debian/ $UBUNTU_VERSION main non-free contrib" > /etc/apt/sources.list; apt-get update
 RUN apt-get install ffmpeg -y
-RUN apt-get clean
+

 # 进入工作路径（必要）
 WORKDIR /gpt

+
 # 安装大部分依赖，利用Docker缓存加速以后的构建 （以下两行，可以删除）
 COPY requirements.txt ./
-RUN uv venv --python=3.12 && uv pip install --verbose -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-ENV PATH="/gpt/.venv/bin:$PATH"
-RUN python -c 'import loguru'
+RUN pip3 install -r requirements.txt
+

 # 装载项目文件，安装剩余依赖（必要）
 COPY . .
-RUN uv pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+RUN pip3 install -r requirements.txt

-# # 非必要步骤，用于预热模块（可以删除）
-RUN python -c 'from check_proxy import warm_up_modules; warm_up_modules()'

-ENV CGO_ENABLED=0
+# 非必要步骤，用于预热模块（可以删除）
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+

 # 启动（必要）
-CMD ["bash", "-c", "python main.py"]
+CMD ["python3", "-u", "main.py"]
--- a/README.md
+++ b/README.md
@@ -1,13 +1,8 @@
 > [!IMPORTANT]
-> `master主分支`最新动态(2025.8.23): Dockerfile构建效率大幅优化  
-> `master主分支`最新动态(2025.7.31): 新GUI前端，Coming Soon  
->
-> 2025.2.2: 三分钟快速接入最强qwen2.5-max[视频](https://www.bilibili.com/video/BV1LeFuerEG4)  
-> 2025.2.1: 支持自定义字体  
-> 2024.10.10: 突发停电，紧急恢复了提供[whl包](https://drive.google.com/drive/folders/14kR-3V-lIbvGxri4AHc8TpiA1fqsw7SK?usp=sharing)的文件服务器  
+> 2024.6.1: 版本3.80加入插件二级菜单功能（详见wiki）  
 > 2024.5.1: 加入Doc2x翻译PDF论文的功能，[查看详情](https://github.com/binary-husky/gpt_academic/wiki/Doc2x)  
 > 2024.3.11: 全力支持Qwen、GLM、DeepseekCoder等中文大语言模型！ SoVits语音克隆模块，[查看详情](https://www.bilibili.com/video/BV1Rp421S7tF/) 
-> 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。
+> 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。

 <br>

@@ -63,6 +58,7 @@ Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanes
 ⭐支持mermaid图像渲染 | 支持让GPT生成[流程图](https://www.bilibili.com/video/BV18c41147H9/)、状态转移图、甘特图、饼状图、GitGraph等等（3.7版本）
 ⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
 ⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
+⭐AutoGen多智能体插件 | [插件] 借助微软AutoGen，探索多Agent的智能涌现可能！
 ⭐虚空终端插件 | [插件] 能够使用自然语言直接调度本项目其他插件
 润色、翻译、代码解释 | 一键润色、翻译、查找论文语法错误、解释代码
 [自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
@@ -127,20 +123,20 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼

 ```mermaid
 flowchart TD
-    A{"安装方法"} --> W1("I 🔑直接运行 (Windows, Linux or MacOS)")
-    W1 --> W11["1 Python pip包管理依赖"]
-    W1 --> W12["2 Anaconda包管理依赖（推荐⭐）"]
+    A{"安装方法"} --> W1("I. 🔑直接运行 (Windows, Linux or MacOS)")
+    W1 --> W11["1. Python pip包管理依赖"]
+    W1 --> W12["2. Anaconda包管理依赖（推荐⭐）"]

-    A --> W2["II 🐳使用Docker (Windows, Linux or MacOS)"]
+    A --> W2["II. 🐳使用Docker (Windows, Linux or MacOS)"]

-    W2 --> k1["1 部署项目全部能力的大镜像（推荐⭐）"]
-    W2 --> k2["2 仅在线模型（GPT, GLM4等）镜像"]
-    W2 --> k3["3 在线模型 + Latex的大镜像"]
+    W2 --> k1["1. 部署项目全部能力的大镜像（推荐⭐）"]
+    W2 --> k2["2. 仅在线模型（GPT, GLM4等）镜像"]
+    W2 --> k3["3. 在线模型 + Latex的大镜像"]

-    A --> W4["IV 🚀其他部署方法"]
-    W4 --> C1["1 Windows/MacOS 一键安装运行脚本（推荐⭐）"]
-    W4 --> C2["2 Huggingface, Sealos远程部署"]
-    W4 --> C4["3 其他 ..."]
+    A --> W4["IV. 🚀其他部署方法"]
+    W4 --> C1["1. Windows/MacOS 一键安装运行脚本（推荐⭐）"]
+    W4 --> C2["2. Huggingface, Sealos远程部署"]
+    W4 --> C4["3. ... 其他 ..."]
 ```

 ### 安装方法I：直接运行 (Windows, Linux or MacOS)
@@ -173,32 +169,26 @@ flowchart TD
    ```


-<details><summary>如果需要支持清华ChatGLM系列/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
+<details><summary>如果需要支持清华ChatGLM2/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
 <p>

-【可选步骤】如果需要支持清华ChatGLM系列/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
+【可选步骤】如果需要支持清华ChatGLM3/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：

 ```sh
 # 【可选步骤I】支持清华ChatGLM3。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
 python -m pip install -r request_llms/requirements_chatglm.txt

-# 【可选步骤II】支持清华ChatGLM4 注意：此模型至少需要24G显存
-python -m pip install -r request_llms/requirements_chatglm4.txt
-# 可使用modelscope下载ChatGLM4模型
-# pip install modelscope
-# modelscope download --model ZhipuAI/glm-4-9b-chat --local_dir ./THUDM/glm-4-9b-chat
-
-# 【可选步骤III】支持复旦MOSS
+# 【可选步骤II】支持复旦MOSS
 python -m pip install -r request_llms/requirements_moss.txt
 git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss  # 注意执行此行代码时，必须处于项目根路径

-# 【可选步骤IV】支持RWKV Runner
+# 【可选步骤III】支持RWKV Runner
 参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner

-# 【可选步骤V】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
+# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

-# 【可选步骤VI】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
+# 【可选步骤V】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
 pip install bitsandbyte
 # windows用户安装bitsandbytes需要使用下面bitsandbytes-windows-webui
 python -m pip install bitsandbytes --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
@@ -426,6 +416,7 @@ timeline LR
 1. `master` 分支: 主分支，稳定版
 2. `frontier` 分支: 开发分支，测试版
 3. 如何[接入其他大模型](request_llms/README.md)
+4. 访问GPT-Academic的[在线服务并支持我们](https://github.com/binary-husky/gpt_academic/wiki/online)

 ### V：参考与学习

--- a/check_proxy.py
+++ b/check_proxy.py
@@ -1,36 +1,24 @@
 from loguru import logger

 def check_proxy(proxies, return_ip=False):
-    """
-    检查代理配置并返回结果。
-
-    Args:
-        proxies (dict): 包含http和https代理配置的字典。
-        return_ip (bool, optional): 是否返回代理的IP地址。默认为False。
-
-    Returns:
-        str or None: 检查的结果信息或代理的IP地址（如果`return_ip`为True）。
-    """
    import requests
    proxies_https = proxies['https'] if proxies is not None else '无'
    ip = None
    try:
-        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)  # ⭐ 执行GET请求以获取代理信息
+        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
        data = response.json()
        if 'country_name' in data:
            country = data['country_name']
            result = f"代理配置 {proxies_https}, 代理所在地：{country}"
-            if 'ip' in data:
-                ip = data['ip']
+            if 'ip' in data: ip = data['ip']
        elif 'error' in data:
-            alternative, ip = _check_with_backup_source(proxies)  # ⭐ 调用备用方法检查代理配置
+            alternative, ip = _check_with_backup_source(proxies)
            if alternative is None:
                result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
            else:
                result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
        else:
            result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
-
        if not return_ip:
            logger.warning(result)
            return result
@@ -45,33 +33,17 @@ def check_proxy(proxies, return_ip=False):
            return ip

 def _check_with_backup_source(proxies):
-    """
-    通过备份源检查代理，并获取相应信息。
-
-    Args:
-        proxies (dict): 包含代理信息的字典。
-
-    Returns:
-        tuple: 代理信息(geo)和IP地址(ip)的元组。
-    """
    import random, string, requests
    random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
    try:
-        res_json = requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()  # ⭐ 执行代理检查和备份源请求
+        res_json = requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()
        return res_json['dns']['geo'], res_json['dns']['ip']
    except:
        return None, None

 def backup_and_download(current_version, remote_version):
    """
-    一键更新协议：备份当前版本，下载远程版本并解压缩。
-
-    Args:
-        current_version (str): 当前版本号。
-        remote_version (str): 远程版本号。
-
-    Returns:
-        str: 新版本目录的路径。
+    一键更新协议：备份和下载
    """
    from toolbox import get_conf
    import shutil
@@ -88,7 +60,7 @@ def backup_and_download(current_version, remote_version):
    proxies = get_conf('proxies')
    try:    r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
    except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
-    zip_file_path = backup_dir+'/master.zip'  # ⭐ 保存备份文件的路径
+    zip_file_path = backup_dir+'/master.zip'
    with open(zip_file_path, 'wb+') as f:
        f.write(r.content)
    dst_path = new_version_dir
@@ -104,17 +76,6 @@ def backup_and_download(current_version, remote_version):
 def patch_and_restart(path):
    """
    一键更新协议：覆盖和重启
-
-    Args:
-        path (str): 新版本代码所在的路径
-
-    注意事项:
-        如果您的程序没有使用config_private.py私密配置文件，则会将config.py重命名为config_private.py以避免配置丢失。
-
-    更新流程:
-        - 复制最新版本代码到当前目录
-        - 更新pip包依赖
-        - 如果更新失败，则提示手动安装依赖库并重启
    """
    from distutils import dir_util
    import shutil
@@ -123,43 +84,32 @@ def patch_and_restart(path):
    import time
    import glob
    from shared_utils.colorful import log亮黄, log亮绿, log亮红
-
+    # if not using config_private, move origin config.py as config_private.py
    if not os.path.exists('config_private.py'):
        log亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
              '另外您可以随时在history子文件夹下找回旧版的程序。')
        shutil.copyfile('config.py', 'config_private.py')
-
    path_new_version = glob.glob(path + '/*-master')[0]
-    dir_util.copy_tree(path_new_version, './')  # ⭐ 将最新版本代码复制到当前目录
-
+    dir_util.copy_tree(path_new_version, './')
    log亮绿('代码已经更新，即将更新pip包依赖……')
    for i in reversed(range(5)): time.sleep(1); log亮绿(i)
-
    try:
        import subprocess
        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
    except:
        log亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-
    log亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
    log亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
    log亮绿(' ------------------------------ -----------------------------------')
-
    for i in reversed(range(8)): time.sleep(1); log亮绿(i)
-    os.execl(sys.executable, sys.executable, *sys.argv)  # 重启程序
+    os.execl(sys.executable, sys.executable, *sys.argv)


 def get_current_version():
-    """
-    获取当前的版本号。
-
-    Returns:
-        str: 当前的版本号。如果无法获取版本号，则返回空字符串。
-    """
    import json
    try:
        with open('./version', 'r', encoding='utf8') as f:
-            current_version = json.loads(f.read())['version']  # ⭐ 从读取的json数据中提取版本号
+            current_version = json.loads(f.read())['version']
    except:
        current_version = ""
    return current_version
@@ -168,12 +118,6 @@ def get_current_version():
 def auto_update(raise_error=False):
    """
    一键更新协议：查询版本和用户意见
-
-    Args:
-        raise_error (bool, optional): 是否在出错时抛出错误。默认为 False。
-
-    Returns:
-        None
    """
    try:
        from toolbox import get_conf
@@ -193,13 +137,13 @@ def auto_update(raise_error=False):
            current_version = json.loads(current_version)['version']
        if (remote_version - current_version) >= 0.01-1e-5:
            from shared_utils.colorful import log亮黄
-            log亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')  # ⭐ 在控制台打印新版本信息
+            log亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
            logger.info('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
            user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
            if user_instruction in ['Y', 'y']:
-                path = backup_and_download(current_version, remote_version)  # ⭐ 备份并下载文件
+                path = backup_and_download(current_version, remote_version)
                try:
-                    patch_and_restart(path)  # ⭐ 执行覆盖并重启操作
+                    patch_and_restart(path)
                except:
                    msg = '更新失败。'
                    if raise_error:
@@ -219,9 +163,6 @@ def auto_update(raise_error=False):
        logger.info(msg)

 def warm_up_modules():
-    """
-    预热模块，加载特定模块并执行预热操作。
-    """
    logger.info('正在执行一些模块的预热 ...')
    from toolbox import ProxyNetworkActivate
    from request_llms.bridge_all import model_info
@@ -230,60 +171,8 @@ def warm_up_modules():
        enc.encode("模块预热", disallowed_special=())
        enc = model_info["gpt-4"]['tokenizer']
        enc.encode("模块预热", disallowed_special=())
-        try_warm_up_vectordb()
-
-
-# def try_warm_up_vectordb():
-#     try:
-#         import os
-#         import nltk
-#         target = os.path.expanduser('~/nltk_data')
-#         logger.info(f'模块预热: nltk punkt (从Github下载部分文件到 {target})')
-#         nltk.data.path.append(target)
-#         nltk.download('punkt', download_dir=target)
-#         logger.info('模块预热完成: nltk punkt')
-#     except:
-#         logger.exception('模块预热: nltk punkt 失败，可能需要手动安装 nltk punkt')
-#         logger.error('模块预热: nltk punkt 失败，可能需要手动安装 nltk punkt')
-
-
-def try_warm_up_vectordb():
-    import os
-    import nltk
-    target = os.path.expanduser('~/nltk_data')
-    nltk.data.path.append(target)
-    try:
-        # 尝试加载 punkt
-        logger.info(f'nltk模块预热')
-        nltk.data.find('tokenizers/punkt')
-        nltk.data.find('tokenizers/punkt_tab')
-        nltk.data.find('taggers/averaged_perceptron_tagger_eng')
-        logger.info('nltk模块预热完成（读取本地缓存）')
-    except:
-        # 如果找不到，则尝试下载
-        try:
-            logger.info(f'模块预热: nltk punkt (从 Github 下载部分文件到 {target})')
-            from shared_utils.nltk_downloader import Downloader
-            _downloader = Downloader()
-            _downloader.download('punkt', download_dir=target)
-            _downloader.download('punkt_tab', download_dir=target)
-            _downloader.download('averaged_perceptron_tagger_eng', download_dir=target)
-            logger.info('nltk模块预热完成')
-        except Exception:
-            logger.exception('模块预热: nltk punkt 失败，可能需要手动安装 nltk punkt')
-

 def warm_up_vectordb():
-    """
-    执行一些模块的预热操作。
-
-    本函数主要用于执行一些模块的预热操作，确保在后续的流程中能够顺利运行。
-
-    ⭐ 关键作用：预热模块
-
-    Returns:
-        None
-    """
    logger.info('正在执行一些模块的预热 ...')
    from toolbox import ProxyNetworkActivate
    with ProxyNetworkActivate("Warmup_Modules"):
--- a/config.py
+++ b/config.py
@@ -7,37 +7,36 @@
    Configuration reading priority: environment variable > config_private.py > config.py
 """

-# [step 1-1]>> ( 接入OpenAI模型家族 ) API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
-API_KEY = "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"   # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
+# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
+API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"

-# [step 1-2]>> ( 强烈推荐！接入通义家族 & 大模型服务平台百炼 ) 接入通义千问在线大模型，api-key获取地址 https://dashscope.console.aliyun.com/
-DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY（用于接入qwen-max，dashscope-qwen3-14b，dashscope-deepseek-r1等）

-# [step 1-3]>> ( 接入 deepseek-reasoner, 即 deepseek-r1 ) 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
-DEEPSEEK_API_KEY = "sk-d99b8cc6b7414cc88a5d950a3ff7585e"
-
-# [step 2]>> 改为True应用代理。如果使用本地或无地域限制的大模型时，此处不修改；如果直接在海外服务器部署，此处不修改
+# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改；如果使用本地或无地域限制的大模型时，此处也不需要修改
 USE_PROXY = False
 if USE_PROXY:
+    """
+    代理网络的地址，打开你的代理软件查看代理协议(socks5h / http)、地址(localhost)和端口(11284)
+    填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
+            <配置教程&视频教程> https://github.com/binary-husky/gpt_academic/issues/1>
+    [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
+    [地址] 填localhost或者127.0.0.1（localhost意思是代理软件安装在本机上）
+    [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
+    """
    proxies = {
-        "http":"socks5h://192.168.8.9:1070",  # 再例如  "http":  "http://127.0.0.1:7890",
-        "https":"socks5h://192.168.8.9:1070",  # 再例如  "https": "http://127.0.0.1:7890",
+        #          [协议]://  [地址]  :[端口]
+        "http":  "socks5h://localhost:11284",  # 再例如  "http":  "http://127.0.0.1:7890",
+        "https": "socks5h://localhost:11284",  # 再例如  "https": "http://127.0.0.1:7890",
    }
 else:
    proxies = None

 # [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
-LLM_MODEL = "gpt-4" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["qwen-max", "o1-mini", "o1-mini-2024-09-12", "o1", "o1-2024-12-17", "o1-preview", "o1-preview-2024-09-12",
-                    "gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
+LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
                    "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
                    "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
-                    "gemini-1.5-pro", "chatglm3", "chatglm4",
-                    "deepseek-chat", "deepseek-coder", "deepseek-reasoner",
-                    "volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226",
-                    "dashscope-deepseek-r1", "dashscope-deepseek-v3",
-                    "dashscope-qwen3-14b", "dashscope-qwen3-235b-a22b", "dashscope-qwen3-32b",
+                    "gemini-1.5-pro", "chatglm3"
                    ]

 EMBEDDING_MODEL = "text-embedding-3-small"
@@ -48,7 +47,7 @@ EMBEDDING_MODEL = "text-embedding-3-small"
 #   "glm-4-0520", "glm-4-air", "glm-4-airx", "glm-4-flash",
 #   "qianfan", "deepseekcoder",
 #   "spark", "sparkv2", "sparkv3", "sparkv3.5", "sparkv4",
-#   "qwen-turbo", "qwen-plus", "qwen-local",
+#   "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
 #   "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
 #   "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125", "gpt-4o-2024-05-13"
 #   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
@@ -56,7 +55,6 @@ EMBEDDING_MODEL = "text-embedding-3-small"
 #   "deepseek-chat" ,"deepseek-coder",
 #   "gemini-1.5-flash",
 #   "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
-#   "grok-beta",
 # ]
 # --- --- --- ---
 # 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
@@ -75,7 +73,7 @@ API_URL_REDIRECT = {}

 # 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
 # 一言以蔽之：免费（5刀）用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 8
+DEFAULT_WORKER_NUM = 3


 # 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
@@ -83,31 +81,6 @@ DEFAULT_WORKER_NUM = 8
 THEME = "Default"
 AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]

-FONT = "Theme-Default-Font"
-AVAIL_FONTS = [
-    "默认值(Theme-Default-Font)",
-    "宋体(SimSun)",
-    "黑体(SimHei)",
-    "楷体(KaiTi)",
-    "仿宋(FangSong)",
-    "华文细黑(STHeiti Light)",
-    "华文楷体(STKaiti)",
-    "华文仿宋(STFangsong)",
-    "华文宋体(STSong)",
-    "华文中宋(STZhongsong)",
-    "华文新魏(STXinwei)",
-    "华文隶书(STLiti)",
-    # 备注：以下字体需要网络支持，您可以自定义任意您喜欢的字体，如下所示，需要满足的格式为 "字体昵称(字体英文真名@字体css下载链接)"
-    "思源宋体(Source Han Serif CN VF@https://chinese-fonts-cdn.deno.dev/packages/syst/dist/SourceHanSerifCN/result.css)",
-    "月星楷(Moon Stars Kai HW@https://chinese-fonts-cdn.deno.dev/packages/moon-stars-kai/dist/MoonStarsKaiHW-Regular/result.css)",
-    "珠圆体(MaokenZhuyuanTi@https://chinese-fonts-cdn.deno.dev/packages/mkzyt/dist/猫啃珠圆体/result.css)",
-    "平方萌萌哒(PING FANG MENG MNEG DA@https://chinese-fonts-cdn.deno.dev/packages/pfmmd/dist/平方萌萌哒/result.css)",
-    "Helvetica",
-    "ui-sans-serif",
-    "sans-serif",
-    "system-ui"
-]
-

 # 默认的系统提示词（system prompt）
 INIT_SYS_PROMPT = "Serve me as a writing and programming assistant."
@@ -134,14 +107,15 @@ TIMEOUT_SECONDS = 30


 # 网页的端口, -1代表随机端口
-WEB_PORT = 19998
+WEB_PORT = -1
+

 # 是否自动打开浏览器页面
 AUTO_OPEN_BROWSER = True


 # 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 3
+MAX_RETRY = 2


 # 插件分类默认选项
@@ -158,15 +132,16 @@ MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
 QWEN_LOCAL_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"


+# 接入通义千问在线大模型 https://dashscope.console.aliyun.com/
+DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
+
+
 # 百度千帆（LLM_MODEL="qianfan"）
 BAIDU_CLOUD_API_KEY = ''
 BAIDU_CLOUD_SECRET_KEY = ''
 BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"


-# 如果使用ChatGLM3或ChatGLM4本地模型，请把 LLM_MODEL="chatglm3" 或LLM_MODEL="chatglm4"，并在此处指定模型路径
-CHATGLM_LOCAL_MODEL_PATH = "THUDM/glm-4-9b-chat" # 例如"/home/hmp/ChatGLM3-6B/"
-
 # 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
 CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"

@@ -185,7 +160,7 @@ AUTO_CLEAR_TXT = False


 # 加一个live2d装饰
-ADD_WAIFU = True
+ADD_WAIFU = False


 # 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
@@ -260,15 +235,13 @@ MOONSHOT_API_KEY = ""
 YIMODEL_API_KEY = ""


-# 接入火山引擎的在线大模型)，api-key获取地址 https://console.volcengine.com/ark/region:ark+cn-beijing/endpoint
-ARK_API_KEY = "00000000-0000-0000-0000-000000000000" # 火山引擎 API KEY
+# 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
+DEEPSEEK_API_KEY = ""


 # 紫东太初大模型 https://ai-maas.wair.ac.cn
 TAICHU_API_KEY = ""

-# Grok API KEY
-GROK_API_KEY = ""

 # Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
 MATHPIX_APPID = ""
@@ -300,8 +273,8 @@ GROBID_URLS = [
 ]


-# Searxng互联网检索服务（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
-SEARXNG_URLS = [ f"https://kaletianlre-beardvs{i}dd.hf.space/" for i in range(1,5) ]
+# Searxng互联网检索服务
+SEARXNG_URL = "https://cloud-1.agent-matrix.com/"


 # 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
@@ -325,7 +298,7 @@ ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"


 # 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
-WHEN_TO_USE_PROXY = ["Connect_OpenAI", "Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
+WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
                     "Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]


@@ -337,27 +310,6 @@ PLUGIN_HOT_RELOAD = False
 NUM_CUSTOM_BASIC_BTN = 4


-# 媒体智能体的服务地址（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
-DAAS_SERVER_URLS = [ f"https://niuziniu-biligpt{i}.hf.space/stream" for i in range(1,5) ]
-
-
-# 在互联网搜索组件中，负责将搜索结果整理成干净的Markdown
-JINA_API_KEY = ""
-
-
-# SEMANTIC SCHOLAR API KEY
-SEMANTIC_SCHOLAR_KEY = ""
-
-
-# 是否自动裁剪上下文长度（是否启动，默认不启动）
-AUTO_CONTEXT_CLIP_ENABLE = False
-# 目标裁剪上下文的token长度（如果超过这个长度，则会自动裁剪）
-AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN = 30*1000
-# 无条件丢弃x以上的轮数
-AUTO_CONTEXT_MAX_ROUND = 64
-# 在裁剪上下文时，倒数第x次对话能“最多”保留的上下文token的比例占 AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN 的多少
-AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01]
-

 """
 --------------- 配置关联关系说明 ---------------
@@ -417,7 +369,6 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0

 本地大模型示意图
 │
-├── "chatglm4"
 ├── "chatglm3"
 ├── "chatglm"
 ├── "chatglm_onnx"
@@ -448,7 +399,7 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0
 插件在线服务配置依赖关系示意图
 │
 ├── 互联网检索
-│   └── SEARXNG_URLS
+│   └── SEARXNG_URL
 │
 ├── 语音功能
 │   ├── ENABLE_AUDIO
--- a/config_private.py
+++ b/config_private.py
@@ -1,466 +0,0 @@
-"""
-    以下所有配置也都支持利用环境变量覆写，环境变量配置格式见docker-compose.yml。
-    读取优先级：环境变量 > config_private.py > config.py
-    --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
-    All the following configurations also support using environment variables to override,
-    and the environment variable configuration format can be seen in docker-compose.yml.
-    Configuration reading priority: environment variable > config_private.py > config.py
-"""
-
-# [step 1-1]>> ( 接入OpenAI模型家族 ) API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
-API_KEY = "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"   # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
-
-# [step 1-2]>> ( 强烈推荐！接入通义家族 & 大模型服务平台百炼 ) 接入通义千问在线大模型，api-key获取地址 https://dashscope.console.aliyun.com/
-DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY（用于接入qwen-max，dashscope-qwen3-14b，dashscope-deepseek-r1等）
-
-# [step 1-3]>> ( 接入 deepseek-reasoner, 即 deepseek-r1 ) 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
-DEEPSEEK_API_KEY = "sk-d99b8cc6b7414cc88a5d950a3ff7585e"
-
-# [step 2]>> 改为True应用代理。如果使用本地或无地域限制的大模型时，此处不修改；如果直接在海外服务器部署，此处不修改
-USE_PROXY = False
-if USE_PROXY:
-    proxies = {
-        "http":"socks5h://192.168.8.9:1070",  # 再例如  "http":  "http://127.0.0.1:7890",
-        "https":"socks5h://192.168.8.9:1070",  # 再例如  "https": "http://127.0.0.1:7890",
-    }
-else:
-    proxies = None
-
-# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
-LLM_MODEL = "gpt-4" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["qwen-max", "o1-mini", "o1-mini-2024-09-12", "o1", "o1-2024-12-17", "o1-preview", "o1-preview-2024-09-12",
-                    "gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
-                    "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
-                    "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
-                    "gemini-1.5-pro", "chatglm3", "chatglm4",
-                    "deepseek-chat", "deepseek-coder", "deepseek-reasoner",
-                    "volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226",
-                    "dashscope-deepseek-r1", "dashscope-deepseek-v3",
-                    "dashscope-qwen3-14b", "dashscope-qwen3-235b-a22b", "dashscope-qwen3-32b",
-                    ]
-
-EMBEDDING_MODEL = "text-embedding-3-small"
-
-# --- --- --- ---
-# P.S. 其他可用的模型还包括
-# AVAIL_LLM_MODELS = [
-#   "glm-4-0520", "glm-4-air", "glm-4-airx", "glm-4-flash",
-#   "qianfan", "deepseekcoder",
-#   "spark", "sparkv2", "sparkv3", "sparkv3.5", "sparkv4",
-#   "qwen-turbo", "qwen-plus", "qwen-local",
-#   "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
-#   "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125", "gpt-4o-2024-05-13"
-#   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
-#   "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
-#   "deepseek-chat" ,"deepseek-coder",
-#   "gemini-1.5-flash",
-#   "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
-#   "grok-beta",
-# ]
-# --- --- --- ---
-# 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
-# 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如
-# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"]
-# --- --- --- ---
-
-
-# --------------- 以下配置可以优化体验 ---------------
-
-# 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
-API_URL_REDIRECT = {}
-
-
-# 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
-# 一言以蔽之：免费（5刀）用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 8
-
-
-# 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
-# 更多主题, 请查阅Gradio主题商店: https://huggingface.co/spaces/gradio/theme-gallery 可选 ["Gstaff/Xkcd", "NoCrypt/Miku", ...]
-THEME = "Default"
-AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
-
-FONT = "Theme-Default-Font"
-AVAIL_FONTS = [
-    "默认值(Theme-Default-Font)",
-    "宋体(SimSun)",
-    "黑体(SimHei)",
-    "楷体(KaiTi)",
-    "仿宋(FangSong)",
-    "华文细黑(STHeiti Light)",
-    "华文楷体(STKaiti)",
-    "华文仿宋(STFangsong)",
-    "华文宋体(STSong)",
-    "华文中宋(STZhongsong)",
-    "华文新魏(STXinwei)",
-    "华文隶书(STLiti)",
-    # 备注：以下字体需要网络支持，您可以自定义任意您喜欢的字体，如下所示，需要满足的格式为 "字体昵称(字体英文真名@字体css下载链接)"
-    "思源宋体(Source Han Serif CN VF@https://chinese-fonts-cdn.deno.dev/packages/syst/dist/SourceHanSerifCN/result.css)",
-    "月星楷(Moon Stars Kai HW@https://chinese-fonts-cdn.deno.dev/packages/moon-stars-kai/dist/MoonStarsKaiHW-Regular/result.css)",
-    "珠圆体(MaokenZhuyuanTi@https://chinese-fonts-cdn.deno.dev/packages/mkzyt/dist/猫啃珠圆体/result.css)",
-    "平方萌萌哒(PING FANG MENG MNEG DA@https://chinese-fonts-cdn.deno.dev/packages/pfmmd/dist/平方萌萌哒/result.css)",
-    "Helvetica",
-    "ui-sans-serif",
-    "sans-serif",
-    "system-ui"
-]
-
-
-# 默认的系统提示词（system prompt）
-INIT_SYS_PROMPT = "Serve me as a writing and programming assistant."
-
-
-# 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效）
-CHATBOT_HEIGHT = 1115
-
-
-# 代码高亮
-CODE_HIGHLIGHT = True
-
-
-# 窗口布局
-LAYOUT = "LEFT-RIGHT"   # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-
-
-# 暗色模式 / 亮色模式
-DARK_MODE = True
-
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 30
-
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = 19998
-
-# 是否自动打开浏览器页面
-AUTO_OPEN_BROWSER = True
-
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 3
-
-
-# 插件分类默认选项
-DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
-
-
-# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
-MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
-
-
-# 选择本地模型变体（只有当AVAIL_LLM_MODELS包含了对应本地模型时，才会起作用）
-# 如果你选择Qwen系列的模型，那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型
-# 也可以是具体的模型路径
-QWEN_LOCAL_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
-
-
-# 百度千帆（LLM_MODEL="qianfan"）
-BAIDU_CLOUD_API_KEY = ''
-BAIDU_CLOUD_SECRET_KEY = ''
-BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"
-
-
-# 如果使用ChatGLM3或ChatGLM4本地模型，请把 LLM_MODEL="chatglm3" 或LLM_MODEL="chatglm4"，并在此处指定模型路径
-CHATGLM_LOCAL_MODEL_PATH = "THUDM/glm-4-9b-chat" # 例如"/home/hmp/ChatGLM3-6B/"
-
-# 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
-CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"
-
-
-# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
-LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
-LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
-
-
-# 设置gradio的并行线程数（不需要修改）
-CONCURRENT_COUNT = 100
-
-
-# 是否在提交时自动清空输入框
-AUTO_CLEAR_TXT = False
-
-
-# 加一个live2d装饰
-ADD_WAIFU = True
-
-
-# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-# [("username", "password"), ("username2", "password2"), ...]
-AUTHENTICATION = []
-
-
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）
-# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
-CUSTOM_PATH = "/"
-
-
-# HTTPS 秘钥和证书（不需要修改）
-SSL_KEYFILE = ""
-SSL_CERTFILE = ""
-
-
-# 极少数情况下，openai的官方KEY需要伴随组织编码（格式如org-xxxxxxxxxxxxxxxxxxxxxxxx）使用
-API_ORG = ""
-
-
-# 如果需要使用Slack Claude，使用教程详情见 request_llms/README.md
-SLACK_CLAUDE_BOT_ID = ''
-SLACK_CLAUDE_USER_TOKEN = ''
-
-
-# 如果需要使用AZURE（方法一：单个azure模型部署）详情请见额外文档 docs\use_azure.md
-AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
-AZURE_API_KEY = "填入azure openai api的密钥"    # 建议直接在API_KEY处填写，该选项即将被弃用
-AZURE_ENGINE = "填入你亲手写的部署名"            # 读 docs\use_azure.md
-
-
-# 如果需要使用AZURE（方法二：多个azure模型部署+动态切换）详情请见额外文档 docs\use_azure.md
-AZURE_CFG_ARRAY = {}
-
-
-# 阿里云实时语音识别 配置难度较高
-# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
-ENABLE_AUDIO = False
-ALIYUN_TOKEN=""     # 例如 f37f30e0f9934c34a992f6f64f7eba4f
-ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
-ALIYUN_ACCESSKEY="" # （无需填写）
-ALIYUN_SECRET=""    # （无需填写）
-
-
-# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
-TTS_TYPE = "EDGE_TTS" # EDGE_TTS / LOCAL_SOVITS_API / DISABLE
-GPT_SOVITS_URL = ""
-EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
-
-
-# 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
-XFYUN_APPID = "00000000"
-XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
-XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
-
-
-# 接入智谱大模型
-ZHIPUAI_API_KEY = ""
-ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
-
-
-# Claude API KEY
-ANTHROPIC_API_KEY = ""
-
-
-# 月之暗面 API KEY
-MOONSHOT_API_KEY = ""
-
-
-# 零一万物(Yi Model) API KEY
-YIMODEL_API_KEY = ""
-
-
-# 接入火山引擎的在线大模型)，api-key获取地址 https://console.volcengine.com/ark/region:ark+cn-beijing/endpoint
-ARK_API_KEY = "00000000-0000-0000-0000-000000000000" # 火山引擎 API KEY
-
-
-# 紫东太初大模型 https://ai-maas.wair.ac.cn
-TAICHU_API_KEY = ""
-
-# Grok API KEY
-GROK_API_KEY = ""
-
-# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
-MATHPIX_APPID = ""
-MATHPIX_APPKEY = ""
-
-
-# DOC2X的PDF解析服务，注册账号并获取API KEY: https://doc2x.noedgeai.com/login
-DOC2X_API_KEY = ""
-
-
-# 自定义API KEY格式
-CUSTOM_API_KEY_PATTERN = ""
-
-
-# Google Gemini API-Key
-GEMINI_API_KEY = ''
-
-
-# HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
-HUGGINGFACE_ACCESS_TOKEN = "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"
-
-
-# GROBID服务器地址（填写多个可以均衡负载），用于高质量地读取PDF文档
-# 获取方法：复制以下空间https://huggingface.co/spaces/qingxu98/grobid，设为public，然后GROBID_URL = "https://(你的hf用户名如qingxu98)-(你的填写的空间名如grobid).hf.space"
-GROBID_URLS = [
-    "https://qingxu98-grobid.hf.space","https://qingxu98-grobid2.hf.space","https://qingxu98-grobid3.hf.space",
-    "https://qingxu98-grobid4.hf.space","https://qingxu98-grobid5.hf.space", "https://qingxu98-grobid6.hf.space",
-    "https://qingxu98-grobid7.hf.space", "https://qingxu98-grobid8.hf.space",
-]
-
-
-# Searxng互联网检索服务（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
-SEARXNG_URLS = [ f"https://kaletianlre-beardvs{i}dd.hf.space/" for i in range(1,5) ]
-
-
-# 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
-ALLOW_RESET_CONFIG = False
-
-
-# 在使用AutoGen插件时，是否使用Docker容器运行代码
-AUTOGEN_USE_DOCKER = False
-
-
-# 临时的上传文件夹位置，请尽量不要修改
-PATH_PRIVATE_UPLOAD = "private_upload"
-
-
-# 日志文件夹的位置，请尽量不要修改
-PATH_LOGGING = "gpt_log"
-
-
-# 存储翻译好的arxiv论文的路径，请尽量不要修改
-ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"
-
-
-# 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
-WHEN_TO_USE_PROXY = ["Connect_OpenAI", "Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
-                     "Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]
-
-
-# 启用插件热加载
-PLUGIN_HOT_RELOAD = False
-
-
-# 自定义按钮的最大数量限制
-NUM_CUSTOM_BASIC_BTN = 4
-
-
-# 媒体智能体的服务地址（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
-DAAS_SERVER_URLS = [ f"https://niuziniu-biligpt{i}.hf.space/stream" for i in range(1,5) ]
-
-
-# 在互联网搜索组件中，负责将搜索结果整理成干净的Markdown
-JINA_API_KEY = ""
-
-
-# SEMANTIC SCHOLAR API KEY
-SEMANTIC_SCHOLAR_KEY = ""
-
-
-# 是否自动裁剪上下文长度（是否启动，默认不启动）
-AUTO_CONTEXT_CLIP_ENABLE = False
-# 目标裁剪上下文的token长度（如果超过这个长度，则会自动裁剪）
-AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN = 30*1000
-# 无条件丢弃x以上的轮数
-AUTO_CONTEXT_MAX_ROUND = 64
-# 在裁剪上下文时，倒数第x次对话能“最多”保留的上下文token的比例占 AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN 的多少
-AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01]
-
-
-"""
--------------- 配置关联关系说明 ---------------
-
-在线大模型配置关联关系示意图
-│
-├── "gpt-3.5-turbo" 等openai模型
-│   ├── API_KEY
-│   ├── CUSTOM_API_KEY_PATTERN（不常用）
-│   ├── API_ORG（不常用）
-│   └── API_URL_REDIRECT（不常用）
-│
-├── "azure-gpt-3.5" 等azure模型（单个azure模型，不需要动态切换）
-│   ├── API_KEY
-│   ├── AZURE_ENDPOINT
-│   ├── AZURE_API_KEY
-│   ├── AZURE_ENGINE
-│   └── API_URL_REDIRECT
-│
-├── "azure-gpt-3.5" 等azure模型（多个azure模型，需要动态切换，高优先级）
-│   └── AZURE_CFG_ARRAY
-│
-├── "spark" 星火认知大模型 spark & sparkv2
-│   ├── XFYUN_APPID
-│   ├── XFYUN_API_SECRET
-│   └── XFYUN_API_KEY
-│
-├── "claude-3-opus-20240229" 等claude模型
-│   └── ANTHROPIC_API_KEY
-│
-├── "stack-claude"
-│   ├── SLACK_CLAUDE_BOT_ID
-│   └── SLACK_CLAUDE_USER_TOKEN
-│
-├── "qianfan" 百度千帆大模型库
-│   ├── BAIDU_CLOUD_QIANFAN_MODEL
-│   ├── BAIDU_CLOUD_API_KEY
-│   └── BAIDU_CLOUD_SECRET_KEY
-│
-├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
-│   └── ZHIPUAI_API_KEY
-│
-├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
-│   └── YIMODEL_API_KEY
-│
-├── "qwen-turbo" 等通义千问大模型
-│   └──  DASHSCOPE_API_KEY
-│
-├── "Gemini"
-│   └──  GEMINI_API_KEY
-│
-└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
-    ├── AVAIL_LLM_MODELS
-    ├── API_KEY
-    └── API_URL_REDIRECT
-
-
-本地大模型示意图
-│
-├── "chatglm4"
-├── "chatglm3"
-├── "chatglm"
-├── "chatglm_onnx"
-├── "chatglmft"
-├── "internlm"
-├── "moss"
-├── "jittorllms_pangualpha"
-├── "jittorllms_llama"
-├── "deepseekcoder"
-├── "qwen-local"
-├──  RWKV的支持见Wiki
-└── "llama2"
-
-
-用户图形界面布局依赖关系示意图
-│
-├── CHATBOT_HEIGHT 对话窗的高度
-├── CODE_HIGHLIGHT 代码高亮
-├── LAYOUT 窗口布局
-├── DARK_MODE 暗色模式 / 亮色模式
-├── DEFAULT_FN_GROUPS 插件分类默认选项
-├── THEME 色彩主题
-├── AUTO_CLEAR_TXT 是否在提交时自动清空输入框
-├── ADD_WAIFU 加一个live2d装饰
-└── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性
-
-
-插件在线服务配置依赖关系示意图
-│
-├── 互联网检索
-│   └── SEARXNG_URLS
-│
-├── 语音功能
-│   ├── ENABLE_AUDIO
-│   ├── ALIYUN_TOKEN
-│   ├── ALIYUN_APPKEY
-│   ├── ALIYUN_ACCESSKEY
-│   └── ALIYUN_SECRET
-│
-└── PDF文档精准解析
-    ├── GROBID_URLS
-    ├── MATHPIX_APPID
-    └── MATHPIX_APPKEY
-
-
-"""
--- a/crazy_functional.py
+++ b/crazy_functional.py
@@ -2,9 +2,11 @@ from toolbox import HotReload  # HotReload 的意思是热更新，修改函数
 from toolbox import trimmed_format_exc
 from loguru import logger

+
 def get_crazy_functions():
-    from crazy_functions.Paper_Abstract_Writer import Paper_Abstract_Writer
-    from crazy_functions.Program_Comment_Gen import 批量Program_Comment_Gen
+    from crazy_functions.读文章写摘要 import 读文章写摘要
+    from crazy_functions.生成函数注释 import 批量生成函数注释
+    from crazy_functions.Rag_Interface import Rag问答
    from crazy_functions.SourceCode_Analyse import 解析项目本身
    from crazy_functions.SourceCode_Analyse import 解析一个Python项目
    from crazy_functions.SourceCode_Analyse import 解析一个Matlab项目
@@ -16,27 +18,27 @@ def get_crazy_functions():
    from crazy_functions.SourceCode_Analyse import 解析一个前端项目
    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
    from crazy_functions.高级功能函数模板 import Demo_Wrap
-    from crazy_functions.Latex_Project_Polish import Latex英文润色
-    from crazy_functions.Multi_LLM_Query import 同时问询
+    from crazy_functions.Latex全文润色 import Latex英文润色
+    from crazy_functions.询问多个大语言模型 import 同时问询
    from crazy_functions.SourceCode_Analyse import 解析一个Lua项目
    from crazy_functions.SourceCode_Analyse import 解析一个CSharp项目
-    from crazy_functions.Word_Summary import Word_Summary
-    from crazy_functions.SourceCode_Analyse_JupyterNotebook import 解析ipynb文件
+    from crazy_functions.总结word文档 import 总结word文档
+    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
    from crazy_functions.Conversation_To_File import 载入对话历史存档
    from crazy_functions.Conversation_To_File import 对话历史存档
    from crazy_functions.Conversation_To_File import Conversation_To_File_Wrap
    from crazy_functions.Conversation_To_File import 删除所有本地对话历史记录
-    from crazy_functions.Helpers import 清除缓存
+    from crazy_functions.辅助功能 import 清除缓存
    from crazy_functions.Markdown_Translate import Markdown英译中
-    from crazy_functions.PDF_Summary import PDF_Summary
+    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
    from crazy_functions.PDF_Translate import 批量翻译PDF文档
-    from crazy_functions.Google_Scholar_Assistant_Legacy import Google_Scholar_Assistant_Legacy
-    from crazy_functions.PDF_QA import PDF_QA标准文件输入
-    from crazy_functions.Latex_Project_Polish import Latex中文润色
-    from crazy_functions.Latex_Project_Polish import Latex英文纠错
+    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
+    from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
+    from crazy_functions.Latex全文润色 import Latex中文润色
+    from crazy_functions.Latex全文润色 import Latex英文纠错
    from crazy_functions.Markdown_Translate import Markdown中译英
-    from crazy_functions.Void_Terminal import Void_Terminal
-    from crazy_functions.Mermaid_Figure_Gen import Mermaid_Gen
+    from crazy_functions.虚空终端 import 虚空终端
+    from crazy_functions.生成多种Mermaid图表 import Mermaid_Gen
    from crazy_functions.PDF_Translate_Wrap import PDF_Tran
    from crazy_functions.Latex_Function import Latex英文纠错加PDF对比
    from crazy_functions.Latex_Function import Latex翻译中文并重新编译PDF
@@ -48,26 +50,21 @@ def get_crazy_functions():
    from crazy_functions.Image_Generate import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
    from crazy_functions.Image_Generate_Wrap import ImageGen_Wrap
    from crazy_functions.SourceCode_Comment import 注释Python项目
-    from crazy_functions.SourceCode_Comment_Wrap import SourceCodeComment_Wrap
-    from crazy_functions.VideoResource_GPT import 多媒体任务
-    from crazy_functions.Document_Conversation import 批量文件询问
-    from crazy_functions.Document_Conversation_Wrap import Document_Conversation_Wrap
-

    function_plugins = {
-        "多媒体智能体": {
-            "Group": "智能体",
+        "Rag智能召回": {
+            "Group": "对话",
            "Color": "stop",
            "AsButton": False,
-            "Info": "【仅测试】多媒体任务",
-            "Function": HotReload(多媒体任务),
+            "Info": "将问答数据记录到向量库中，作为长期参考。",
+            "Function": HotReload(Rag问答),
        },
        "虚空终端": {
            "Group": "对话|编程|学术|智能体",
            "Color": "stop",
            "AsButton": True,
            "Info": "使用自然语言实现您的想法",
-            "Function": HotReload(Void_Terminal),
+            "Function": HotReload(虚空终端),
        },
        "解析整个Python项目": {
            "Group": "编程",
@@ -82,7 +79,6 @@ def get_crazy_functions():
            "AsButton": False,
            "Info": "上传一系列python源文件(或者压缩包), 为这些代码添加docstring | 输入参数为路径",
            "Function": HotReload(注释Python项目),
-            "Class": SourceCodeComment_Wrap,
        },
        "载入对话历史存档（先上传存档或输入路径）": {
            "Group": "对话",
@@ -116,7 +112,7 @@ def get_crazy_functions():
            "Group": "学术",
            "Color": "stop",
            "AsButton": True,
-            "Info": "ArXiv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+            "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
            "Function": HotReload(Latex翻译中文并重新编译PDF),  # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
            "Class": Arxiv_Localize,    # 新一代插件需要注册Class
        },
@@ -125,7 +121,7 @@ def get_crazy_functions():
            "Color": "stop",
            "AsButton": False,
            "Info": "批量总结word文档 | 输入参数为路径",
-            "Function": HotReload(Word_Summary),
+            "Function": HotReload(总结word文档),
        },
        "解析整个Matlab项目": {
            "Group": "编程",
@@ -204,7 +200,7 @@ def get_crazy_functions():
            "Color": "stop",
            "AsButton": False,
            "Info": "读取Tex论文并写摘要 | 输入参数为路径",
-            "Function": HotReload(Paper_Abstract_Writer),
+            "Function": HotReload(读文章写摘要),
        },
        "翻译README或MD": {
            "Group": "编程",
@@ -225,14 +221,14 @@ def get_crazy_functions():
            "Color": "stop",
            "AsButton": False,  # 加入下拉菜单中
            "Info": "批量生成函数的注释 | 输入参数为路径",
-            "Function": HotReload(批量Program_Comment_Gen),
+            "Function": HotReload(批量生成函数注释),
        },
        "保存当前的对话": {
            "Group": "对话",
            "Color": "stop",
            "AsButton": True,
            "Info": "保存当前的对话 | 不需要输入参数",
-            "Function": HotReload(对话历史存档),    # 当注册Class后，Function旧接口仅会在“Void_Terminal”中起作用
+            "Function": HotReload(对话历史存档),    # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
            "Class": Conversation_To_File_Wrap     # 新一代插件需要注册Class
        },
        "[多线程Demo]解析此项目本身（源码自译解）": {
@@ -258,12 +254,12 @@ def get_crazy_functions():
            "Function": None,
            "Class": Demo_Wrap, # 新一代插件需要注册Class
        },
-        "PDF论文翻译": {
+        "精准翻译PDF论文": {
            "Group": "学术",
            "Color": "stop",
            "AsButton": True,
            "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档), # 当注册Class后，Function旧接口仅会在“Void_Terminal”中起作用
+            "Function": HotReload(批量翻译PDF文档), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
            "Class": PDF_Tran,  # 新一代插件需要注册Class
        },
        "询问多个GPT模型": {
@@ -277,21 +273,21 @@ def get_crazy_functions():
            "Color": "stop",
            "AsButton": False,  # 加入下拉菜单中
            "Info": "批量总结PDF文档的内容 | 输入参数为路径",
-            "Function": HotReload(PDF_Summary),
+            "Function": HotReload(批量总结PDF文档),
        },
        "谷歌学术检索助手（输入谷歌学术搜索页url）": {
            "Group": "学术",
            "Color": "stop",
            "AsButton": False,  # 加入下拉菜单中
            "Info": "使用谷歌学术检索助手搜索指定URL的结果 | 输入参数为谷歌学术搜索页的URL",
-            "Function": HotReload(Google_Scholar_Assistant_Legacy),
+            "Function": HotReload(谷歌检索小助手),
        },
        "理解PDF文档内容 （模仿ChatPDF）": {
            "Group": "学术",
            "Color": "stop",
            "AsButton": False,  # 加入下拉菜单中
            "Info": "理解PDF文档的内容并进行回答 | 输入参数为路径",
-            "Function": HotReload(PDF_QA标准文件输入),
+            "Function": HotReload(理解PDF文档内容标准文件输入),
        },
        "英文Latex项目全文润色（输入路径或上传压缩包）": {
            "Group": "学术",
@@ -355,8 +351,8 @@ def get_crazy_functions():
            "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
                            r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
                            r'If the term "agent" is used in this section, it should be translated to "智能体". ',
-            "Info": "ArXiv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
-            "Function": HotReload(Latex翻译中文并重新编译PDF),  # 当注册Class后，Function旧接口仅会在“Void_Terminal”中起作用
+            "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+            "Function": HotReload(Latex翻译中文并重新编译PDF),  # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
            "Class": Arxiv_Localize,    # 新一代插件需要注册Class
        },
        "📚本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
@@ -379,18 +375,9 @@ def get_crazy_functions():
                            r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
                            r'If the term "agent" is used in this section, it should be translated to "智能体". ',
            "Info": "PDF翻译中文，并重新编译PDF | 输入参数为路径",
-            "Function": HotReload(PDF翻译中文并重新编译PDF),   # 当注册Class后，Function旧接口仅会在“Void_Terminal”中起作用
+            "Function": HotReload(PDF翻译中文并重新编译PDF),   # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
            "Class": PDF_Localize   # 新一代插件需要注册Class
-        },
-        "批量文件询问 (支持自定义总结各种文件)": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,
-            "AdvancedArgs": False,
-            "Info": "先上传文件，点击此按钮，进行提问",
-            "Function": HotReload(批量文件询问),
-            "Class": Document_Conversation_Wrap,
-        },
+        }
    }

    function_plugins.update(
@@ -400,7 +387,7 @@ def get_crazy_functions():
                "Color": "stop",
                "AsButton": False,
                "Info": "使用 DALLE2/DALLE3 生成图片 | 输入参数字符串，提供图像的内容",
-                "Function": HotReload(图片生成_DALLE2),   # 当注册Class后，Function旧接口仅会在“Void_Terminal”中起作用
+                "Function": HotReload(图片生成_DALLE2),   # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
                "Class": ImageGen_Wrap  # 新一代插件需要注册Class
            },
        }
@@ -426,8 +413,10 @@ def get_crazy_functions():



+
+    # -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
    try:
-        from crazy_functions.Arxiv_Downloader import 下载arxiv论文并翻译摘要
+        from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要

        function_plugins.update(
            {
@@ -444,6 +433,36 @@ def get_crazy_functions():
        logger.error(trimmed_format_exc())
        logger.error("Load function plugin failed")

+    # try:
+    #     from crazy_functions.联网的ChatGPT import 连接网络回答问题
+
+    #     function_plugins.update(
+    #         {
+    #             "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
+    #                 "Group": "对话",
+    #                 "Color": "stop",
+    #                 "AsButton": False,  # 加入下拉菜单中
+    #                 # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
+    #                 "Function": HotReload(连接网络回答问题),
+    #             }
+    #         }
+    #     )
+    #     from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
+
+    #     function_plugins.update(
+    #         {
+    #             "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
+    #                 "Group": "对话",
+    #                 "Color": "stop",
+    #                 "AsButton": False,  # 加入下拉菜单中
+    #                 "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
+    #                 "Function": HotReload(连接bing搜索回答问题),
+    #             }
+    #         }
+    #     )
+    # except:
+    #     logger.error(trimmed_format_exc())
+    #     logger.error("Load function plugin failed")

    try:
        from crazy_functions.SourceCode_Analyse import 解析任意code项目
@@ -465,7 +484,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.Multi_LLM_Query import 同时问询_指定模型
+        from crazy_functions.询问多个大语言模型 import 同时问询_指定模型

        function_plugins.update(
            {
@@ -486,7 +505,7 @@ def get_crazy_functions():


    try:
-        from crazy_functions.Audio_Summary import Audio_Summary
+        from crazy_functions.总结音视频 import 总结音视频

        function_plugins.update(
            {
@@ -497,7 +516,7 @@ def get_crazy_functions():
                    "AdvancedArgs": True,
                    "ArgsReminder": "调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。",
                    "Info": "批量总结音频或视频 | 输入参数为路径",
-                    "Function": HotReload(Audio_Summary),
+                    "Function": HotReload(总结音视频),
                }
            }
        )
@@ -506,7 +525,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.Math_Animation_Gen import 动画生成
+        from crazy_functions.数学动画生成manim import 动画生成

        function_plugins.update(
            {
@@ -543,7 +562,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.Vectorstore_QA import 知识库文件注入
+        from crazy_functions.知识库问答 import 知识库文件注入

        function_plugins.update(
            {
@@ -562,7 +581,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.Vectorstore_QA import 读取知识库作答
+        from crazy_functions.知识库问答 import 读取知识库作答

        function_plugins.update(
            {
@@ -581,7 +600,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.Interactive_Func_Template import 交互功能模板函数
+        from crazy_functions.交互功能函数模板 import 交互功能模板函数

        function_plugins.update(
            {
@@ -603,7 +622,7 @@ def get_crazy_functions():

        ENABLE_AUDIO = get_conf("ENABLE_AUDIO")
        if ENABLE_AUDIO:
-            from crazy_functions.Audio_Assistant import Audio_Assistant
+            from crazy_functions.语音助手 import 语音助手

            function_plugins.update(
                {
@@ -612,7 +631,7 @@ def get_crazy_functions():
                        "Color": "stop",
                        "AsButton": True,
                        "Info": "这是一个时刻聆听着的语音对话助手 | 没有输入参数",
-                        "Function": HotReload(Audio_Assistant),
+                        "Function": HotReload(语音助手),
                    }
                }
            )
@@ -621,7 +640,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.PDF_Translate_Nougat import 批量翻译PDF文档
+        from crazy_functions.批量翻译PDF文档_NOUGAT import 批量翻译PDF文档

        function_plugins.update(
            {
@@ -638,7 +657,7 @@ def get_crazy_functions():
        logger.error("Load function plugin failed")

    try:
-        from crazy_functions.Dynamic_Function_Generate import Dynamic_Function_Generate
+        from crazy_functions.函数动态生成 import 函数动态生成

        function_plugins.update(
            {
@@ -646,7 +665,7 @@ def get_crazy_functions():
                    "Group": "智能体",
                    "Color": "stop",
                    "AsButton": False,
-                    "Function": HotReload(Dynamic_Function_Generate),
+                    "Function": HotReload(函数动态生成),
                }
            }
        )
@@ -654,79 +673,40 @@ def get_crazy_functions():
        logger.error(trimmed_format_exc())
        logger.error("Load function plugin failed")

-    # try:
-    #     from crazy_functions.Multi_Agent_Legacy import Multi_Agent_Legacy终端
-    #     function_plugins.update(
-    #         {
-    #             "AutoGenMulti_Agent_Legacy终端（仅供测试）": {
-    #                 "Group": "智能体",
-    #                 "Color": "stop",
-    #                 "AsButton": False,
-    #                 "Function": HotReload(Multi_Agent_Legacy终端),
-    #             }
-    #         }
-    #     )
-    # except:
-    #     logger.error(trimmed_format_exc())
-    #     logger.error("Load function plugin failed")
-
    try:
-        from crazy_functions.Rag_Interface import Rag问答
+        from crazy_functions.多智能体 import 多智能体终端

        function_plugins.update(
            {
-                "Rag智能召回": {
-                    "Group": "对话",
+                "AutoGen多智能体终端（仅供测试）": {
+                    "Group": "智能体",
                    "Color": "stop",
                    "AsButton": False,
-                    "Info": "将问答数据记录到向量库中，作为长期参考。",
-                    "Function": HotReload(Rag问答),
-                },
+                    "Function": HotReload(多智能体终端),
+                }
            }
        )
    except:
        logger.error(trimmed_format_exc())
        logger.error("Load function plugin failed")

-    # try:
-    #     from crazy_functions.Document_Optimize import 自定义智能文档处理
-    #     function_plugins.update(
-    #         {
-    #             "一键处理文档（支持自定义全文润色、降重等）": {
-    #                 "Group": "学术",
-    #                 "Color": "stop",
-    #                 "AsButton": False,
-    #                 "AdvancedArgs": True,
-    #                 "ArgsReminder": "请输入处理指令和要求（可以详细描述），如：请帮我润色文本，要求幽默点。默认调用润色指令。",
-    #                 "Info": "保留文档结构，智能处理文档内容 | 输入参数为文件路径",
-    #                 "Function": HotReload(自定义智能文档处理)
-    #             },
-    #         }
-    #     )
-    # except:
-    #     logger.error(trimmed_format_exc())
-    #     logger.error("Load function plugin failed")
-
-
-
    try:
-        from crazy_functions.Paper_Reading import 快速论文解读
+        from crazy_functions.互动小游戏 import 随机小游戏
+
        function_plugins.update(
            {
-                "速读论文": {
-                    "Group": "学术",
+                "随机互动小游戏（仅供测试）": {
+                    "Group": "智能体",
                    "Color": "stop",
                    "AsButton": False,
-                    "Info": "上传一篇论文进行快速分析和解读 |  输入参数为论文路径或DOI/arXiv ID",
-                    "Function": HotReload(快速论文解读),
-                },
+                    "Function": HotReload(随机小游戏),
+                }
            }
        )
    except:
        logger.error(trimmed_format_exc())
        logger.error("Load function plugin failed")

-
    # try:
    #     from crazy_functions.高级功能函数模板 import 测试图表渲染
    #     function_plugins.update({
@@ -741,6 +721,19 @@ def get_crazy_functions():
    #     logger.error(trimmed_format_exc())
    #     print('Load function plugin failed')

+    # try:
+    #     from crazy_functions.chatglm微调工具 import 微调数据集生成
+    #     function_plugins.update({
+    #         "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
+    #             "Color": "stop",
+    #             "AsButton": False,
+    #             "AdvancedArgs": True,
+    #             "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
+    #             "Function": HotReload(微调数据集生成)
+    #         }
+    #     })
+    # except:
+    #     print('Load function plugin failed')

    """
    设置默认值:
@@ -760,26 +753,3 @@ def get_crazy_functions():
            function_plugins[name]["Color"] = "secondary"

    return function_plugins
-
-
-
-
-def get_multiplex_button_functions():
-    """多路复用主提交按钮的功能映射
-    """
-    return {
-        "常规对话":
-            "",
-
-        "查互联网后回答":
-            "查互联网后回答",
-
-        "多模型对话":
-            "询问多个GPT模型", # 映射到上面的 `询问多个GPT模型` 插件
-
-        "智能召回 RAG":
-            "Rag智能召回", # 映射到上面的 `Rag智能召回` 插件
-
-        "多媒体查询":
-            "多媒体智能体", # 映射到上面的 `多媒体智能体` 插件
-    }
--- a/crazy_functions/Academic_Conversation.py
+++ b/crazy_functions/Academic_Conversation.py
@@ -1,290 +0,0 @@
-import re
-import os
-import asyncio
-from typing import List, Dict, Tuple
-from dataclasses import dataclass
-from textwrap import dedent
-from toolbox import CatchException, get_conf, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
-from toolbox import update_ui, CatchException, report_exception, write_history_to_file
-from crazy_functions.review_fns.data_sources.semantic_source import SemanticScholarSource
-from crazy_functions.review_fns.data_sources.arxiv_source import ArxivSource
-from crazy_functions.review_fns.query_analyzer import QueryAnalyzer
-from crazy_functions.review_fns.handlers.review_handler import 文献综述功能
-from crazy_functions.review_fns.handlers.recommend_handler import 论文推荐功能
-from crazy_functions.review_fns.handlers.qa_handler import 学术问答功能
-from crazy_functions.review_fns.handlers.paper_handler import 单篇论文分析功能
-from crazy_functions.Conversation_To_File import write_chat_to_file
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from crazy_functions.review_fns.handlers.latest_handler import Arxiv最新论文推荐功能
-from datetime import datetime
-
-@CatchException
-def 学术对话(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-           history: List, system_prompt: str, user_request: str):
-    """主函数"""
-
-    # 初始化数据源
-    arxiv_source = ArxivSource()
-    semantic_source = SemanticScholarSource(
-        api_key=get_conf("SEMANTIC_SCHOLAR_KEY")
-    )
-
-    # 初始化处理器
-    handlers = {
-        "review": 文献综述功能(arxiv_source, semantic_source, llm_kwargs),
-        "recommend": 论文推荐功能(arxiv_source, semantic_source, llm_kwargs),
-        "qa": 学术问答功能(arxiv_source, semantic_source, llm_kwargs),
-        "paper": 单篇论文分析功能(arxiv_source, semantic_source, llm_kwargs),
-        "latest": Arxiv最新论文推荐功能(arxiv_source, semantic_source, llm_kwargs),
-    }
-
-    # 分析查询意图
-    chatbot.append([None, "正在分析研究主题和查询要求..."])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    query_analyzer = QueryAnalyzer()
-    search_criteria = yield from query_analyzer.analyze_query(txt, chatbot, llm_kwargs)
-    handler = handlers.get(search_criteria.query_type)
-    if not handler:
-        handler = handlers["qa"]  # 默认使用QA处理器
-
-    # 处理查询
-    chatbot.append([None, f"使用{handler.__class__.__name__}处理...，可能需要您耐心等待3～5分钟..."])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    final_prompt = asyncio.run(handler.handle(
-        criteria=search_criteria,
-        chatbot=chatbot,
-        history=history,
-        system_prompt=system_prompt,
-        llm_kwargs=llm_kwargs,
-        plugin_kwargs=plugin_kwargs
-    ))
-
-    if final_prompt:
-        # 检查是否是道歉提示
-        if "很抱歉，我们未能找到" in final_prompt:
-            chatbot.append([txt, final_prompt])
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-        # 在 final_prompt 末尾添加用户原始查询要求
-        final_prompt += dedent(f"""
-        Original user query: "{txt}"
-
-        IMPORTANT NOTE :
-        - Your response must directly address the user's original user query above
-        - While following the previous guidelines, prioritize answering what the user specifically asked
-        - Make sure your response format and content align with the user's expectations
-        - Do not translate paper titles, keep them in their original language
-        - Do not generate a reference list in your response - references will be handled separately
-        """)
-
-        # 使用最终的prompt生成回答
-        response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=final_prompt,
-            inputs_show_user=txt,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history=[],
-            sys_prompt=f"You are a helpful academic assistant. Response in Chinese by default unless specified language is required in the user's query."
-        )
-
-        # 1. 获取文献列表
-        papers_list = handler.ranked_papers  # 直接使用原始论文数据
-
-        # 在新的对话中添加格式化的参考文献列表
-        if papers_list:
-            references = ""
-            for idx, paper in enumerate(papers_list, 1):
-                # 构建作者列表
-                authors = paper.authors[:3]
-                if len(paper.authors) > 3:
-                    authors.append("et al.")
-                authors_str = ", ".join(authors)
-
-                # 构建期刊指标信息
-                metrics = []
-                if hasattr(paper, 'if_factor') and paper.if_factor:
-                    metrics.append(f"IF: {paper.if_factor}")
-                if hasattr(paper, 'jcr_division') and paper.jcr_division:
-                    metrics.append(f"JCR: {paper.jcr_division}")
-                if hasattr(paper, 'cas_division') and paper.cas_division:
-                    metrics.append(f"中科院分区: {paper.cas_division}")
-                metrics_str = f" [{', '.join(metrics)}]" if metrics else ""
-
-                # 构建DOI链接
-                doi_link = ""
-                if paper.doi:
-                    if "arxiv.org" in str(paper.doi):
-                        doi_url = paper.doi
-                    else:
-                        doi_url = f"https://doi.org/{paper.doi}"
-                    doi_link = f" <a href='{doi_url}' target='_blank'>DOI: {paper.doi}</a>"
-
-                # 构建完整的引用
-                reference = f"[{idx}] {authors_str}. *{paper.title}*"
-                if paper.venue_name:
-                    reference += f". {paper.venue_name}"
-                if paper.year:
-                    reference += f", {paper.year}"
-                reference += metrics_str
-                if doi_link:
-                    reference += f".{doi_link}"
-                reference += "  \n"
-
-                references += reference
-
-            # 添加新的对话显示参考文献
-            chatbot.append(["参考文献如下：", references])
-            yield from update_ui(chatbot=chatbot, history=history)
-
-
-        # 2. 保存为不同格式
-        from .review_fns.conversation_doc.word_doc import WordFormatter
-        from .review_fns.conversation_doc.word2pdf import WordToPdfConverter
-        from .review_fns.conversation_doc.markdown_doc import MarkdownFormatter
-        from .review_fns.conversation_doc.html_doc import HtmlFormatter
-
-        # 创建保存目录
-        save_dir =  get_log_folder(get_user(chatbot),  plugin_name='chatscholar')
-
-        if not os.path.exists(save_dir):
-            os.makedirs(save_dir)
-
-        # 生成文件名
-        def get_safe_filename(txt, max_length=10):
-            # 获取文本前max_length个字符作为文件名
-            filename = txt[:max_length].strip()
-            # 移除不安全的文件名字符
-            filename = re.sub(r'[\\/:*?"<>|]', '', filename)
-            # 如果文件名为空，使用时间戳
-            if not filename:
-                filename = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-            return filename
-
-        base_filename = get_safe_filename(txt)
-
-        result_files = []  # 收集所有生成的文件
-        pdf_path = None  # 用于跟踪PDF是否成功生成
-
-        # 保存为Markdown
-        try:
-            md_formatter = MarkdownFormatter()
-            md_content = md_formatter.create_document(txt, response, papers_list)
-            result_file_md = write_history_to_file(
-                history=[md_content],
-                file_basename=f"markdown_{base_filename}.md"
-            )
-            result_files.append(result_file_md)
-        except Exception as e:
-            print(f"Markdown保存失败: {str(e)}")
-
-        # 保存为HTML
-        try:
-            html_formatter = HtmlFormatter()
-            html_content = html_formatter.create_document(txt, response, papers_list)
-            result_file_html = write_history_to_file(
-                history=[html_content],
-                file_basename=f"html_{base_filename}.html"
-            )
-            result_files.append(result_file_html)
-        except Exception as e:
-            print(f"HTML保存失败: {str(e)}")
-
-        # 保存为Word
-        try:
-            word_formatter = WordFormatter()
-            try:
-                doc = word_formatter.create_document(txt, response, papers_list)
-            except Exception as e:
-                print(f"Word文档内容生成失败: {str(e)}")
-                raise e
-
-            try:
-                result_file_docx = os.path.join(
-                    os.path.dirname(result_file_md) if result_file_md else save_dir,
-                    f"docx_{base_filename}.docx"
-                )
-                doc.save(result_file_docx)
-                result_files.append(result_file_docx)
-                print(f"Word文档已保存到: {result_file_docx}")
-
-                # 转换为PDF
-                try:
-                    pdf_path = WordToPdfConverter.convert_to_pdf(result_file_docx)
-                    if pdf_path:
-                        result_files.append(pdf_path)
-                        print(f"PDF文档已生成: {pdf_path}")
-                except Exception as e:
-                    print(f"PDF转换失败: {str(e)}")
-
-            except Exception as e:
-                print(f"Word文档保存失败: {str(e)}")
-                raise e
-
-        except Exception as e:
-            print(f"Word格式化失败: {str(e)}")
-            import traceback
-            print(f"详细错误信息: {traceback.format_exc()}")
-
-        # 保存为BibTeX格式
-        try:
-            from .review_fns.conversation_doc.reference_formatter import ReferenceFormatter
-            ref_formatter = ReferenceFormatter()
-            bibtex_content = ref_formatter.create_document(papers_list)
-
-            # 在与其他文件相同目录下创建BibTeX文件
-            result_file_bib = os.path.join(
-                os.path.dirname(result_file_md) if result_file_md else save_dir,
-                f"references_{base_filename}.bib"
-            )
-
-            # 直接写入文件
-            with open(result_file_bib, 'w', encoding='utf-8') as f:
-                f.write(bibtex_content)
-
-            result_files.append(result_file_bib)
-            print(f"BibTeX文件已保存到: {result_file_bib}")
-        except Exception as e:
-            print(f"BibTeX格式保存失败: {str(e)}")
-
-        # 保存为EndNote格式
-        try:
-            from .review_fns.conversation_doc.endnote_doc import EndNoteFormatter
-            endnote_formatter = EndNoteFormatter()
-            endnote_content = endnote_formatter.create_document(papers_list)
-
-            # 在与其他文件相同目录下创建EndNote文件
-            result_file_enw = os.path.join(
-                os.path.dirname(result_file_md) if result_file_md else save_dir,
-                f"references_{base_filename}.enw"
-            )
-
-            # 直接写入文件
-            with open(result_file_enw, 'w', encoding='utf-8') as f:
-                f.write(endnote_content)
-
-            result_files.append(result_file_enw)
-            print(f"EndNote文件已保存到: {result_file_enw}")
-        except Exception as e:
-            print(f"EndNote格式保存失败: {str(e)}")
-
-        # 添加所有文件到下载区
-        success_files = []
-        for file in result_files:
-            try:
-                promote_file_to_downloadzone(file, chatbot=chatbot)
-                success_files.append(os.path.basename(file))
-            except Exception as e:
-                print(f"文件添加到下载区失败: {str(e)}")
-
-        # 更新成功提示消息
-        if success_files:
-            chatbot.append(["保存对话记录成功，bib和enw文件支持导入到EndNote、Zotero、JabRef、Mendeley等文献管理软件，HTML文件支持在浏览器中打开，里面包含详细论文源信息", "对话已保存并添加到下载区，可以在下载区找到相关文件"])
-        else:
-            chatbot.append(["保存对话记录", "所有格式的保存都失败了，请检查错误日志。"])
-
-        yield from update_ui(chatbot=chatbot, history=history)
-    else:
-        report_exception(chatbot, history, a=f"处理失败", b=f"请尝试其他查询")
-        yield from update_ui(chatbot=chatbot, history=history)
--- a/crazy_functions/Conversation_To_File.py
+++ b/crazy_functions/Conversation_To_File.py
@@ -1,11 +1,10 @@
-import re
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user, update_ui_latest_msg
+from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
 from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
-from loguru import logger
+import re

 f_prefix = 'GPT-Academic对话存档'

-def write_chat_to_file_legacy(chatbot, history=None, file_name=None):
+def write_chat_to_file(chatbot, history=None, file_name=None):
    """
    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
    """
@@ -13,9 +12,6 @@ def write_chat_to_file_legacy(chatbot, history=None, file_name=None):
    import time
    from themes.theme import advanced_css

-    if (file_name is not None) and (file_name != "") and (not file_name.endswith('.html')): file_name += '.html'
-    else: file_name = None
-
    if file_name is None:
        file_name = f_prefix + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
    fp = os.path.join(get_log_folder(get_user(chatbot), plugin_name='chat_history'), file_name)
@@ -72,147 +68,6 @@ def write_chat_to_file_legacy(chatbot, history=None, file_name=None):
    promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
    return '对话历史写入：' + fp

-def write_chat_to_file(chatbot, history=None, file_name=None):
-    """
-    将对话记录history以多种格式（HTML、Word、Markdown）写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    
-    Args:
-        chatbot: 聊天机器人对象，包含对话内容
-        history: 对话历史记录
-        file_name: 指定的文件名，如果为None则使用时间戳
-        
-    Returns:
-        str: 提示信息，包含文件保存路径
-    """
-    import os
-    import time
-    import asyncio
-    import aiofiles
-    from toolbox import promote_file_to_downloadzone
-    from crazy_functions.doc_fns.conversation_doc.excel_doc import save_chat_tables
-    from crazy_functions.doc_fns.conversation_doc.html_doc import HtmlFormatter
-    from crazy_functions.doc_fns.conversation_doc.markdown_doc import MarkdownFormatter
-    from crazy_functions.doc_fns.conversation_doc.word_doc import WordFormatter
-    from crazy_functions.doc_fns.conversation_doc.txt_doc import TxtFormatter
-    from crazy_functions.doc_fns.conversation_doc.word2pdf import WordToPdfConverter
-
-    async def save_html():
-        try:
-            html_formatter = HtmlFormatter(chatbot, history)
-            html_content = html_formatter.create_document()
-            html_file = os.path.join(save_dir, base_name + '.html')
-            async with aiofiles.open(html_file, 'w', encoding='utf8') as f:
-                await f.write(html_content)
-            return html_file
-        except Exception as e:
-            print(f"保存HTML格式失败: {str(e)}")
-            return None
-
-    async def save_word():
-        try:
-            word_formatter = WordFormatter()
-            doc = word_formatter.create_document(history)
-            docx_file = os.path.join(save_dir, base_name + '.docx')
-            # 由于python-docx不支持异步，使用线程池执行
-            loop = asyncio.get_event_loop()
-            await loop.run_in_executor(None, doc.save, docx_file)
-            return docx_file
-        except Exception as e:
-            print(f"保存Word格式失败: {str(e)}")
-            return None
-    async def save_pdf(docx_file):
-        try:
-            if docx_file:
-                # 获取文件名和保存路径
-                pdf_file = os.path.join(save_dir, base_name + '.pdf')
-                
-                # 在线程池中执行转换
-                loop = asyncio.get_event_loop()
-                pdf_file = await loop.run_in_executor(
-                    None, 
-                    WordToPdfConverter.convert_to_pdf,
-                    docx_file
-                    # save_dir
-                )
-                
-                return pdf_file
-                
-        except Exception as e:
-            print(f"保存PDF格式失败: {str(e)}")
-            return None
-
-    async def save_markdown():
-        try:
-            md_formatter = MarkdownFormatter()
-            md_content = md_formatter.create_document(history)
-            md_file = os.path.join(save_dir, base_name + '.md')
-            async with aiofiles.open(md_file, 'w', encoding='utf8') as f:
-                await f.write(md_content)
-            return md_file
-        except Exception as e:
-            print(f"保存Markdown格式失败: {str(e)}")
-            return None
-
-    async def save_txt():
-        try:
-            txt_formatter = TxtFormatter()
-            txt_content = txt_formatter.create_document(history)
-            txt_file = os.path.join(save_dir, base_name + '.txt')
-            async with aiofiles.open(txt_file, 'w', encoding='utf8') as f:
-                await f.write(txt_content)
-            return txt_file
-        except Exception as e:
-            print(f"保存TXT格式失败: {str(e)}")
-            return None
-
-    async def main():
-        # 并发执行所有保存任务
-        html_task = asyncio.create_task(save_html())
-        word_task = asyncio.create_task(save_word())
-        md_task = asyncio.create_task(save_markdown())
-        txt_task = asyncio.create_task(save_txt())
-        
-        # 等待所有任务完成
-        html_file = await html_task
-        docx_file = await word_task
-        md_file = await md_task
-        txt_file = await txt_task
-        
-        # PDF转换需要等待word文件生成完成
-        pdf_file = await save_pdf(docx_file)
-        # 收集所有成功生成的文件
-        result_files = [f for f in [html_file, docx_file, md_file, txt_file, pdf_file] if f]
-        
-        # 保存Excel表格
-        excel_files = save_chat_tables(history, save_dir, base_name)
-        result_files.extend(excel_files)
-        
-        return result_files
-
-    # 生成时间戳
-    timestamp = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    
-    # 获取保存目录
-    save_dir = get_log_folder(get_user(chatbot), plugin_name='chat_history')
-    
-    # 处理文件名
-    base_name = file_name if file_name else f"聊天记录_{timestamp}"
-    
-    # 运行异步任务
-    result_files = asyncio.run(main())
-    
-    # 将生成的文件添加到下载区
-    for file in result_files:
-        promote_file_to_downloadzone(file, rename_file=os.path.basename(file), chatbot=chatbot)
-    
-    # 如果没有成功保存任何文件，返回错误信息
-    if not result_files:
-        return "保存对话记录失败，请检查错误日志"
-    
-    ext_list = [os.path.splitext(f)[1] for f in result_files]
-    # 返回成功信息和文件路径
-    return f"对话历史已保存至以下格式文件：" + "、".join(ext_list)
-
 def gen_file_preview(file_name):
    try:
        with open(file_name, 'r', encoding='utf8') as f:
@@ -264,21 +119,12 @@ def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
    user_request    当前用户的请求信息（IP地址等）
    """
    file_name = plugin_kwargs.get("file_name", None)
+    if (file_name is not None) and (file_name != "") and (not file_name.endswith('.html')): file_name += '.html'
+    else: file_name = None
+
+    chatbot.append((None, f"[Local Message] {write_chat_to_file(chatbot, history, file_name)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新

-    chatbot.append((None, f"[Local Message] {write_chat_to_file_legacy(chatbot, history, file_name)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
-    try:
-        chatbot.append((None, f"[Local Message] 正在尝试生成pdf以及word格式的对话存档，请稍等..."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求需要一段时间，我们先及时地做一次界面更新
-        lastmsg = f"[Local Message] {write_chat_to_file(chatbot, history, file_name)}。" \
-              f"您可以调用下拉菜单中的“载入对话历史会话”还原当下的对话，请注意，目前只支持html格式载入历史。" \
-              f"当模型回答中存在表格，将提取表格内容存储为Excel的xlsx格式，如果你提供一些数据,然后输入指令要求模型帮你整理为表格" \
-              f"（如“请帮我将下面的数据整理为表格：”），再利用此插件就可以获取到Excel表格。"
-        yield from update_ui_latest_msg(lastmsg, chatbot, history) # 刷新界面 # 由于请求需要一段时间，我们先及时地做一次界面更新
-    except Exception as e:
-        logger.exception(f"已完成对话存档（pdf和word格式的对话存档生成未成功）。{str(e)}")
-        lastmsg = "已完成对话存档（pdf和word格式的对话存档生成未成功）。"
-        yield from update_ui_latest_msg(lastmsg, chatbot, history) # 刷新界面 # 由于请求需要一段时间，我们先及时地做一次界面更新
-    return

 class Conversation_To_File_Wrap(GptAcademicPluginTemplate):
    def __init__(self):
--- a/crazy_functions/Document_Conversation.py
+++ b/crazy_functions/Document_Conversation.py
@@ -1,537 +0,0 @@
-import os
-import threading
-import time
-from dataclasses import dataclass
-from typing import List, Tuple, Dict, Generator
-from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-from crazy_functions.rag_fns.rag_file_support import extract_text
-from request_llms.bridge_all import model_info
-from toolbox import update_ui, CatchException, report_exception
-from shared_utils.fastapi_server import validate_path_safety
-
-
-@dataclass
-class FileFragment:
-    """文件片段数据类，用于组织处理单元"""
-    file_path: str
-    content: str
-    rel_path: str
-    fragment_index: int
-    total_fragments: int
-
-
-class BatchDocumentSummarizer:
-    """优化的文档总结器 - 批处理版本"""
-
-    def __init__(self, llm_kwargs: Dict, query: str, chatbot: List, history: List, system_prompt: str):
-        """初始化总结器"""
-        self.llm_kwargs = llm_kwargs
-        self.query = query
-        self.chatbot = chatbot
-        self.history = history
-        self.system_prompt = system_prompt
-        self.failed_files = []
-        self.file_summaries_map = {}
-
-    def _get_token_limit(self) -> int:
-        """获取模型token限制"""
-        max_token = model_info[self.llm_kwargs['llm_model']]['max_token']
-        return max_token * 3 // 4
-
-    def _create_batch_inputs(self, fragments: List[FileFragment]) -> Tuple[List, List, List]:
-        """创建批处理输入"""
-        inputs_array = []
-        inputs_show_user_array = []
-        history_array = []
-
-        for frag in fragments:
-            if self.query:
-                i_say = (f'请按照用户要求对文件内容进行处理，文件名为{os.path.basename(frag.file_path)}，'
-                         f'用户要求为：{self.query}：'
-                         f'文件内容是 ```{frag.content}```')
-                i_say_show_user = (f'正在处理 {frag.rel_path} (片段 {frag.fragment_index + 1}/{frag.total_fragments})')
-            else:
-                i_say = (f'请对下面的内容用中文做总结，不超过500字，文件名是{os.path.basename(frag.file_path)}，'
-                         f'内容是 ```{frag.content}```')
-                i_say_show_user = f'正在处理 {frag.rel_path} (片段 {frag.fragment_index + 1}/{frag.total_fragments})'
-
-            inputs_array.append(i_say)
-            inputs_show_user_array.append(i_say_show_user)
-            history_array.append([])
-
-        return inputs_array, inputs_show_user_array, history_array
-
-    def _process_single_file_with_timeout(self, file_info: Tuple[str, str], mutable_status: List) -> List[FileFragment]:
-        """包装了超时控制的文件处理函数"""
-
-        def timeout_handler():
-            thread = threading.current_thread()
-            if hasattr(thread, '_timeout_occurred'):
-                thread._timeout_occurred = True
-
-        # 设置超时标记
-        thread = threading.current_thread()
-        thread._timeout_occurred = False
-
-        # 设置超时时间为30秒，给予更多处理时间
-        TIMEOUT_SECONDS = 30
-        timer = threading.Timer(TIMEOUT_SECONDS, timeout_handler)
-        timer.start()
-
-        try:
-            fp, project_folder = file_info
-            fragments = []
-
-            # 定期检查是否超时
-            def check_timeout():
-                if hasattr(thread, '_timeout_occurred') and thread._timeout_occurred:
-                    raise TimeoutError(f"处理文件 {os.path.basename(fp)} 超时（{TIMEOUT_SECONDS}秒）")
-
-            # 更新状态
-            mutable_status[0] = "检查文件大小"
-            mutable_status[1] = time.time()
-            check_timeout()
-
-            # 文件大小检查
-            if os.path.getsize(fp) > self.max_file_size:
-                self.failed_files.append((fp, f"文件过大：超过{self.max_file_size / 1024 / 1024}MB"))
-                mutable_status[2] = "文件过大"
-                return fragments
-
-            # 更新状态
-            mutable_status[0] = "提取文件内容"
-            mutable_status[1] = time.time()
-
-            # 提取内容 - 使用单独的超时控制
-            content = None
-            extract_start_time = time.time()
-            try:
-                while True:
-                    check_timeout()  # 检查全局超时
-
-                    # 检查提取过程是否超时（10秒）
-                    if time.time() - extract_start_time > 10:
-                        raise TimeoutError("文件内容提取超时（10秒）")
-
-                    try:
-                        content = extract_text(fp)
-                        break
-                    except Exception as e:
-                        if "timeout" in str(e).lower():
-                            continue  # 如果是临时超时，重试
-                        raise  # 其他错误直接抛出
-
-            except Exception as e:
-                self.failed_files.append((fp, f"文件读取失败：{str(e)}"))
-                mutable_status[2] = "读取失败"
-                return fragments
-
-            if content is None:
-                self.failed_files.append((fp, "文件解析失败：不支持的格式或文件损坏"))
-                mutable_status[2] = "格式不支持"
-                return fragments
-            elif not content.strip():
-                self.failed_files.append((fp, "文件内容为空"))
-                mutable_status[2] = "内容为空"
-                return fragments
-
-            check_timeout()
-
-            # 更新状态
-            mutable_status[0] = "分割文本"
-            mutable_status[1] = time.time()
-
-            # 分割文本 - 添加超时检查
-            split_start_time = time.time()
-            try:
-                while True:
-                    check_timeout()  # 检查全局超时
-
-                    # 检查分割过程是否超时（5秒）
-                    if time.time() - split_start_time > 5:
-                        raise TimeoutError("文本分割超时（5秒）")
-
-                    paper_fragments = breakdown_text_to_satisfy_token_limit(
-                        txt=content,
-                        limit=self._get_token_limit(),
-                        llm_model=self.llm_kwargs['llm_model']
-                    )
-                    break
-
-            except Exception as e:
-                self.failed_files.append((fp, f"文本分割失败：{str(e)}"))
-                mutable_status[2] = "分割失败"
-                return fragments
-
-            # 处理片段
-            rel_path = os.path.relpath(fp, project_folder)
-            for i, frag in enumerate(paper_fragments):
-                check_timeout()  # 每处理一个片段检查一次超时
-                if frag.strip():
-                    fragments.append(FileFragment(
-                        file_path=fp,
-                        content=frag,
-                        rel_path=rel_path,
-                        fragment_index=i,
-                        total_fragments=len(paper_fragments)
-                    ))
-
-            mutable_status[2] = "处理完成"
-            return fragments
-
-        except TimeoutError as e:
-            self.failed_files.append((fp, str(e)))
-            mutable_status[2] = "处理超时"
-            return []
-        except Exception as e:
-            self.failed_files.append((fp, f"处理失败：{str(e)}"))
-            mutable_status[2] = "处理异常"
-            return []
-        finally:
-            timer.cancel()
-
-    def prepare_fragments(self, project_folder: str, file_paths: List[str]) -> Generator:
-        import concurrent.futures
-
-        from concurrent.futures import ThreadPoolExecutor
-        from typing import Generator, List
-        """并行准备所有文件的处理片段"""
-        all_fragments = []
-        total_files = len(file_paths)
-
-        # 配置参数
-        self.refresh_interval = 0.2  # UI刷新间隔
-        self.watch_dog_patience = 5  # 看门狗超时时间
-        self.max_file_size = 10 * 1024 * 1024  # 10MB限制
-        self.max_workers = min(32, len(file_paths))  # 最多32个线程
-
-        # 创建有超时控制的线程池
-        executor = ThreadPoolExecutor(max_workers=self.max_workers)
-
-        # 用于跨线程状态传递的可变列表 - 增加文件名信息
-        mutable_status_array = [["等待中", time.time(), "pending", file_path] for file_path in file_paths]
-
-        # 创建文件处理任务
-        file_infos = [(fp, project_folder) for fp in file_paths]
-
-        # 提交所有任务，使用带超时控制的处理函数
-        futures = [
-            executor.submit(
-                self._process_single_file_with_timeout,
-                file_info,
-                mutable_status_array[i]
-            ) for i, file_info in enumerate(file_infos)
-        ]
-
-        # 更新UI的计数器
-        cnt = 0
-
-        try:
-            # 监控任务执行
-            while True:
-                time.sleep(self.refresh_interval)
-                cnt += 1
-
-                # 检查任务完成状态
-                worker_done = [f.done() for f in futures]
-
-                # 更新状态显示
-                status_str = ""
-                for i, (status, timestamp, desc, file_path) in enumerate(mutable_status_array):
-                    # 获取文件名（去掉路径）
-                    file_name = os.path.basename(file_path)
-                    if worker_done[i]:
-                        status_str += f"文件 {file_name}: {desc}\n\n"
-                    else:
-                        status_str += f"文件 {file_name}: {status} {desc}\n\n"
-
-                # 更新UI
-                self.chatbot[-1] = [
-                    "处理进度",
-                    f"正在处理文件...\n\n{status_str}" + "." * (cnt % 10 + 1)
-                ]
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-                # 检查是否所有任务完成
-                if all(worker_done):
-                    break
-
-        finally:
-            # 确保线程池正确关闭
-            executor.shutdown(wait=False)
-
-        # 收集结果
-        processed_files = 0
-        for future in futures:
-            try:
-                fragments = future.result(timeout=0.1)  # 给予一个短暂的超时时间来获取结果
-                all_fragments.extend(fragments)
-                processed_files += 1
-            except concurrent.futures.TimeoutError:
-                # 处理获取结果超时
-                file_index = futures.index(future)
-                self.failed_files.append((file_paths[file_index], "结果获取超时"))
-                continue
-            except Exception as e:
-                # 处理其他异常
-                file_index = futures.index(future)
-                self.failed_files.append((file_paths[file_index], f"未知错误：{str(e)}"))
-                continue
-
-        # 最终进度更新
-        self.chatbot.append([
-            "文件处理完成",
-            f"成功处理 {len(all_fragments)} 个片段，失败 {len(self.failed_files)} 个文件"
-        ])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        return all_fragments
-
-    def _process_fragments_batch(self, fragments: List[FileFragment]) -> Generator:
-        """批量处理文件片段"""
-        from collections import defaultdict
-        batch_size = 64  # 每批处理的片段数
-        max_retries = 3  # 最大重试次数
-        retry_delay = 5  # 重试延迟（秒）
-        results = defaultdict(list)
-
-        # 按批次处理
-        for i in range(0, len(fragments), batch_size):
-            batch = fragments[i:i + batch_size]
-
-            inputs_array, inputs_show_user_array, history_array = self._create_batch_inputs(batch)
-            sys_prompt_array = ["请总结以下内容："] * len(batch)
-
-            # 添加重试机制
-            for retry in range(max_retries):
-                try:
-                    response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                        inputs_array=inputs_array,
-                        inputs_show_user_array=inputs_show_user_array,
-                        llm_kwargs=self.llm_kwargs,
-                        chatbot=self.chatbot,
-                        history_array=history_array,
-                        sys_prompt_array=sys_prompt_array,
-                    )
-
-                    # 处理响应
-                    for j, frag in enumerate(batch):
-                        summary = response_collection[j * 2 + 1]
-                        if summary and summary.strip():
-                            results[frag.rel_path].append({
-                                'index': frag.fragment_index,
-                                'summary': summary,
-                                'total': frag.total_fragments
-                            })
-                    break  # 成功处理，跳出重试循环
-
-                except Exception as e:
-                    if retry == max_retries - 1:  # 最后一次重试失败
-                        for frag in batch:
-                            self.failed_files.append((frag.file_path, f"处理失败：{str(e)}"))
-                    else:
-                        yield from update_ui(self.chatbot.append([f"批次处理失败，{retry_delay}秒后重试...", str(e)]))
-                        time.sleep(retry_delay)
-
-        return results
-
-    def _generate_final_summary_request(self) -> Tuple[List, List, List]:
-        """准备最终总结请求"""
-        if not self.file_summaries_map:
-            return (["无可用的文件总结"], ["生成最终总结"], [[]])
-
-        summaries = list(self.file_summaries_map.values())
-        if all(not summary for summary in summaries):
-            return (["所有文件处理均失败"], ["生成最终总结"], [[]])
-
-        if self.plugin_kwargs.get("advanced_arg"):
-            i_say = "根据以上所有文件的处理结果，按要求进行综合处理：" + self.plugin_kwargs['advanced_arg']
-        else:
-            i_say = "请根据以上所有文件的处理结果，生成最终的总结，不超过1000字。"
-
-        return ([i_say], [i_say], [summaries])
-
-    def process_files(self, project_folder: str, file_paths: List[str]) -> Generator:
-        """处理所有文件"""
-        total_files = len(file_paths)
-        self.chatbot.append([f"开始处理", f"总计 {total_files} 个文件"])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 1. 准备所有文件片段
-        # 在 process_files 函数中：
-        fragments = yield from self.prepare_fragments(project_folder, file_paths)
-        if not fragments:
-            self.chatbot.append(["处理失败", "没有可处理的文件内容"])
-            return "没有可处理的文件内容"
-
-        # 2. 批量处理所有文件片段
-        self.chatbot.append([f"文件分析", f"共计 {len(fragments)} 个处理单元"])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        try:
-            file_summaries = yield from self._process_fragments_batch(fragments)
-        except Exception as e:
-            self.chatbot.append(["处理错误", f"批处理过程失败：{str(e)}"])
-            return "处理过程发生错误"
-
-        # 3. 为每个文件生成整体总结
-        self.chatbot.append(["生成总结", "正在汇总文件内容..."])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 处理每个文件的总结
-        for rel_path, summaries in file_summaries.items():
-            if len(summaries) > 1:  # 多片段文件需要生成整体总结
-                sorted_summaries = sorted(summaries, key=lambda x: x['index'])
-                if self.plugin_kwargs.get("advanced_arg"):
-
-                    i_say = f'请按照用户要求对文件内容进行处理，用户要求为：{self.plugin_kwargs["advanced_arg"]}：'
-                else:
-                    i_say = f"请总结文件 {os.path.basename(rel_path)} 的主要内容，不超过500字。"
-
-                try:
-                    summary_texts = [s['summary'] for s in sorted_summaries]
-                    response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                        inputs_array=[i_say],
-                        inputs_show_user_array=[f"生成 {rel_path} 的处理结果"],
-                        llm_kwargs=self.llm_kwargs,
-                        chatbot=self.chatbot,
-                        history_array=[summary_texts],
-                        sys_prompt_array=["你是一个优秀的助手，"],
-                    )
-                    self.file_summaries_map[rel_path] = response_collection[1]
-                except Exception as e:
-                    self.chatbot.append(["警告", f"文件 {rel_path} 总结生成失败：{str(e)}"])
-                    self.file_summaries_map[rel_path] = "总结生成失败"
-            else:  # 单片段文件直接使用其唯一的总结
-                self.file_summaries_map[rel_path] = summaries[0]['summary']
-
-        # 4. 生成最终总结
-        if total_files == 1:
-            return "文件数为1，此时不调用总结模块"
-        else:
-            try:
-                # 收集所有文件的总结用于生成最终总结
-                file_summaries_for_final = []
-                for rel_path, summary in self.file_summaries_map.items():
-                    file_summaries_for_final.append(f"文件 {rel_path} 的总结：\n{summary}")
-
-                if self.plugin_kwargs.get("advanced_arg"):
-                    final_summary_prompt = ("根据以下所有文件的总结内容，按要求进行综合处理：" +
-                                            self.plugin_kwargs['advanced_arg'])
-                else:
-                    final_summary_prompt = "请根据以下所有文件的总结内容，生成最终的总结报告。"
-
-                response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                    inputs_array=[final_summary_prompt],
-                    inputs_show_user_array=["生成最终总结报告"],
-                    llm_kwargs=self.llm_kwargs,
-                    chatbot=self.chatbot,
-                    history_array=[file_summaries_for_final],
-                    sys_prompt_array=["总结所有文件内容。"],
-                    max_workers=1
-                )
-
-                return response_collection[1] if len(response_collection) > 1 else "生成总结失败"
-            except Exception as e:
-                self.chatbot.append(["错误", f"最终总结生成失败：{str(e)}"])
-                return "生成总结失败"
-
-    def save_results(self, final_summary: str):
-        """保存结果到文件"""
-        from toolbox import promote_file_to_downloadzone, write_history_to_file
-        from crazy_functions.doc_fns.batch_file_query_doc import MarkdownFormatter, HtmlFormatter, WordFormatter
-        import os
-        timestamp = time.strftime("%Y%m%d_%H%M%S")
-
-        # 创建各种格式化器
-        md_formatter = MarkdownFormatter(final_summary, self.file_summaries_map, self.failed_files)
-        html_formatter = HtmlFormatter(final_summary, self.file_summaries_map, self.failed_files)
-        word_formatter = WordFormatter(final_summary, self.file_summaries_map, self.failed_files)
-
-        result_files = []
-
-        # 保存 Markdown
-        try:
-            md_content = md_formatter.create_document()
-            result_file_md = write_history_to_file(
-                history=[md_content],  # 直接传入内容列表
-                file_basename=f"文档总结_{timestamp}.md"
-            )
-            result_files.append(result_file_md)
-        except:
-            pass
-
-        # 保存 HTML
-        try:
-            html_content = html_formatter.create_document()
-            result_file_html = write_history_to_file(
-                history=[html_content],
-                file_basename=f"文档总结_{timestamp}.html"
-            )
-            result_files.append(result_file_html)
-        except:
-            pass
-
-        # 保存 Word
-        try:
-            doc = word_formatter.create_document()
-            # 由于 Word 文档需要用 doc.save()，我们使用与 md 文件相同的目录
-            result_file_docx = os.path.join(
-                os.path.dirname(result_file_md),
-                f"文档总结_{timestamp}.docx"
-            )
-            doc.save(result_file_docx)
-            result_files.append(result_file_docx)
-        except:
-            pass
-
-        # 添加到下载区
-        for file in result_files:
-            promote_file_to_downloadzone(file, chatbot=self.chatbot)
-
-        self.chatbot.append(["处理完成", f"结果已保存至: {', '.join(result_files)}"])
-
-
-@CatchException
-def 批量文件询问(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-                 history: List, system_prompt: str, user_request: str):
-    """主函数 - 优化版本"""
-    # 初始化
-    import glob
-    import re
-    from crazy_functions.rag_fns.rag_file_support import supports_format
-    from toolbox import report_exception
-    query = plugin_kwargs.get("advanced_arg")
-    summarizer = BatchDocumentSummarizer(llm_kwargs, query, chatbot, history, system_prompt)
-    chatbot.append(["函数插件功能", f"作者：lbykkkk，批量总结文件。支持格式: {', '.join(supports_format)}等其他文本格式文件，如果长时间卡在文件处理过程，请查看处理进度，然后删除所有处于“pending”状态的文件，然后重新上传处理。"])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    # 验证输入路径
-    if not os.path.exists(txt):
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 获取文件列表
-    project_folder = txt
-    user_name = chatbot.get_user()
-    validate_path_safety(project_folder, user_name)
-    extract_folder = next((d for d in glob.glob(f'{project_folder}/*')
-                           if os.path.isdir(d) and d.endswith('.extract')), project_folder)
-    exclude_patterns = r'/[^/]+\.(zip|rar|7z|tar|gz)$'
-    file_manifest = [f for f in glob.glob(f'{extract_folder}/**', recursive=True)
-                     if os.path.isfile(f) and not re.search(exclude_patterns, f)]
-
-    if not file_manifest:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b="未找到支持的文件类型")
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 处理所有文件并生成总结
-    final_summary = yield from summarizer.process_files(project_folder, file_manifest)
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    # 保存结果
-    summarizer.save_results(final_summary)
-    yield from update_ui(chatbot=chatbot, history=history)
--- a/crazy_functions/Document_Conversation_Wrap.py
+++ b/crazy_functions/Document_Conversation_Wrap.py
@@ -1,36 +0,0 @@
-import random
-from toolbox import get_conf
-from crazy_functions.Document_Conversation import 批量文件询问
-from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
-
-
-class Document_Conversation_Wrap(GptAcademicPluginTemplate):
-    def __init__(self):
-        """
-        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
-        """
-        pass
-
-    def define_arg_selection_menu(self):
-        """
-        定义插件的二级选项菜单
-
-        第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
-        第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
-        第三个参数，名称`allow_cache`，参数`type`声明这是一个下拉菜单，下拉菜单上方显示`title`+`description`，下拉菜单的选项为`options`，`default_value`为下拉菜单默认值；
-
-        """
-        gui_definition = {
-            "main_input":
-                ArgProperty(title="已上传的文件", description="上传文件后自动填充", default_value="", type="string").model_dump_json(),
-            "searxng_url":
-                ArgProperty(title="对材料提问", description="提问", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
-        }
-        return gui_definition
-
-    def execute(txt, llm_kwargs, plugin_kwargs:dict, chatbot, history, system_prompt, user_request):
-        """
-        执行插件
-        """
-        yield from 批量文件询问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
-
--- a/crazy_functions/Document_Optimize.py
+++ b/crazy_functions/Document_Optimize.py
@@ -1,673 +0,0 @@
-import os
-import time
-import glob
-import re
-import threading
-from typing import Dict, List, Generator, Tuple
-from dataclasses import dataclass
-
-from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-from crazy_functions.rag_fns.rag_file_support import extract_text, supports_format, convert_to_markdown
-from request_llms.bridge_all import model_info
-from toolbox import update_ui, CatchException, report_exception, promote_file_to_downloadzone, write_history_to_file
-from shared_utils.fastapi_server import validate_path_safety
-
-# 新增：导入结构化论文提取器
-from crazy_functions.doc_fns.read_fns.unstructured_all.paper_structure_extractor import PaperStructureExtractor, ExtractorConfig, StructuredPaper
-
-# 导入格式化器
-from crazy_functions.paper_fns.file2file_doc import (
-    TxtFormatter,
-    MarkdownFormatter,
-    HtmlFormatter,
-    WordFormatter
-)
-
-@dataclass
-class TextFragment:
-    """文本片段数据类，用于组织处理单元"""
-    content: str
-    fragment_index: int
-    total_fragments: int
-
-
-class DocumentProcessor:
-    """文档处理器 - 处理单个文档并输出结果"""
-
-    def __init__(self, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List, history: List, system_prompt: str):
-        """初始化处理器"""
-        self.llm_kwargs = llm_kwargs
-        self.plugin_kwargs = plugin_kwargs
-        self.chatbot = chatbot
-        self.history = history
-        self.system_prompt = system_prompt
-        self.processed_results = []
-        self.failed_fragments = []
-        # 新增：初始化论文结构提取器
-        self.paper_extractor = PaperStructureExtractor()
-
-    def _get_token_limit(self) -> int:
-        """获取模型token限制，返回更小的值以确保更细粒度的分割"""
-        max_token = model_info[self.llm_kwargs['llm_model']]['max_token']
-        # 降低token限制，使每个片段更小
-        return max_token // 4  # 从3/4降低到1/4
-
-    def _create_batch_inputs(self, fragments: List[TextFragment]) -> Tuple[List, List, List]:
-        """创建批处理输入"""
-        inputs_array = []
-        inputs_show_user_array = []
-        history_array = []
-
-        user_instruction = self.plugin_kwargs.get("advanced_arg", "请润色以下学术文本，提高其语言表达的准确性、专业性和流畅度，保持学术风格，确保逻辑连贯，但不改变原文的科学内容和核心观点")
-
-        for frag in fragments:
-            i_say = (f'请按照以下要求处理文本内容：{user_instruction}\n\n'
-                     f'请将对文本的处理结果放在<decision>和</decision>标签之间。\n\n'
-                     f'文本内容：\n```\n{frag.content}\n```')
-
-            i_say_show_user = f'正在处理文本片段 {frag.fragment_index + 1}/{frag.total_fragments}'
-
-            inputs_array.append(i_say)
-            inputs_show_user_array.append(i_say_show_user)
-            history_array.append([])
-
-        return inputs_array, inputs_show_user_array, history_array
-
-    def _extract_decision(self, text: str) -> str:
-        """从LLM响应中提取<decision>标签内的内容"""
-        import re
-        pattern = r'<decision>(.*?)</decision>'
-        matches = re.findall(pattern, text, re.DOTALL)
-
-        if matches:
-            return matches[0].strip()
-        else:
-            # 如果没有找到标签，返回原始文本
-            return text.strip()
-
-    def process_file(self, file_path: str) -> Generator:
-        """处理单个文件"""
-        self.chatbot.append(["开始处理文件", f"文件路径: {file_path}"])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        try:
-            # 首先尝试转换为Markdown
-            from crazy_functions.rag_fns.rag_file_support import convert_to_markdown
-            file_path = convert_to_markdown(file_path)
-
-            # 1. 检查文件是否为支持的论文格式
-            is_paper_format = any(file_path.lower().endswith(ext) for ext in self.paper_extractor.SUPPORTED_EXTENSIONS)
-
-            if is_paper_format:
-                # 使用结构化提取器处理论文
-                return (yield from self._process_structured_paper(file_path))
-            else:
-                # 使用原有方式处理普通文档
-                return (yield from self._process_regular_file(file_path))
-
-        except Exception as e:
-            self.chatbot.append(["处理错误", f"文件处理失败: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return None
-
-    def _process_structured_paper(self, file_path: str) -> Generator:
-        """处理结构化论文文件"""
-        # 1. 提取论文结构
-        self.chatbot[-1] = ["正在分析论文结构", f"文件路径: {file_path}"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        try:
-            paper = self.paper_extractor.extract_paper_structure(file_path)
-
-            if not paper or not paper.sections:
-                self.chatbot.append(["无法提取论文结构", "将使用全文内容进行处理"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-                # 使用全文内容进行段落切分
-                if paper and paper.full_text:
-                    # 使用增强的分割函数进行更细致的分割
-                    fragments = self._breakdown_section_content(paper.full_text)
-
-                    # 创建文本片段对象
-                    text_fragments = []
-                    for i, frag in enumerate(fragments):
-                        if frag.strip():
-                            text_fragments.append(TextFragment(
-                                content=frag,
-                                fragment_index=i,
-                                total_fragments=len(fragments)
-                            ))
-
-                    # 批量处理片段
-                    if text_fragments:
-                        self.chatbot[-1] = ["开始处理文本", f"共 {len(text_fragments)} 个片段"]
-                        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-                        # 一次性准备所有输入
-                        inputs_array, inputs_show_user_array, history_array = self._create_batch_inputs(text_fragments)
-
-                        # 使用系统提示
-                        instruction = self.plugin_kwargs.get("advanced_arg", "请润色以下学术文本，提高其语言表达的准确性、专业性和流畅度，保持学术风格，确保逻辑连贯，但不改变原文的科学内容和核心观点")
-                        sys_prompt_array = [f"你是一个专业的学术文献编辑助手。请按照用户的要求：'{instruction}'处理文本。保持学术风格，增强表达的准确性和专业性。"] * len(text_fragments)
-
-                        # 调用LLM一次性处理所有片段
-                        response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                            inputs_array=inputs_array,
-                            inputs_show_user_array=inputs_show_user_array,
-                            llm_kwargs=self.llm_kwargs,
-                            chatbot=self.chatbot,
-                            history_array=history_array,
-                            sys_prompt_array=sys_prompt_array,
-                        )
-
-                        # 处理响应
-                        for j, frag in enumerate(text_fragments):
-                            try:
-                                llm_response = response_collection[j * 2 + 1]
-                                processed_text = self._extract_decision(llm_response)
-
-                                if processed_text and processed_text.strip():
-                                    self.processed_results.append({
-                                        'index': frag.fragment_index,
-                                        'content': processed_text
-                                    })
-                                else:
-                                    self.failed_fragments.append(frag)
-                                    self.processed_results.append({
-                                        'index': frag.fragment_index,
-                                        'content': frag.content
-                                    })
-                            except Exception as e:
-                                self.failed_fragments.append(frag)
-                                self.processed_results.append({
-                                    'index': frag.fragment_index,
-                                    'content': frag.content
-                                })
-
-                        # 按原始顺序合并结果
-                        self.processed_results.sort(key=lambda x: x['index'])
-                        final_content = "\n".join([item['content'] for item in self.processed_results])
-
-                        # 更新UI
-                        success_count = len(text_fragments) - len(self.failed_fragments)
-                        self.chatbot[-1] = ["处理完成", f"成功处理 {success_count}/{len(text_fragments)} 个片段"]
-                        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-                        return final_content
-                    else:
-                        self.chatbot.append(["处理失败", "未能提取到有效的文本内容"])
-                        yield from update_ui(chatbot=self.chatbot, history=self.history)
-                        return None
-                else:
-                    self.chatbot.append(["处理失败", "未能提取到论文内容"])
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                    return None
-
-            # 2. 准备处理章节内容（不处理标题）
-            self.chatbot[-1] = ["已提取论文结构", f"共 {len(paper.sections)} 个主要章节"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            # 3. 收集所有需要处理的章节内容并分割为合适大小
-            sections_to_process = []
-            section_map = {}  # 用于映射处理前后的内容
-
-            def collect_section_contents(sections, parent_path=""):
-                """递归收集章节内容，跳过参考文献部分"""
-                for i, section in enumerate(sections):
-                    current_path = f"{parent_path}/{i}" if parent_path else f"{i}"
-
-                    # 检查是否为参考文献部分，如果是则跳过
-                    if section.section_type == 'references' or section.title.lower() in ['references', '参考文献', 'bibliography', '文献']:
-                        continue  # 跳过参考文献部分
-
-                    # 只处理内容非空的章节
-                    if section.content and section.content.strip():
-                        # 使用增强的分割函数进行更细致的分割
-                        fragments = self._breakdown_section_content(section.content)
-
-                        for fragment_idx, fragment_content in enumerate(fragments):
-                            if fragment_content.strip():
-                                fragment_index = len(sections_to_process)
-                                sections_to_process.append(TextFragment(
-                                    content=fragment_content,
-                                    fragment_index=fragment_index,
-                                    total_fragments=0  # 临时值，稍后更新
-                                ))
-
-                                # 保存映射关系，用于稍后更新章节内容
-                                # 为每个片段存储原始章节和片段索引信息
-                                section_map[fragment_index] = (current_path, section, fragment_idx, len(fragments))
-
-                    # 递归处理子章节
-                    if section.subsections:
-                        collect_section_contents(section.subsections, current_path)
-
-            # 收集所有章节内容
-            collect_section_contents(paper.sections)
-
-            # 更新总片段数
-            total_fragments = len(sections_to_process)
-            for frag in sections_to_process:
-                frag.total_fragments = total_fragments
-
-            # 4. 如果没有内容需要处理，直接返回
-            if not sections_to_process:
-                self.chatbot.append(["处理完成", "未找到需要处理的内容"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return None
-
-            # 5. 批量处理章节内容
-            self.chatbot[-1] = ["开始处理论文内容", f"共 {len(sections_to_process)} 个内容片段"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            # 一次性准备所有输入
-            inputs_array, inputs_show_user_array, history_array = self._create_batch_inputs(sections_to_process)
-
-            # 使用系统提示
-            instruction = self.plugin_kwargs.get("advanced_arg", "请润色以下学术文本，提高其语言表达的准确性、专业性和流畅度，保持学术风格，确保逻辑连贯，但不改变原文的科学内容和核心观点")
-            sys_prompt_array = [f"你是一个专业的学术文献编辑助手。请按照用户的要求：'{instruction}'处理文本。保持学术风格，增强表达的准确性和专业性。"] * len(sections_to_process)
-
-            # 调用LLM一次性处理所有片段
-            response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                inputs_array=inputs_array,
-                inputs_show_user_array=inputs_show_user_array,
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history_array=history_array,
-                sys_prompt_array=sys_prompt_array,
-            )
-
-            # 处理响应，重组章节内容
-            section_contents = {}  # 用于重组各章节的处理后内容
-
-            for j, frag in enumerate(sections_to_process):
-                try:
-                    llm_response = response_collection[j * 2 + 1]
-                    processed_text = self._extract_decision(llm_response)
-
-                    if processed_text and processed_text.strip():
-                        # 保存处理结果
-                        self.processed_results.append({
-                            'index': frag.fragment_index,
-                            'content': processed_text
-                        })
-
-                        # 存储处理后的文本片段，用于后续重组
-                        fragment_index = frag.fragment_index
-                        if fragment_index in section_map:
-                            path, section, fragment_idx, total_fragments = section_map[fragment_index]
-
-                            # 初始化此章节的内容容器（如果尚未创建）
-                            if path not in section_contents:
-                                section_contents[path] = [""] * total_fragments
-
-                            # 将处理后的片段放入正确位置
-                            section_contents[path][fragment_idx] = processed_text
-                    else:
-                        self.failed_fragments.append(frag)
-                except Exception as e:
-                    self.failed_fragments.append(frag)
-
-            # 重组每个章节的内容
-            for path, fragments in section_contents.items():
-                section = None
-                for idx in section_map:
-                    if section_map[idx][0] == path:
-                        section = section_map[idx][1]
-                        break
-
-                if section:
-                    # 合并该章节的所有处理后片段
-                    section.content = "\n".join(fragments)
-
-            # 6. 更新UI
-            success_count = total_fragments - len(self.failed_fragments)
-            self.chatbot[-1] = ["处理完成", f"成功处理 {success_count}/{total_fragments} 个内容片段"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            # 收集参考文献部分（不进行处理）
-            references_sections = []
-            def collect_references(sections, parent_path=""):
-                """递归收集参考文献部分"""
-                for i, section in enumerate(sections):
-                    current_path = f"{parent_path}/{i}" if parent_path else f"{i}"
-
-                    # 检查是否为参考文献部分
-                    if section.section_type == 'references' or section.title.lower() in ['references', '参考文献', 'bibliography', '文献']:
-                        references_sections.append((current_path, section))
-
-                    # 递归检查子章节
-                    if section.subsections:
-                        collect_references(section.subsections, current_path)
-
-            # 收集参考文献
-            collect_references(paper.sections)
-
-            # 7. 将处理后的结构化论文转换为Markdown
-            markdown_content = self.paper_extractor.generate_markdown(paper)
-
-            # 8. 返回处理后的内容
-            self.chatbot[-1] = ["处理完成", f"成功处理 {success_count}/{total_fragments} 个内容片段，参考文献部分未处理"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            return markdown_content
-
-        except Exception as e:
-            self.chatbot.append(["结构化处理失败", f"错误: {str(e)}，将尝试作为普通文件处理"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return (yield from self._process_regular_file(file_path))
-
-    def _process_regular_file(self, file_path: str) -> Generator:
-        """使用原有方式处理普通文件"""
-        # 原有的文件处理逻辑
-        self.chatbot[-1] = ["正在读取文件", f"文件路径: {file_path}"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        content = extract_text(file_path)
-        if not content or not content.strip():
-            self.chatbot.append(["处理失败", "文件内容为空或无法提取内容"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return None
-
-        # 2. 分割文本
-        self.chatbot[-1] = ["正在分析文件", "将文件内容分割为适当大小的片段"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 使用增强的分割函数
-        fragments = self._breakdown_section_content(content)
-
-        # 3. 创建文本片段对象
-        text_fragments = []
-        for i, frag in enumerate(fragments):
-            if frag.strip():
-                text_fragments.append(TextFragment(
-                    content=frag,
-                    fragment_index=i,
-                    total_fragments=len(fragments)
-                ))
-
-        # 4. 处理所有片段
-        self.chatbot[-1] = ["开始处理文本", f"共 {len(text_fragments)} 个片段"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 批量处理片段
-        batch_size = 8  # 每批处理的片段数
-        for i in range(0, len(text_fragments), batch_size):
-            batch = text_fragments[i:i + batch_size]
-
-            inputs_array, inputs_show_user_array, history_array = self._create_batch_inputs(batch)
-
-            # 使用系统提示
-            instruction = self.plugin_kwargs.get("advanced_arg", "请润色以下文本")
-            sys_prompt_array = [f"你是一个专业的文本处理助手。请按照用户的要求：'{instruction}'处理文本。"] * len(batch)
-
-            # 调用LLM处理
-            response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                inputs_array=inputs_array,
-                inputs_show_user_array=inputs_show_user_array,
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history_array=history_array,
-                sys_prompt_array=sys_prompt_array,
-            )
-
-            # 处理响应
-            for j, frag in enumerate(batch):
-                try:
-                    llm_response = response_collection[j * 2 + 1]
-                    processed_text = self._extract_decision(llm_response)
-
-                    if processed_text and processed_text.strip():
-                        self.processed_results.append({
-                            'index': frag.fragment_index,
-                            'content': processed_text
-                        })
-                    else:
-                        self.failed_fragments.append(frag)
-                        self.processed_results.append({
-                            'index': frag.fragment_index,
-                            'content': frag.content  # 如果处理失败，使用原始内容
-                        })
-                except Exception as e:
-                    self.failed_fragments.append(frag)
-                    self.processed_results.append({
-                        'index': frag.fragment_index,
-                        'content': frag.content  # 如果处理失败，使用原始内容
-                    })
-
-        # 5. 按原始顺序合并结果
-        self.processed_results.sort(key=lambda x: x['index'])
-        final_content = "\n".join([item['content'] for item in self.processed_results])
-
-        # 6. 更新UI
-        success_count = len(text_fragments) - len(self.failed_fragments)
-        self.chatbot[-1] = ["处理完成", f"成功处理 {success_count}/{len(text_fragments)} 个片段"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        return final_content
-
-    def save_results(self, content: str, original_file_path: str) -> List[str]:
-        """保存处理结果为多种格式"""
-        if not content:
-            return []
-
-        timestamp = time.strftime("%Y%m%d_%H%M%S")
-        original_filename = os.path.basename(original_file_path)
-        filename_without_ext = os.path.splitext(original_filename)[0]
-        base_filename = f"{filename_without_ext}_processed_{timestamp}"
-
-        result_files = []
-
-        # 获取用户指定的处理类型
-        processing_type = self.plugin_kwargs.get("advanced_arg", "文本处理")
-
-        # 1. 保存为TXT
-        try:
-            txt_formatter = TxtFormatter()
-            txt_content = txt_formatter.create_document(content)
-            txt_file = write_history_to_file(
-                history=[txt_content],
-                file_basename=f"{base_filename}.txt"
-            )
-            result_files.append(txt_file)
-        except Exception as e:
-            self.chatbot.append(["警告", f"TXT格式保存失败: {str(e)}"])
-
-        # 2. 保存为Markdown
-        try:
-            md_formatter = MarkdownFormatter()
-            md_content = md_formatter.create_document(content, processing_type)
-            md_file = write_history_to_file(
-                history=[md_content],
-                file_basename=f"{base_filename}.md"
-            )
-            result_files.append(md_file)
-        except Exception as e:
-            self.chatbot.append(["警告", f"Markdown格式保存失败: {str(e)}"])
-
-        # 3. 保存为HTML
-        try:
-            html_formatter = HtmlFormatter(processing_type=processing_type)
-            html_content = html_formatter.create_document(content)
-            html_file = write_history_to_file(
-                history=[html_content],
-                file_basename=f"{base_filename}.html"
-            )
-            result_files.append(html_file)
-        except Exception as e:
-            self.chatbot.append(["警告", f"HTML格式保存失败: {str(e)}"])
-
-        # 4. 保存为Word
-        try:
-            word_formatter = WordFormatter()
-            doc = word_formatter.create_document(content, processing_type)
-
-            # 获取保存路径
-            from toolbox import get_log_folder
-            word_path = os.path.join(get_log_folder(), f"{base_filename}.docx")
-            doc.save(word_path)
-
-            # 5. 保存为PDF（通过Word转换）
-            try:
-                from crazy_functions.paper_fns.file2file_doc.word2pdf import WordToPdfConverter
-                pdf_path = WordToPdfConverter.convert_to_pdf(word_path)
-                result_files.append(pdf_path)
-            except Exception as e:
-                self.chatbot.append(["警告", f"PDF格式保存失败: {str(e)}"])
-
-        except Exception as e:
-            self.chatbot.append(["警告", f"Word格式保存失败: {str(e)}"])
-
-        # 添加到下载区
-        for file in result_files:
-            promote_file_to_downloadzone(file, chatbot=self.chatbot)
-
-        return result_files
-
-    def _breakdown_section_content(self, content: str) -> List[str]:
-        """对文本内容进行分割与合并
-
-        主要按段落进行组织，只合并较小的段落以减少片段数量
-        保留原始段落结构，不对长段落进行强制分割
-        针对中英文设置不同的阈值，因为字符密度不同
-        """
-        # 先按段落分割文本
-        paragraphs = content.split('\n\n')
-
-        # 检测语言类型
-        chinese_char_count = sum(1 for char in content if '\u4e00' <= char <= '\u9fff')
-        is_chinese_text = chinese_char_count / max(1, len(content)) > 0.3
-
-        # 根据语言类型设置不同的阈值（只用于合并小段落）
-        if is_chinese_text:
-            # 中文文本：一个汉字就是一个字符，信息密度高
-            min_chunk_size = 300  # 段落合并的最小阈值
-            target_size = 800  # 理想的段落大小
-        else:
-            # 英文文本：一个单词由多个字符组成，信息密度低
-            min_chunk_size = 600  # 段落合并的最小阈值
-            target_size = 1600  # 理想的段落大小
-
-        # 1. 只合并小段落，不对长段落进行分割
-        result_fragments = []
-        current_chunk = []
-        current_length = 0
-
-        for para in paragraphs:
-            # 如果段落太小且不会超过目标大小，则合并
-            if len(para) < min_chunk_size and current_length + len(para) <= target_size:
-                current_chunk.append(para)
-                current_length += len(para)
-            # 否则，创建新段落
-            else:
-                # 如果当前块非空且与当前段落无关，先保存它
-                if current_chunk and current_length > 0:
-                    result_fragments.append('\n\n'.join(current_chunk))
-
-                # 当前段落作为新块
-                current_chunk = [para]
-                current_length = len(para)
-
-            # 如果当前块大小已接近目标大小，保存并开始新块
-            if current_length >= target_size:
-                result_fragments.append('\n\n'.join(current_chunk))
-                current_chunk = []
-                current_length = 0
-
-        # 保存最后一个块
-        if current_chunk:
-            result_fragments.append('\n\n'.join(current_chunk))
-
-        # 2. 处理可能过大的片段（确保不超过token限制）
-        final_fragments = []
-        max_token = self._get_token_limit()
-
-        for fragment in result_fragments:
-            # 检查fragment是否可能超出token限制
-            # 根据语言类型调整token估算
-            if is_chinese_text:
-                estimated_tokens = len(fragment) / 1.5  # 中文每个token约1-2个字符
-            else:
-                estimated_tokens = len(fragment) / 4  # 英文每个token约4个字符
-
-            if estimated_tokens > max_token:
-                # 即使可能超出限制，也尽量保持段落的完整性
-                # 使用breakdown_text但设置更大的限制来减少分割
-                larger_limit = max_token * 0.95  # 使用95%的限制
-                sub_fragments = breakdown_text_to_satisfy_token_limit(
-                    txt=fragment,
-                    limit=larger_limit,
-                    llm_model=self.llm_kwargs['llm_model']
-                )
-                final_fragments.extend(sub_fragments)
-            else:
-                final_fragments.append(fragment)
-
-        return final_fragments
-
-
-@CatchException
-def 自定义智能文档处理(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-              history: List, system_prompt: str, user_request: str):
-    """主函数 - 文件到文件处理"""
-    # 初始化
-    processor = DocumentProcessor(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    chatbot.append(["函数插件功能", "文件内容处理：将文档内容按照指定要求处理后输出为多种格式"])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    # 验证输入路径
-    if not os.path.exists(txt):
-        report_exception(chatbot, history, a=f"解析路径: {txt}", b=f"找不到路径或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 验证路径安全性
-    user_name = chatbot.get_user()
-    validate_path_safety(txt, user_name)
-
-    # 获取文件列表
-    if os.path.isfile(txt):
-        # 单个文件处理
-        file_paths = [txt]
-    else:
-        # 目录处理 - 类似批量文件询问插件
-        project_folder = txt
-        extract_folder = next((d for d in glob.glob(f'{project_folder}/*')
-                           if os.path.isdir(d) and d.endswith('.extract')), project_folder)
-
-        # 排除压缩文件
-        exclude_patterns = r'/[^/]+\.(zip|rar|7z|tar|gz)$'
-        file_paths = [f for f in glob.glob(f'{extract_folder}/**', recursive=True)
-                     if os.path.isfile(f) and not re.search(exclude_patterns, f)]
-
-        # 过滤支持的文件格式
-        file_paths = [f for f in file_paths if any(f.lower().endswith(ext) for ext in
-                    list(processor.paper_extractor.SUPPORTED_EXTENSIONS) + ['.json', '.csv', '.xlsx', '.xls'])]
-
-    if not file_paths:
-        report_exception(chatbot, history, a=f"解析路径: {txt}", b="未找到支持的文件类型")
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 处理文件
-    if len(file_paths) > 1:
-        chatbot.append(["发现多个文件", f"共找到 {len(file_paths)} 个文件，将处理第一个文件"])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 只处理第一个文件
-    file_to_process = file_paths[0]
-    processed_content = yield from processor.process_file(file_to_process)
-
-    if processed_content:
-        # 保存结果
-        result_files = processor.save_results(processed_content, file_to_process)
-
-        if result_files:
-            chatbot.append(["处理完成", f"已生成 {len(result_files)} 个结果文件"])
-        else:
-            chatbot.append(["处理完成", "但未能保存任何结果文件"])
-    else:
-        chatbot.append(["处理失败", "未能生成有效的处理结果"])
-
-    yield from update_ui(chatbot=chatbot, history=history)
--- a/crazy_functions/Internet_GPT.py
+++ b/crazy_functions/Internet_GPT.py
@@ -7,7 +7,7 @@ from bs4 import BeautifulSoup
 from functools import lru_cache
 from itertools import zip_longest
 from check_proxy import check_proxy
-from toolbox import CatchException, update_ui, get_conf, update_ui_latest_msg
+from toolbox import CatchException, update_ui, get_conf
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
 from request_llms.bridge_all import model_info
 from request_llms.bridge_all import predict_no_ui_long_connection
@@ -49,7 +49,7 @@ def search_optimizer(
    mutable = ["", time.time(), ""]
    llm_kwargs["temperature"] = 0.8
    try:
-        query_json = predict_no_ui_long_connection(
+        querys_json = predict_no_ui_long_connection(
            inputs=query,
            llm_kwargs=llm_kwargs,
            history=[],
@@ -57,31 +57,31 @@ def search_optimizer(
            observe_window=mutable,
        )
    except Exception:
-        query_json = "null"
+        querys_json = "1234"
    #* 尝试解码优化后的搜索结果
-    query_json = re.sub(r"```json|```", "", query_json)
+    querys_json = re.sub(r"```json|```", "", querys_json)
    try:
-        queries = json.loads(query_json)
+        querys = json.loads(querys_json)
    except Exception:
        #* 如果解码失败,降低温度再试一次
        try:
            llm_kwargs["temperature"] = 0.4
-            query_json = predict_no_ui_long_connection(
+            querys_json = predict_no_ui_long_connection(
                inputs=query,
                llm_kwargs=llm_kwargs,
                history=[],
                sys_prompt=sys_prompt,
                observe_window=mutable,
            )
-            query_json = re.sub(r"```json|```", "", query_json)
-            queries = json.loads(query_json)
+            querys_json = re.sub(r"```json|```", "", querys_json)
+            querys = json.loads(querys_json)
        except Exception:
            #* 如果再次失败，直接返回原始问题
-            queries = [query]
+            querys = [query]
    links = []
    success = 0
    Exceptions = ""
-    for q in queries:
+    for q in querys:
        try:
            link = searxng_request(q, proxies, categories, searxng_url, engines=engines)
            if len(link) > 0:
@@ -115,8 +115,7 @@ def get_auth_ip():

 def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
    if searxng_url is None:
-        urls = get_conf("SEARXNG_URLS")
-        url = random.choice(urls)
+        url = get_conf("SEARXNG_URL")
    else:
        url = searxng_url

@@ -175,17 +174,10 @@ def scrape_text(url, proxies) -> str:
    Returns:
        str: The scraped text
    """
-    from loguru import logger
    headers = {
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
        'Content-Type': 'text/plain',
    }
-
-    # 首先采用Jina进行文本提取
-    if get_conf("JINA_API_KEY"):
-        try: return jina_scrape_text(url)
-        except: logger.debug("Jina API 请求失败，回到旧方法")
-
    try:
        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
        if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
@@ -201,56 +193,6 @@ def scrape_text(url, proxies) -> str:
    return text


-def jina_scrape_text(url) -> str:
-    "jina_39727421c8fa4e4fa9bd698e5211feaaDyGeVFESNrRaepWiLT0wmHYJSh-d"
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
-        'Content-Type': 'text/plain',
-        "X-Retain-Images": "none",
-        "Authorization": f'Bearer {get_conf("JINA_API_KEY")}'
-    }
-    response = requests.get("https://r.jina.ai/" + url, headers=headers, proxies=None, timeout=8)
-    if response.status_code != 200:
-        raise ValueError("Jina API 请求失败，开始尝试旧方法！" + response.text)
-    if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
-    result = response.text
-    result = result.replace("\\[", "[").replace("\\]", "]").replace("\\(", "(").replace("\\)", ")")
-    return response.text
-
-
-def internet_search_with_analysis_prompt(prompt, analysis_prompt, llm_kwargs, chatbot):
-    from toolbox import get_conf
-    proxies = get_conf('proxies')
-    categories = 'general'
-    searxng_url = None  # 使用默认的searxng_url
-    engines = None  # 使用默认的搜索引擎
-    yield from update_ui_latest_msg(lastmsg=f"检索中: {prompt} ...", chatbot=chatbot, history=[], delay=1)
-    urls = searxng_request(prompt, proxies, categories, searxng_url, engines=engines)
-    yield from update_ui_latest_msg(lastmsg=f"依次访问搜索到的网站 ...", chatbot=chatbot, history=[], delay=1)
-    if len(urls) == 0:
-        return None
-    max_search_result = 5   # 最多收纳多少个网页的结果
-    history = []
-    for index, url in enumerate(urls[:max_search_result]):
-        yield from update_ui_latest_msg(lastmsg=f"依次访问搜索到的网站: {url['link']} ...", chatbot=chatbot, history=[], delay=1)
-        res = scrape_text(url['link'], proxies)
-        prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
-        history.extend([prefix, res])
-    i_say = f"从以上搜索结果中抽取信息，然后回答问题：{prompt} {analysis_prompt}"
-    i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
-        inputs=i_say,
-        history=history,
-        max_token_limit=8192
-    )
-    gpt_say = predict_no_ui_long_connection(
-        inputs=i_say,
-        llm_kwargs=llm_kwargs,
-        history=history,
-        sys_prompt="请从搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。",
-        console_silence=False,
-    )
-    return gpt_say
-
@CatchException
 def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
    optimizer_history = history[:-8]
@@ -271,52 +213,23 @@ def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
        urls = search_optimizer(txt, proxies, optimizer_history, llm_kwargs, optimizer, categories, searxng_url, engines)
    history = []
    if len(urls) == 0:
-        chatbot.append((f"结论：{txt}", "[Local Message] 受到限制，无法从searxng获取信息！请尝试更换搜索引擎。"))
+        chatbot.append((f"结论：{txt}",
+                        "[Local Message] 受到限制，无法从searxng获取信息！请尝试更换搜索引擎。"))
        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
        return

    # ------------- < 第2步：依次访问网页 > -------------
-    from concurrent.futures import ThreadPoolExecutor
-    from textwrap import dedent
    max_search_result = 5   # 最多收纳多少个网页的结果
    if optimizer == "开启(增强)":
        max_search_result = 8
-    template = dedent("""
-        <details>
-        <summary>{TITLE}</summary>
-        <div class="search_result">{URL}</div>
-        <div class="search_result">{CONTENT}</div>
-        </details>
-    """)
-
-    buffer = ""
-
-    # 创建线程池
-    with ThreadPoolExecutor(max_workers=5) as executor:
-        # 提交任务到线程池
-        futures = []
+    chatbot.append(["联网检索中 ...", None])
    for index, url in enumerate(urls[:max_search_result]):
-            future = executor.submit(scrape_text, url['link'], proxies)
-            futures.append((index, future, url))
-
-        # 处理完成的任务
-        for index, future, url in futures:
-            # 开始
-            prefix = f"正在加载 第{index+1}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
-            string_structure = template.format(TITLE=prefix, URL=url['link'], CONTENT="正在加载，请稍后 ......")
-            yield from update_ui_latest_msg(lastmsg=(buffer + string_structure), chatbot=chatbot, history=history, delay=0.1)  # 刷新界面
-
-            # 获取结果
-            res = future.result()
-
-            # 显示结果
-            prefix = f"第{index+1}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
-            string_structure = template.format(TITLE=prefix, URL=url['link'], CONTENT=res[:1000] + "......")
-            buffer += string_structure
-
-            # 更新历史
+        res = scrape_text(url['link'], proxies)
+        prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
        history.extend([prefix, res])
-            yield from update_ui_latest_msg(lastmsg=buffer, chatbot=chatbot, history=history, delay=0.1)  # 刷新界面
+        res_squeeze = res.replace('\n', '...')
+        chatbot[-1] = [prefix + "\n\n" + res_squeeze[:500] + "......", None]
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

    # ------------- < 第3步：ChatGPT综合 > -------------
    if (optimizer != "开启(增强)"):
--- a/crazy_functions/Internet_GPT_Wrap.py
+++ b/crazy_functions/Internet_GPT_Wrap.py
@@ -1,4 +1,4 @@
-import random
+
 from toolbox import get_conf
 from crazy_functions.Internet_GPT import 连接网络回答问题
 from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
@@ -20,9 +20,6 @@ class NetworkGPT_Wrap(GptAcademicPluginTemplate):
        第三个参数，名称`allow_cache`，参数`type`声明这是一个下拉菜单，下拉菜单上方显示`title`+`description`，下拉菜单的选项为`options`，`default_value`为下拉菜单默认值；

        """
-        urls = get_conf("SEARXNG_URLS")
-        url = random.choice(urls)
-
        gui_definition = {
            "main_input":
                ArgProperty(title="输入问题", description="待通过互联网检索的问题，会自动读取输入框内容", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
@@ -33,17 +30,16 @@ class NetworkGPT_Wrap(GptAcademicPluginTemplate):
            "optimizer":
                ArgProperty(title="搜索优化", options=["关闭", "开启", "开启(增强)"], default_value="关闭", description="是否使用搜索增强。注意这可能会消耗较多token", type="dropdown").model_dump_json(),
            "searxng_url":
-                ArgProperty(title="Searxng服务地址", description="输入Searxng的地址", default_value=url, type="string").model_dump_json(), # 主输入，自动从输入框同步
+                ArgProperty(title="Searxng服务地址", description="输入Searxng的地址", default_value=get_conf("SEARXNG_URL"), type="string").model_dump_json(), # 主输入，自动从输入框同步

        }
        return gui_definition

-    def execute(txt, llm_kwargs, plugin_kwargs:dict, chatbot, history, system_prompt, user_request):
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
        """
        执行插件
        """
-        if plugin_kwargs.get("categories", None) == "网页": plugin_kwargs["categories"] = "general"
-        elif plugin_kwargs.get("categories", None) == "学术论文": plugin_kwargs["categories"] = "science"
-        else: plugin_kwargs["categories"] = "general"
+        if plugin_kwargs["categories"] == "网页": plugin_kwargs["categories"] = "general"
+        if plugin_kwargs["categories"] == "学术论文": plugin_kwargs["categories"] = "science"
        yield from 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)

--- a/crazy_functions/Latex_Function.py
+++ b/crazy_functions/Latex_Function.py
@@ -1,9 +1,9 @@
 from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone, check_repeat_upload, map_file_to_sha256
-from toolbox import CatchException, report_exception, update_ui_latest_msg, zip_result, gen_time_str
+from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
 from functools import partial
 from loguru import logger

-import glob, os, requests, time, json, tarfile, threading
+import glob, os, requests, time, json, tarfile

 pj = os.path.join
 ARXIV_CACHE_DIR = get_conf("ARXIV_CACHE_DIR")
@@ -41,7 +41,7 @@ def switch_prompt(pfg, mode, more_requirement):
    return inputs_array, sys_prompt_array


-def descend_to_extracted_folder_if_exist(project_folder):
+def desend_to_extracted_folder_if_exist(project_folder):
    """
    Descend into the extracted folder if it exists, otherwise return the original folder.

@@ -130,7 +130,7 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):

    if not txt.startswith('https://arxiv.org/abs/'):
        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
-        yield from update_ui_latest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
+        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
        return msg, None
    # <-------------- set format ------------->
    arxiv_id = url_.split('/abs/')[-1]
@@ -138,43 +138,25 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
    if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id

-    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
+    url_tar = url_.replace('/abs/', '/e-print/')
    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
-    dst = pj(translation_dir, arxiv_id + '.tar')
+    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
    os.makedirs(translation_dir, exist_ok=True)
-    # <-------------- download arxiv source file ------------->

-    def fix_url_and_download():
-        # for url_tar in [url_.replace('/abs/', '/e-print/'), url_.replace('/abs/', '/src/')]:
-        for url_tar in [url_.replace('/abs/', '/src/'), url_.replace('/abs/', '/e-print/')]:
+    # <-------------- download arxiv source file ------------->
+    dst = pj(translation_dir, arxiv_id + '.tar')
+    if os.path.exists(dst):
+        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
+    else:
+        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
        proxies = get_conf('proxies')
        r = requests.get(url_tar, proxies=proxies)
-            if r.status_code == 200:
        with open(dst, 'wb+') as f:
            f.write(r.content)
-                return True
-        return False
-
-    if os.path.exists(dst) and allow_cache:
-        yield from update_ui_latest_msg(f"调用缓存 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
-        success = True
-    else:
-        yield from update_ui_latest_msg(f"开始下载 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
-        success = fix_url_and_download()
-        yield from update_ui_latest_msg(f"下载完成 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
-
-
-    if not success:
-        yield from update_ui_latest_msg(f"下载失败 {arxiv_id}", chatbot=chatbot, history=history)
-        raise tarfile.ReadError(f"论文下载失败 {arxiv_id}")
-
    # <-------------- extract file ------------->
+    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
    from toolbox import extract_archive
-    try:
    extract_archive(file_path=dst, dest_dir=extract_dst)
-    except tarfile.ReadError:
-        os.remove(dst)
-        raise tarfile.ReadError(f"论文下载失败")
    return extract_dst, arxiv_id


@@ -288,7 +270,7 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
        return

    # <-------------- if is a zip/tar file ------------->
-    project_folder = descend_to_extracted_folder_if_exist(project_folder)
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)

    # <-------------- move latex project away from temp folder ------------->
    from shared_utils.fastapi_server import validate_path_safety
@@ -338,17 +320,11 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
    # <-------------- more requirements ------------->
    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
    more_req = plugin_kwargs.get("advanced_arg", "")
-
-    no_cache = ("--no-cache" in more_req)
-    if no_cache: more_req = more_req.replace("--no-cache", "").strip()
-
-    allow_gptac_cloud_io = ("--allow-cloudio" in more_req)  # 从云端下载翻译结果，以及上传翻译结果到云端
-    if allow_gptac_cloud_io: more_req = more_req.replace("--allow-cloudio", "").strip()
-
+    no_cache = more_req.startswith("--no-cache")
+    if no_cache: more_req.lstrip("--no-cache")
    allow_cache = not no_cache
    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)

-
    # <-------------- check deps ------------->
    try:
        import glob, os, time, subprocess
@@ -365,7 +341,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
    try:
        txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
    except tarfile.ReadError as e:
-        yield from update_ui_latest_msg(
+        yield from update_ui_lastest_msg(
            "无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
            chatbot=chatbot, history=history)
        return
@@ -375,20 +351,6 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
        return

-    # #################################################################
-    if allow_gptac_cloud_io and arxiv_id:
-        # 访问 GPTAC学术云，查询云端是否存在该论文的翻译版本
-        from crazy_functions.latex_fns.latex_actions import check_gptac_cloud
-        success, downloaded = check_gptac_cloud(arxiv_id, chatbot)
-        if success:
-            chatbot.append([
-                f"检测到GPTAC云端存在翻译版本, 如果不满意翻译结果, 请禁用云端分享, 然后重新执行。", 
-                None
-            ])
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-    #################################################################
-
    if os.path.exists(txt):
        project_folder = txt
    else:
@@ -404,7 +366,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
        return

    # <-------------- if is a zip/tar file ------------->
-    project_folder = descend_to_extracted_folder_if_exist(project_folder)
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)

    # <-------------- move latex project away from temp folder ------------->
    from shared_utils.fastapi_server import validate_path_safety
@@ -426,21 +388,14 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
    # <-------------- zip PDF ------------->
    zip_res = zip_result(project_folder)
    if success:
-        if allow_gptac_cloud_io and arxiv_id:
-            # 如果用户允许，我们将翻译好的arxiv论文PDF上传到GPTAC学术云
-            from crazy_functions.latex_fns.latex_actions import upload_to_gptac_cloud_if_user_allow
-            threading.Thread(target=upload_to_gptac_cloud_if_user_allow, 
-                args=(chatbot, arxiv_id), daemon=True).start()
-
        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history)
+        yield from update_ui(chatbot=chatbot, history=history);
        time.sleep(1)  # 刷新界面
        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
    else:
        chatbot.append((f"失败了",
                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
-        yield from update_ui(chatbot=chatbot, history=history)
+        yield from update_ui(chatbot=chatbot, history=history);
        time.sleep(1)  # 刷新界面
        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)

@@ -518,7 +473,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
    # repeat, project_folder = check_repeat_upload(file_manifest[0], hash_tag)

    # if repeat:
-    #     yield from update_ui_latest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
+    #     yield from update_ui_lastest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
    #     try:
    #         translate_pdf = [f for f in glob.glob(f'{project_folder}/**/merge_translate_zh.pdf', recursive=True)][0]
    #         promote_file_to_downloadzone(translate_pdf, rename_file=None, chatbot=chatbot)
@@ -531,7 +486,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
    #         report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现重复上传，但是无法找到相关文件")
    #         yield from update_ui(chatbot=chatbot, history=history)
    # else:
-    #     yield from update_ui_latest_msg(f"未发现重复上传", chatbot=chatbot, history=history)
+    #     yield from update_ui_lastest_msg(f"未发现重复上传", chatbot=chatbot, history=history)

    # <-------------- convert pdf into tex ------------->
    chatbot.append([f"解析项目: {txt}", "正在将PDF转换为tex项目，请耐心等待..."])
@@ -543,7 +498,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
        return False

    # <-------------- translate latex file into Chinese ------------->
-    yield from update_ui_latest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
+    yield from update_ui_lastest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
    if len(file_manifest) == 0:
        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
@@ -551,7 +506,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
        return

    # <-------------- if is a zip/tar file ------------->
-    project_folder = descend_to_extracted_folder_if_exist(project_folder)
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)

    # <-------------- move latex project away from temp folder ------------->
    from shared_utils.fastapi_server import validate_path_safety
@@ -559,7 +514,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
    project_folder = move_project(project_folder)

    # <-------------- set a hash tag for repeat-checking ------------->
-    with open(pj(project_folder, hash_tag + '.tag'), 'w', encoding='utf8') as f:
+    with open(pj(project_folder, hash_tag + '.tag'), 'w') as f:
        f.write(hash_tag)
        f.close()

@@ -571,7 +526,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
                                    switch_prompt=_switch_prompt_)

    # <-------------- compile PDF ------------->
-    yield from update_ui_latest_msg("正在将翻译好的项目tex项目编译为PDF...", chatbot=chatbot, history=history)
+    yield from update_ui_lastest_msg("正在将翻译好的项目tex项目编译为PDF...", chatbot=chatbot, history=history)
    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
                                main_file_modified='merge_translate_zh', mode='translate_zh',
                                work_folder_original=project_folder, work_folder_modified=project_folder,
--- a/crazy_functions/Latex_Function_Wrap.py
+++ b/crazy_functions/Latex_Function_Wrap.py
@@ -30,8 +30,6 @@ class Arxiv_Localize(GptAcademicPluginTemplate):
                            default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
            "allow_cache":
                ArgProperty(title="是否允许从缓存中调取结果", options=["允许缓存", "从头执行"], default_value="允许缓存", description="无", type="dropdown").model_dump_json(),
-            "allow_cloudio":
-                ArgProperty(title="是否允许从GPTAC学术云下载(或者上传)翻译结果(仅针对Arxiv论文)", options=["允许", "禁止"], default_value="禁止", description="共享文献，互助互利", type="dropdown").model_dump_json(),
        }
        return gui_definition

@@ -40,14 +38,9 @@ class Arxiv_Localize(GptAcademicPluginTemplate):
        执行插件
        """
        allow_cache = plugin_kwargs["allow_cache"]
-        allow_cloudio = plugin_kwargs["allow_cloudio"]
        advanced_arg = plugin_kwargs["advanced_arg"]

        if allow_cache == "从头执行": plugin_kwargs["advanced_arg"] = "--no-cache " + plugin_kwargs["advanced_arg"]
-
-        # 从云端下载翻译结果，以及上传翻译结果到云端；人人为我，我为人人。
-        if allow_cloudio == "允许": plugin_kwargs["advanced_arg"] = "--allow-cloudio " + plugin_kwargs["advanced_arg"]
-
        yield from Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)


--- a/crazy_functions/Latex_Project_Polish.py
+++ b/crazy_functions/Latex_Project_Polish.py
--- a/crazy_functions/Latex_Project_Translate_Legacy.py
+++ b/crazy_functions/Latex_Project_Translate_Legacy.py
--- a/crazy_functions/Markdown_Translate.py
+++ b/crazy_functions/Markdown_Translate.py
@@ -65,7 +65,7 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
            pfg.file_contents.append(file_content)

    #  <-------- 拆分过长的Markdown文件 ---------->
-    pfg.run_file_split(max_token_limit=1024)
+    pfg.run_file_split(max_token_limit=2048)
    n_split = len(pfg.sp_file_contents)

    #  <-------- 多线程翻译开始 ---------->
--- a/crazy_functions/PDF_Translate.py
+++ b/crazy_functions/PDF_Translate.py
@@ -1,5 +1,5 @@
 from toolbox import CatchException, check_packages, get_conf
-from toolbox import update_ui, update_ui_latest_msg, disable_auto_promotion
+from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion
 from toolbox import trimmed_format_exc_markdown
 from crazy_functions.crazy_utils import get_files_from_everything
 from crazy_functions.pdf_fns.parse_pdf import get_avail_grobid_url
@@ -47,7 +47,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
                yield from 解析PDF_基于DOC2X(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request)
                return
            except:
-                chatbot.append([None, f"DOC2X服务不可用，请检查报错详细。{trimmed_format_exc_markdown()}"])
+                chatbot.append([None, f"DOC2X服务不可用，现在将执行效果稍差的旧版代码。{trimmed_format_exc_markdown()}"])
                yield from update_ui(chatbot=chatbot, history=history)

    if method == "GROBID":
@@ -57,9 +57,9 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
            yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
            return

-    if method == "Classic":
+    if method == "ClASSIC":
        # ------- 第三种方法，早期代码，效果不理想 -------
-        yield from update_ui_latest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
+        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
        yield from 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
        return

@@ -77,7 +77,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
        if grobid_url is not None:
            yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
            return
-        yield from update_ui_latest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
+        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
        yield from 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
        return

--- a/crazy_functions/PDF_Translate_Wrap.py
+++ b/crazy_functions/PDF_Translate_Wrap.py
@@ -19,7 +19,7 @@ class PDF_Tran(GptAcademicPluginTemplate):
            "additional_prompt":
                ArgProperty(title="额外提示词", description="例如：对专有名词、翻译语气等方面的要求", default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
            "pdf_parse_method":
-                ArgProperty(title="PDF解析方法", options=["DOC2X", "GROBID", "Classic"], description="无", default_value="GROBID", type="dropdown").model_dump_json(),
+                ArgProperty(title="PDF解析方法", options=["DOC2X", "GROBID", "ClASSIC"], description="无", default_value="GROBID", type="dropdown").model_dump_json(),
        }
        return gui_definition

--- a/crazy_functions/Paper_Reading.py
+++ b/crazy_functions/Paper_Reading.py
@@ -1,360 +0,0 @@
-import os
-import time
-import glob
-from pathlib import Path
-from datetime import datetime
-from dataclasses import dataclass
-from typing import Dict, List, Generator, Tuple
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import update_ui, promote_file_to_downloadzone, write_history_to_file, CatchException, report_exception
-from shared_utils.fastapi_server import validate_path_safety
-from crazy_functions.paper_fns.paper_download import extract_paper_id, extract_paper_ids, get_arxiv_paper, format_arxiv_id
-
-
-
-@dataclass
-class PaperQuestion:
-    """论文分析问题类"""
-    id: str  # 问题ID
-    question: str  # 问题内容
-    importance: int  # 重要性 (1-5，5最高)
-    description: str  # 问题描述
-
-
-class PaperAnalyzer:
-    """论文快速分析器"""
-
-    def __init__(self, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List, history: List, system_prompt: str):
-        """初始化分析器"""
-        self.llm_kwargs = llm_kwargs
-        self.plugin_kwargs = plugin_kwargs
-        self.chatbot = chatbot
-        self.history = history
-        self.system_prompt = system_prompt
-        self.paper_content = ""
-        self.results = {}
-
-        # 定义论文分析问题库（已合并为4个核心问题）
-        self.questions = [
-            PaperQuestion(
-                id="research_and_methods",
-                question="这篇论文的主要研究问题、目标和方法是什么？请分析：1)论文的核心研究问题和研究动机；2)论文提出的关键方法、模型或理论框架；3)这些方法如何解决研究问题。",
-                importance=5,
-                description="研究问题与方法"
-            ),
-            PaperQuestion(
-                id="findings_and_innovation",
-                question="论文的主要发现、结论及创新点是什么？请分析：1)论文的核心结果与主要发现；2)作者得出的关键结论；3)研究的创新点与对领域的贡献；4)与已有工作的区别。",
-                importance=4,
-                description="研究发现与创新"
-            ),
-            PaperQuestion(
-                id="methodology_and_data",
-                question="论文使用了什么研究方法和数据？请详细分析：1)研究设计与实验设置；2)数据收集方法与数据集特点；3)分析技术与评估方法；4)方法学上的合理性。",
-                importance=3,
-                description="研究方法与数据"
-            ),
-            PaperQuestion(
-                id="limitations_and_impact",
-                question="论文的局限性、未来方向及潜在影响是什么？请分析：1)研究的不足与限制因素；2)作者提出的未来研究方向；3)该研究对学术界和行业可能产生的影响；4)研究结果的适用范围与推广价值。",
-                importance=2,
-                description="局限性与影响"
-            ),
-        ]
-
-        # 按重要性排序
-        self.questions.sort(key=lambda q: q.importance, reverse=True)
-
-    def _load_paper(self, paper_path: str) -> Generator:
-        from crazy_functions.doc_fns.text_content_loader import TextContentLoader
-        """加载论文内容"""
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 使用TextContentLoader读取文件
-        loader = TextContentLoader(self.chatbot, self.history)
-
-        yield from loader.execute_single_file(paper_path)
-
-        # 获取加载的内容
-        if len(self.history) >= 2 and self.history[-2]:
-            self.paper_content = self.history[-2]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return True
-        else:
-            self.chatbot.append(["错误", "无法读取论文内容，请检查文件是否有效"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return False
-
-    def _analyze_question(self, question: PaperQuestion) -> Generator:
-        """分析单个问题 - 直接显示问题和答案"""
-        try:
-            # 创建分析提示
-            prompt = f"请基于以下论文内容回答问题：\n\n{self.paper_content}\n\n问题：{question.question}"
-
-            # 使用单线程版本的请求函数
-            response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=prompt,
-                inputs_show_user=question.question,  # 显示问题本身
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history=[],  # 空历史，确保每个问题独立分析
-                sys_prompt="你是一个专业的科研论文分析助手，需要仔细阅读论文内容并回答问题。请保持客观、准确，并基于论文内容提供深入分析。"
-            )
-
-            if response:
-                self.results[question.id] = response
-                return True
-            return False
-
-        except Exception as e:
-            self.chatbot.append(["错误", f"分析问题时出错: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return False
-
-    def _generate_summary(self) -> Generator:
-        """生成最终总结报告"""
-        self.chatbot.append(["生成报告", "正在整合分析结果，生成最终报告..."])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        summary_prompt = "请基于以下对论文的各个方面的分析，生成一份全面的论文解读报告。报告应该简明扼要地呈现论文的关键内容，并保持逻辑连贯性。"
-
-        for q in self.questions:
-            if q.id in self.results:
-                summary_prompt += f"\n\n关于{q.description}的分析:\n{self.results[q.id]}"
-
-        try:
-            # 使用单线程版本的请求函数，可以在前端实时显示生成结果
-            response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=summary_prompt,
-                inputs_show_user="生成论文解读报告",
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history=[],
-                sys_prompt="你是一个科研论文解读专家，请将多个方面的分析整合为一份完整、连贯、有条理的报告。报告应当重点突出，层次分明，并且保持学术性和客观性。"
-            )
-
-            if response:
-                return response
-            return "报告生成失败"
-
-        except Exception as e:
-            self.chatbot.append(["错误", f"生成报告时出错: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return "报告生成失败: " + str(e)
-
-    def save_report(self, report: str) -> Generator:
-        """保存分析报告"""
-        timestamp = time.strftime("%Y%m%d_%H%M%S")
-
-        # 保存为Markdown文件
-        try:
-            md_content = f"# 论文快速解读报告\n\n{report}"
-            for q in self.questions:
-                if q.id in self.results:
-                    md_content += f"\n\n## {q.description}\n\n{self.results[q.id]}"
-
-            result_file = write_history_to_file(
-                history=[md_content],
-                file_basename=f"论文解读_{timestamp}.md"
-            )
-
-            if result_file and os.path.exists(result_file):
-                promote_file_to_downloadzone(result_file, chatbot=self.chatbot)
-                self.chatbot.append(["保存成功", f"解读报告已保存至: {os.path.basename(result_file)}"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-            else:
-                self.chatbot.append(["警告", "保存报告成功但找不到文件"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-        except Exception as e:
-            self.chatbot.append(["警告", f"保存报告失败: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-    def analyze_paper(self, paper_path: str) -> Generator:
-        """分析论文主流程"""
-        # 加载论文
-        success = yield from self._load_paper(paper_path)
-        if not success:
-            return
-
-        # 分析关键问题 - 直接询问每个问题，不显示进度信息
-        for question in self.questions:
-            yield from self._analyze_question(question)
-
-        # 生成总结报告
-        final_report = yield from self._generate_summary()
-
-        # 显示最终报告
-        # self.chatbot.append(["论文解读报告", final_report])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 保存报告
-        yield from self.save_report(final_report)
-
-
-def _find_paper_file(path: str) -> str:
-    """查找路径中的论文文件（简化版）"""
-    if os.path.isfile(path):
-        return path
-
-    # 支持的文件扩展名（按优先级排序）
-    extensions = ["pdf", "docx", "doc", "txt", "md", "tex"]
-
-    # 简单地遍历目录
-    if os.path.isdir(path):
-        try:
-            for ext in extensions:
-                # 手动检查每个可能的文件，而不使用glob
-                potential_file = os.path.join(path, f"paper.{ext}")
-                if os.path.exists(potential_file) and os.path.isfile(potential_file):
-                    return potential_file
-
-            # 如果没找到特定命名的文件，检查目录中的所有文件
-            for file in os.listdir(path):
-                file_path = os.path.join(path, file)
-                if os.path.isfile(file_path):
-                    file_ext = file.split('.')[-1].lower() if '.' in file else ""
-                    if file_ext in extensions:
-                        return file_path
-        except Exception:
-            pass  # 忽略任何错误
-
-    return None
-
-
-def download_paper_by_id(paper_info, chatbot, history) -> str:
-    """下载论文并返回保存路径
-
-    Args:
-        paper_info: 元组，包含论文ID类型（arxiv或doi）和ID值
-        chatbot: 聊天机器人对象
-        history: 历史记录
-
-    Returns:
-        str: 下载的论文路径或None
-    """
-    from crazy_functions.review_fns.data_sources.scihub_source import SciHub
-    id_type, paper_id = paper_info
-
-    # 创建保存目录 - 使用时间戳创建唯一文件夹
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    user_name = chatbot.get_user() if hasattr(chatbot, 'get_user') else "default"
-    from toolbox import get_log_folder, get_user
-    base_save_dir = get_log_folder(get_user(chatbot), plugin_name='paper_download')
-    save_dir = os.path.join(base_save_dir, f"papers_{timestamp}")
-    if not os.path.exists(save_dir):
-        os.makedirs(save_dir)
-    save_path = Path(save_dir)
-
-    chatbot.append([f"下载论文", f"正在下载{'arXiv' if id_type == 'arxiv' else 'DOI'} {paper_id} 的论文..."])
-    update_ui(chatbot=chatbot, history=history)
-
-    pdf_path = None
-
-    try:
-        if id_type == 'arxiv':
-            # 使用改进的arxiv查询方法
-            formatted_id = format_arxiv_id(paper_id)
-            paper_result = get_arxiv_paper(formatted_id)
-
-            if not paper_result:
-                chatbot.append([f"下载失败", f"未找到arXiv论文: {paper_id}"])
-                update_ui(chatbot=chatbot, history=history)
-                return None
-
-            # 下载PDF
-            filename = f"arxiv_{paper_id.replace('/', '_')}.pdf"
-            pdf_path = str(save_path / filename)
-            paper_result.download_pdf(filename=pdf_path)
-
-        else:  # doi
-            # 下载DOI
-            sci_hub = SciHub(
-                doi=paper_id,
-                path=save_path
-            )
-            pdf_path = sci_hub.fetch()
-
-        # 检查下载结果
-        if pdf_path and os.path.exists(pdf_path):
-            promote_file_to_downloadzone(pdf_path, chatbot=chatbot)
-            chatbot.append([f"下载成功", f"已成功下载论文: {os.path.basename(pdf_path)}"])
-            update_ui(chatbot=chatbot, history=history)
-            return pdf_path
-        else:
-            chatbot.append([f"下载失败", f"论文下载失败: {paper_id}"])
-            update_ui(chatbot=chatbot, history=history)
-            return None
-
-    except Exception as e:
-        chatbot.append([f"下载错误", f"下载论文时出错: {str(e)}"])
-        update_ui(chatbot=chatbot, history=history)
-        return None
-
-
-@CatchException
-def 快速论文解读(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-             history: List, system_prompt: str, user_request: str):
-    """主函数 - 论文快速解读"""
-    # 初始化分析器
-    chatbot.append(["函数插件功能及使用方式", "论文快速解读：通过分析论文的关键要素，帮助您迅速理解论文内容，适用于各学科领域的科研论文。 <br><br>📋 使用方式：<br>1、直接上传PDF文件或者输入DOI号（仅针对SCI hub存在的论文）或arXiv ID（如2501.03916）<br>2、点击插件开始分析"])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    paper_file = None
-
-    # 检查输入是否为论文ID（arxiv或DOI）
-    paper_info = extract_paper_id(txt)
-
-    if paper_info:
-        # 如果是论文ID，下载论文
-        chatbot.append(["检测到论文ID", f"检测到{'arXiv' if paper_info[0] == 'arxiv' else 'DOI'} ID: {paper_info[1]}，准备下载论文..."])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-        # 下载论文 - 完全重新实现
-        paper_file = download_paper_by_id(paper_info, chatbot, history)
-
-        if not paper_file:
-            report_exception(chatbot, history, a=f"下载论文失败", b=f"无法下载{'arXiv' if paper_info[0] == 'arxiv' else 'DOI'}论文: {paper_info[1]}")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-    else:
-        # 检查输入路径
-        if not os.path.exists(txt):
-            report_exception(chatbot, history, a=f"解析论文: {txt}", b=f"找不到文件或无权访问: {txt}")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-
-        # 验证路径安全性
-        user_name = chatbot.get_user()
-        validate_path_safety(txt, user_name)
-
-        # 查找论文文件
-        paper_file = _find_paper_file(txt)
-
-        if not paper_file:
-            report_exception(chatbot, history, a=f"解析论文", b=f"在路径 {txt} 中未找到支持的论文文件")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    # 增加调试信息，检查paper_file的类型和值
-    chatbot.append(["文件类型检查", f"paper_file类型: {type(paper_file)}, 值: {paper_file}"])
-    yield from update_ui(chatbot=chatbot, history=history)
-    chatbot.pop()  # 移除调试信息
-
-    # 确保paper_file是字符串
-    if paper_file is not None and not isinstance(paper_file, str):
-        # 尝试转换为字符串
-        try:
-            paper_file = str(paper_file)
-        except:
-            report_exception(chatbot, history, a=f"类型错误", b=f"论文路径不是有效的字符串: {type(paper_file)}")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-
-    # 分析论文
-    chatbot.append(["开始分析", f"正在分析论文: {os.path.basename(paper_file)}"])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    analyzer = PaperAnalyzer(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    yield from analyzer.analyze_paper(paper_file)
--- a/crazy_functions/Rag_Interface.py
+++ b/crazy_functions/Rag_Interface.py
@@ -1,67 +1,31 @@
-import os,glob
-from typing import List
-
-from shared_utils.fastapi_server import validate_path_safety
-
-from toolbox import report_exception
-from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_latest_msg
-from shared_utils.fastapi_server import validate_path_safety
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive

-RAG_WORKER_REGISTER = {}
-MAX_HISTORY_ROUND = 5
-MAX_CONTEXT_TOKEN_LIMIT = 4096
-REMEMBER_PREVIEW = 1000
-
-@CatchException
-def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker):
-    """
-    Handles document uploads by extracting text and adding it to the vector store.
-    """
-    from llama_index.core import Document
-    from crazy_functions.rag_fns.rag_file_support import extract_text, supports_format
-    user_name = chatbot.get_user()
-    checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
-
-    for file_path in files:
-        try:
-            validate_path_safety(file_path, user_name)
-            text = extract_text(file_path)
-            if text is None:
-                chatbot.append(
-                    [f"上传文件: {os.path.basename(file_path)}", f"文件解析失败，无法提取文本内容，请更换文件。失败原因可能为：1.文档格式过于复杂；2. 不支持的文件格式，支持的文件格式后缀有:" + ", ".join(supports_format)])
-            else:
-                chatbot.append(
-                    [f"上传文件: {os.path.basename(file_path)}", f"上传文件前50个字符为:{text[:50]}。"])
-                document = Document(text=text, metadata={"source": file_path})
-                rag_worker.add_documents_to_vector_store([document])
-                chatbot.append([f"上传文件: {os.path.basename(file_path)}", "文件已成功添加到知识库。"])
-        except Exception as e:
-            report_exception(chatbot, history, a=f"处理文件: {file_path}", b=str(e))
-
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-
-
-# Main Q&A function with document upload support
-@CatchException
-def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-
-    # import vector store lib
 VECTOR_STORE_TYPE = "Milvus"
+
 if VECTOR_STORE_TYPE == "Milvus":
    try:
        from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
    except:
        VECTOR_STORE_TYPE = "Simple"
+
 if VECTOR_STORE_TYPE == "Simple":
    from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker

+
+RAG_WORKER_REGISTER = {}
+
+MAX_HISTORY_ROUND = 5
+MAX_CONTEXT_TOKEN_LIMIT = 4096
+REMEMBER_PREVIEW = 1000
+
+@CatchException
+def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+
    # 1. we retrieve rag worker from global context
    user_name = chatbot.get_user()
    checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
-
    if user_name in RAG_WORKER_REGISTER:
        rag_worker = RAG_WORKER_REGISTER[user_name]
    else:
@@ -69,37 +33,21 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
            user_name, 
            llm_kwargs, 
            checkpoint_dir=checkpoint_dir, 
-            auto_load_checkpoint=True
-        )
-
+            auto_load_checkpoint=True)
    current_context = f"{VECTOR_STORE_TYPE} @ {checkpoint_dir}"
    tip = "提示：输入“清空向量数据库”可以清空RAG向量数据库"
-
-    # 2. Handle special commands
-    if os.path.exists(txt) and os.path.isdir(txt):
-        project_folder = txt
-        validate_path_safety(project_folder, chatbot.get_user())
-        # Extract file paths from the user input
-        # Assuming the user inputs file paths separated by commas after the command
-        file_paths =  [f for f in glob.glob(f'{project_folder}/**/*', recursive=True)]
-        chatbot.append([txt, f'正在处理上传的文档 ({current_context}) ...'])
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-        yield from handle_document_upload(file_paths, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker)
-        return
-
-    elif txt == "清空向量数据库":
+    if txt == "清空向量数据库":
        chatbot.append([txt, f'正在清空 ({current_context}) ...'])
        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        rag_worker.purge_vector_store()
-        yield from update_ui_latest_msg('已清空', chatbot, history, delay=0)  # 刷新界面
+        rag_worker.purge()
+        yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
        return

-    # 3. Normal Q&A processing
    chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

-    # 4. Clip history to reduce token consumption
+    # 2. clip history to reduce token consumption
+    #   2-1. reduce chat round
    txt_origin = txt

    if len(history) > MAX_HISTORY_ROUND * 2:
@@ -107,47 +55,41 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
    txt_clip, history, flags = input_clipping(txt, history, max_token_limit=MAX_CONTEXT_TOKEN_LIMIT, return_clip_flags=True)
    input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])

-    # 5. If input is clipped, add input to vector store before retrieve
+    #   2-2. if input is clipped, add input to vector store before retrieve
    if input_is_clipped_flag:
-        yield from update_ui_latest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0)  # 刷新界面
-        # Save input to vector store
+        yield from update_ui_lastest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
+        # save input to vector store
        rag_worker.add_text_to_vector_store(txt_origin)
-        yield from update_ui_latest_msg('向量化完成 ...', chatbot, history, delay=0)  # 刷新界面
-
+        yield from update_ui_lastest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
        if len(txt_origin) > REMEMBER_PREVIEW:
            HALF = REMEMBER_PREVIEW//2
            i_say_to_remember = txt[:HALF] + f" ...\n...(省略{len(txt_origin)-REMEMBER_PREVIEW}字)...\n... " + txt[-HALF:]
            if (flags["original_input_len"] - flags["clipped_input_len"]) > HALF:
                txt_clip = txt_clip  + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
+            else:
+                pass
+            i_say = txt_clip
        else:
            i_say_to_remember = i_say = txt_clip
    else:
        i_say_to_remember = i_say = txt_clip

-    # 6. Search vector store and build prompts
+    # 3. we search vector store and build prompts
    nodes = rag_worker.retrieve_from_store_with_query(i_say)
    prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
-    # 7. Query language model
-    if len(chatbot) != 0:
-        chatbot.pop(-1)  # Pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`

+    # 4. it is time to query llms
+    if len(chatbot) != 0: chatbot.pop(-1) # pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
    model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=prompt,
-        inputs_show_user=i_say,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history=history,
+        inputs=prompt, inputs_show_user=i_say,
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
        sys_prompt=system_prompt,
        retry_times_at_unknown_error=0
    )

-    # 8. Remember Q&A
-    yield from update_ui_latest_msg(
-        model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...',
-        chatbot, history, delay=0.5
-    )
+    # 5. remember what has been asked / answered
+    yield from update_ui_lastest_msg(model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...', chatbot, history, delay=0.5) # 刷新界面
    rag_worker.remember_qa(i_say_to_remember, model_say)
    history.extend([i_say, model_say])

-    # 9. Final UI Update
-    yield from update_ui_latest_msg(model_say, chatbot, history, delay=0, msg=tip)
+    yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip) # 刷新界面
--- a/crazy_functions/Social_Helper.py
+++ b/crazy_functions/Social_Helper.py
@@ -1,5 +1,5 @@
 import pickle, os, random
-from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_latest_msg
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from request_llms.bridge_all import predict_no_ui_long_connection
@@ -9,7 +9,7 @@ from loguru import logger
 from typing import List


-SOCIAL_NETWORK_WORKER_REGISTER = {}
+SOCIAL_NETWOK_WORKER_REGISTER = {}

 class SocialNetwork():
    def __init__(self):
@@ -78,7 +78,7 @@ class SocialNetworkWorker(SaveAndLoad):
            for f in friend.friends_list: 
                self.add_friend(f)
            msg = f"成功添加{len(friend.friends_list)}个联系人: {str(friend.friends_list)}"
-            yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=0)
+            yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=0)


    def run(self, txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
@@ -104,12 +104,12 @@ class SocialNetworkWorker(SaveAndLoad):
        }

        try:
-            Explanation = '\n'.join([f'{k}: {v["explain_to_llm"]}' for k, v in self.tools_to_select.items()])
+            Explaination = '\n'.join([f'{k}: {v["explain_to_llm"]}' for k, v in self.tools_to_select.items()])
            class UserSociaIntention(BaseModel):
                intention_type: str = Field(
                    description=
                        f"The type of user intention. You must choose from {self.tools_to_select.keys()}.\n\n" 
-                        f"Explanation:\n{Explanation}", 
+                        f"Explaination:\n{Explaination}", 
                    default="SocialAdvice"
                )
            pydantic_cls_instance, err_msg = select_tool(
@@ -118,7 +118,7 @@ class SocialNetworkWorker(SaveAndLoad):
                pydantic_cls=UserSociaIntention
            )
        except Exception as e:
-            yield from update_ui_latest_msg(
+            yield from update_ui_lastest_msg(
                lastmsg=f"无法理解用户意图 {err_msg}", 
                chatbot=chatbot, 
                history=history, 
@@ -150,10 +150,10 @@ def I人助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt,
    # 1. we retrieve worker from global context
    user_name = chatbot.get_user()
    checkpoint_dir=get_log_folder(user_name, plugin_name='experimental_rag')
-    if user_name in SOCIAL_NETWORK_WORKER_REGISTER:
-        social_network_worker = SOCIAL_NETWORK_WORKER_REGISTER[user_name]
+    if user_name in SOCIAL_NETWOK_WORKER_REGISTER:
+        social_network_worker = SOCIAL_NETWOK_WORKER_REGISTER[user_name]
    else:
-        social_network_worker = SOCIAL_NETWORK_WORKER_REGISTER[user_name] = SocialNetworkWorker(
+        social_network_worker = SOCIAL_NETWOK_WORKER_REGISTER[user_name] = SocialNetworkWorker(
            user_name, 
            llm_kwargs, 
            checkpoint_dir=checkpoint_dir, 
--- a/crazy_functions/SourceCode_Comment.py
+++ b/crazy_functions/SourceCode_Comment.py
@@ -1,15 +1,12 @@
 import os, copy, time
-from toolbox import CatchException, report_exception, update_ui, zip_result, promote_file_to_downloadzone, update_ui_latest_msg, get_conf, generate_file_link
+from toolbox import CatchException, report_exception, update_ui, zip_result, promote_file_to_downloadzone, update_ui_lastest_msg, get_conf, generate_file_link
 from shared_utils.fastapi_server import validate_path_safety
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from crazy_functions.agent_fns.python_comment_agent import PythonCodeComment
 from crazy_functions.diagram_fns.file_tree import FileNode
-from crazy_functions.agent_fns.watchdog import WatchDog
 from shared_utils.advanced_markdown_format import markdown_convertion_for_file
-from loguru import logger
-

 def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):

@@ -27,13 +24,12 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
        file_tree_struct.add_file(file_path, file_path)

    # <第一步，逐个文件分析，多线程>
-    lang = "" if not plugin_kwargs["use_chinese"] else " (you must use Chinese)"
    for index, fp in enumerate(file_manifest):
        # 读取文件
        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
            file_content = f.read()
        prefix = ""
-        i_say = prefix + f'Please conclude the following source code at {os.path.relpath(fp, project_folder)} with only one sentence{lang}, the code is:\n```{file_content}```'
+        i_say = prefix + f'Please conclude the following source code at {os.path.relpath(fp, project_folder)} with only one sentence, the code is:\n```{file_content}```'
        i_say_show_user = prefix + f'[{index+1}/{len(file_manifest)}] 请用一句话对下面的程序文件做一个整体概述: {fp}'
        # 装载请求内容
        MAX_TOKEN_SINGLE_FILE = 2560
@@ -41,7 +37,7 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
        inputs_array.append(i_say)
        inputs_show_user_array.append(i_say_show_user)
        history_array.append([])
-        sys_prompt_array.append(f"You are a software architecture analyst analyzing a source code project. Do not dig into details, tell me what the code is doing in general. Your answer must be short, simple and clear{lang}.")
+        sys_prompt_array.append("You are a software architecture analyst analyzing a source code project. Do not dig into details, tell me what the code is doing in general. Your answer must be short, simple and clear.")
    # 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到大模型进行分析
    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
        inputs_array = inputs_array,
@@ -54,20 +50,10 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
    )

    # <第二步，逐个文件分析，生成带注释文件>
-    tasks = ["" for _ in range(len(file_manifest))]
-    def bark_fn(tasks):
-        for i in range(len(tasks)): tasks[i] = "watchdog is dead"
-    wd = WatchDog(timeout=10, bark_fn=lambda: bark_fn(tasks), interval=3, msg="ThreadWatcher timeout")
-    wd.begin_watch()
    from concurrent.futures import ThreadPoolExecutor
    executor = ThreadPoolExecutor(max_workers=get_conf('DEFAULT_WORKER_NUM'))
-    def _task_multi_threading(i_say, gpt_say, fp, file_tree_struct, index):
-        language = 'Chinese' if plugin_kwargs["use_chinese"] else 'English'
-        def observe_window_update(x):
-            if tasks[index] == "watchdog is dead":
-                raise TimeoutError("ThreadWatcher: watchdog is dead")
-            tasks[index] = x
-        pcc = PythonCodeComment(llm_kwargs, plugin_kwargs, language=language, observe_window_update=observe_window_update)
+    def _task_multi_threading(i_say, gpt_say, fp, file_tree_struct):
+        pcc = PythonCodeComment(llm_kwargs, language='English')
        pcc.read_file(path=fp, brief=gpt_say)
        revised_path, revised_content = pcc.begin_comment_source_code(None, None)
        file_tree_struct.manifest[fp].revised_path = revised_path
@@ -79,8 +65,7 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
        with open("crazy_functions/agent_fns/python_comment_compare.html", 'r', encoding='utf-8') as f:
            html_template = f.read()
        warp = lambda x: "```python\n\n" + x + "\n\n```"
-        from themes.theme import load_dynamic_theme
-        _, advanced_css, _, _ = load_dynamic_theme("Default")
+        from themes.theme import advanced_css
        html_template = html_template.replace("ADVANCED_CSS", advanced_css)
        html_template = html_template.replace("REPLACE_CODE_FILE_LEFT", pcc.get_markdown_block_in_html(markdown_convertion_for_file(warp(pcc.original_content))))
        html_template = html_template.replace("REPLACE_CODE_FILE_RIGHT", pcc.get_markdown_block_in_html(markdown_convertion_for_file(warp(revised_content))))
@@ -88,21 +73,17 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
        file_tree_struct.manifest[fp].compare_html = compare_html_path
        with open(compare_html_path, 'w', encoding='utf-8') as f:
            f.write(html_template)
-        tasks[index] = ""
+        # print('done 1')

    chatbot.append([None, f"正在处理:"])
    futures = []
-    index = 0
    for i_say, gpt_say, fp in zip(gpt_response_collection[0::2], gpt_response_collection[1::2], file_manifest):
-        future = executor.submit(_task_multi_threading, i_say, gpt_say, fp, file_tree_struct, index)
-        index += 1
+        future = executor.submit(_task_multi_threading, i_say, gpt_say, fp, file_tree_struct)
        futures.append(future)

-    # <第三步，等待任务完成>
    cnt = 0
    while True:
        cnt += 1
-        wd.feed()
        time.sleep(3)
        worker_done = [h.done() for h in futures]
        remain = len(worker_done) - sum(worker_done)
@@ -111,18 +92,14 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
        preview_html_list = []
        for done, fp in zip(worker_done, file_manifest):
            if not done: continue
-            if hasattr(file_tree_struct.manifest[fp], 'compare_html'):
            preview_html_list.append(file_tree_struct.manifest[fp].compare_html)
-            else:
-                logger.error(f"文件: {fp} 的注释结果未能成功")
        file_links = generate_file_link(preview_html_list)

-        yield from update_ui_latest_msg(
-            f"当前任务: <br/>{'<br/>'.join(tasks)}.<br/>" + 
-            f"剩余源文件数量: {remain}.<br/>" + 
-            f"已完成的文件: {sum(worker_done)}.<br/>" + 
+        yield from update_ui_lastest_msg(
+            f"剩余源文件数量: {remain}.\n\n" + 
+            f"已完成的文件: {sum(worker_done)}.\n\n" + 
            file_links +
-            "<br/>" +
+            "\n\n" +
            ''.join(['.']*(cnt % 10 + 1)
        ), chatbot=chatbot, history=history, delay=0)
        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
@@ -143,7 +120,6 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
@CatchException
 def 注释Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
    history = []    # 清空历史，以免输入溢出
-    plugin_kwargs["use_chinese"] = plugin_kwargs.get("use_chinese", False)
    import glob, os
    if os.path.exists(txt):
        project_folder = txt
--- a/crazy_functions/SourceCode_Comment_Wrap.py
+++ b/crazy_functions/SourceCode_Comment_Wrap.py
@@ -1,36 +0,0 @@
-
-from toolbox import get_conf, update_ui
-from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
-from crazy_functions.SourceCode_Comment import 注释Python项目
-
-class SourceCodeComment_Wrap(GptAcademicPluginTemplate):
-    def __init__(self):
-        """
-        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
-        """
-        pass
-
-    def define_arg_selection_menu(self):
-        """
-        定义插件的二级选项菜单
-        """
-        gui_definition = {
-            "main_input":
-                ArgProperty(title="路径", description="程序路径（上传文件后自动填写）", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
-            "use_chinese":
-                ArgProperty(title="注释语言", options=["英文", "中文"], default_value="英文", description="无", type="dropdown").model_dump_json(),
-            # "use_emoji":
-                # ArgProperty(title="在注释中使用emoji", options=["禁止", "允许"], default_value="禁止", description="无", type="dropdown").model_dump_json(),
-        }
-        return gui_definition
-
-    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-        """
-        执行插件
-        """
-        if plugin_kwargs["use_chinese"] == "中文": 
-            plugin_kwargs["use_chinese"] = True
-        else: 
-            plugin_kwargs["use_chinese"] = False
-
-        yield from 注释Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
--- a/crazy_functions/VideoResource_GPT.py
+++ b/crazy_functions/VideoResource_GPT.py
@@ -1,204 +0,0 @@
-import requests
-import random
-import time
-import re
-import json
-from bs4 import BeautifulSoup
-from functools import lru_cache
-from itertools import zip_longest
-from check_proxy import check_proxy
-from toolbox import CatchException, update_ui, get_conf, promote_file_to_downloadzone, update_ui_latest_msg, generate_file_link
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
-from request_llms.bridge_all import model_info
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.prompts.internet import SearchOptimizerPrompt, SearchAcademicOptimizerPrompt
-from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
-from textwrap import dedent
-from loguru import logger
-from pydantic import BaseModel, Field
-
-class Query(BaseModel):
-    search_keyword: str = Field(description="search query for video resource")
-
-
-class VideoResource(BaseModel):
-    thought: str = Field(description="analysis of the search results based on the user's query")
-    title: str = Field(description="title of the video")
-    author: str = Field(description="author/uploader of the video") 
-    bvid: str = Field(description="unique ID of the video")
-    another_failsafe_bvid: str = Field(description="provide another bvid, the other one is not working")
-
-
-def get_video_resource(search_keyword):
-    from crazy_functions.media_fns.get_media import search_videos
-
-    # Search for videos and return the first result
-    videos = search_videos(
-        search_keyword
-    )
-
-    # Return the first video if results exist, otherwise return None
-    return videos
-
-def download_video(bvid, user_name, chatbot, history):
-    # from experimental_mods.get_bilibili_resource import download_bilibili
-    from crazy_functions.media_fns.get_media import download_video
-    # pause a while
-    tic_time = 8
-    for i in range(tic_time):
-        yield from update_ui_latest_msg(
-            lastmsg=f"即将下载音频。等待{tic_time-i}秒后自动继续, 点击“停止”键取消此操作。", 
-            chatbot=chatbot, history=[], delay=1)
-
-    # download audio
-    chatbot.append((None, "下载音频, 请稍等...")); yield from update_ui(chatbot=chatbot, history=history)
-    downloaded_files = yield from download_video(bvid, only_audio=True, user_name=user_name, chatbot=chatbot, history=history)
-
-    if len(downloaded_files) == 0:
-        # failed to download audio
-        return []
-
-    # preview
-    preview_list = [promote_file_to_downloadzone(fp) for fp in downloaded_files]
-    file_links = generate_file_link(preview_list)
-    yield from update_ui_latest_msg(f"已完成的文件: <br/>" + file_links, chatbot=chatbot, history=history, delay=0)
-    chatbot.append((None, f"即将下载视频。"))
-
-    # pause a while
-    tic_time = 16
-    for i in range(tic_time):
-        yield from update_ui_latest_msg(
-            lastmsg=f"即将下载视频。等待{tic_time-i}秒后自动继续, 点击“停止”键取消此操作。", 
-            chatbot=chatbot, history=[], delay=1)
-
-    # download video
-    chatbot.append((None, "下载视频, 请稍等...")); yield from update_ui(chatbot=chatbot, history=history)
-    downloaded_files_part2 = yield from download_video(bvid, only_audio=False, user_name=user_name, chatbot=chatbot, history=history)
-
-    # preview
-    preview_list = [promote_file_to_downloadzone(fp) for fp in downloaded_files_part2]
-    file_links = generate_file_link(preview_list)
-    yield from update_ui_latest_msg(f"已完成的文件: <br/>" + file_links, chatbot=chatbot, history=history, delay=0)
-
-    # return
-    return downloaded_files + downloaded_files_part2
-
-
-class Strategy(BaseModel):
-    thought: str = Field(description="analysis of the user's wish, for example, can you recall the name of the resource?")
-    which_methods: str = Field(description="Which method to use to find the necessary information? choose from 'method_1' and 'method_2'.")
-    method_1_search_keywords: str = Field(description="Generate keywords to search the internet if you choose method 1, otherwise empty.")
-    method_2_generate_keywords: str = Field(description="Generate keywords for video download engine if you choose method 2, otherwise empty.")
-
-
-@CatchException
-def 多媒体任务(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    user_wish: str = txt
-    # query demos: 
-    #   - "我想找一首歌，里面有句歌词是“turn your face towards the sun”"
-    #   - "一首歌，第一句是红豆生南国"
-    #   - "一首音乐，中国航天任务专用的那首"
-    #   - "戴森球计划在熔岩星球的音乐"
-    #   - "hanser的百变什么精"
-    #   - "打大圣残躯时的bgm"
-    #   - "渊下宫战斗音乐"
-
-    # 搜索
-    chatbot.append((txt, "检索中, 请稍等..."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    if "跳过联网搜索" not in user_wish:
-        # 结构化生成
-        internet_search_keyword = user_wish
-
-        yield from update_ui_latest_msg(lastmsg=f"发起互联网检索: {internet_search_keyword} ...", chatbot=chatbot, history=[], delay=1)
-        from crazy_functions.Internet_GPT import internet_search_with_analysis_prompt
-        result = yield from internet_search_with_analysis_prompt(
-            prompt=internet_search_keyword,
-            analysis_prompt="请根据搜索结果分析，获取用户需要找的资源的名称、作者、出处等信息。",
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot
-        )
-
-        yield from update_ui_latest_msg(lastmsg=f"互联网检索结论: {result} \n\n 正在生成进一步检索方案 ...", chatbot=chatbot, history=[], delay=1)
-        rf_req = dedent(f"""
-        The user wish to get the following resource:
-            {user_wish}
-        Meanwhile, you can access another expert's opinion on the user's wish:
-            {result}
-        Generate search keywords (less than 5 keywords) for video download engine accordingly.
-        """)
-    else:
-        user_wish = user_wish.replace("跳过联网搜索", "").strip()
-        rf_req = dedent(f"""
-        The user wish to get the following resource:
-            {user_wish}
-        Generate research keywords (less than 5 keywords) accordingly.
-        """)
-    gpt_json_io = GptJsonIO(Query)
-    inputs = rf_req + gpt_json_io.format_instructions
-    run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
-    analyze_res = run_gpt_fn(inputs, "")
-    logger.info(analyze_res)
-    query: Query = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
-    video_engine_keywords = query.search_keyword
-    # 关键词展示
-    chatbot.append((None, f"检索关键词已确认: {video_engine_keywords}。筛选中, 请稍等..."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 获取候选资源
-    candidate_dictionary: dict =  get_video_resource(video_engine_keywords)
-    candidate_dictionary_as_str = json.dumps(candidate_dictionary, ensure_ascii=False, indent=4)
-
-    # 展示候选资源
-    candidate_display = "\n".join([f"{i+1}. {it['title']}" for i, it in enumerate(candidate_dictionary)])
-    chatbot.append((None, f"候选:\n\n{candidate_display}"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 结构化生成
-    rf_req_2 = dedent(f"""
-    The user wish to get the following resource:
-        {user_wish}
-
-    Select the most relevant and suitable video resource from the following search results:
-        {candidate_dictionary_as_str}
-
-    Note:
-        1. The first several search video results are more likely to satisfy the user's wish.
-        2. The time duration of the video should be less than 10 minutes.
-        3. You should analyze the search results first, before giving your answer.
-        4. Use Chinese if possible.
-        5. Beside the primary video selection, give a backup video resource `bvid`.
-    """)
-    gpt_json_io = GptJsonIO(VideoResource)
-    inputs = rf_req_2 + gpt_json_io.format_instructions
-    run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
-    analyze_res = run_gpt_fn(inputs, "")
-    logger.info(analyze_res)
-    video_resource: VideoResource = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
-
-    # Display
-    chatbot.append(
-        (None, 
-            f"分析：{video_resource.thought}" "<br/>"
-            f"选择: `{video_resource.title}`。" "<br/>" 
-            f"作者：{video_resource.author}"
-        )
-    )
-    chatbot.append((None, f"下载中, 请稍等..."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if video_resource and video_resource.bvid:
-        logger.info(video_resource)
-        downloaded = yield from download_video(video_resource.bvid, chatbot.get_user(), chatbot, history)
-        if not downloaded:
-            chatbot.append((None, f"下载失败, 尝试备选 ..."))
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-            downloaded = yield from download_video(video_resource.another_failsafe_bvid, chatbot.get_user(), chatbot, history)
-
-
-
-
-        
-@CatchException
-def debug(bvid, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    yield from download_video(bvid, chatbot.get_user(), chatbot, history)
--- a/crazy_functions/agent_fns/auto_agent.py
+++ b/crazy_functions/agent_fns/auto_agent.py
@@ -1,5 +1,5 @@
 from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
-from toolbox import report_exception, get_log_folder, update_ui_latest_msg, Singleton
+from toolbox import report_exception, get_log_folder, update_ui_lastest_msg, Singleton
 from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
 from crazy_functions.agent_fns.general import AutoGenGeneral

--- a/crazy_functions/agent_fns/echo_agent.py
+++ b/crazy_functions/agent_fns/echo_agent.py
@@ -8,7 +8,7 @@ class EchoDemo(PluginMultiprocessManager):
        while True:
            msg = self.child_conn.recv() # PipeCom
            if msg.cmd == "user_input":
-                # wait father user input
+                # wait futher user input
                self.child_conn.send(PipeCom("show", msg.content))
                wait_success = self.subprocess_worker_wait_user_feedback(wait_msg="我准备好处理下一个问题了.")
                if not wait_success:
--- a/crazy_functions/agent_fns/general.py
+++ b/crazy_functions/agent_fns/general.py
@@ -27,7 +27,7 @@ def gpt_academic_generate_oai_reply(
        llm_kwargs=llm_config,
        history=history,
        sys_prompt=self._oai_system_message[0]['content'],
-        console_silence=True
+        console_slience=True
    )
    assumed_done = reply.endswith('\nTERMINATE')
    return True, reply
--- a/crazy_functions/agent_fns/python_comment_agent.py
+++ b/crazy_functions/agent_fns/python_comment_agent.py
@@ -10,7 +10,7 @@ from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_
 # TODO: 解决缩进问题

 find_function_end_prompt = '''
-Below is a page of code that you need to read. This page may not yet complete, you job is to split this page to separate functions, class functions etc.
+Below is a page of code that you need to read. This page may not yet complete, you job is to split this page to sperate functions, class functions etc.
 - Provide the line number where the first visible function ends.
 - Provide the line number where the next visible function begins.
 - If there are no other functions in this page, you should simply return the line number of the last line.
@@ -59,7 +59,7 @@ OUTPUT:



-revise_function_prompt = '''
+revise_funtion_prompt = '''
 You need to read the following code, and revise the source code ({FILE_BASENAME}) according to following instructions:
 1. You should analyze the purpose of the functions (if there are any).
 2. You need to add docstring for the provided functions (if there are any).
@@ -68,7 +68,6 @@ Be aware:
 1. You must NOT modify the indent of code.
 2. You are NOT authorized to change or translate non-comment code, and you are NOT authorized to add empty lines either, toggle qu.
 3. Use {LANG} to add comments and docstrings. Do NOT translate Chinese that is already in the code.
-4. Besides adding a docstring, use the ⭐ symbol to annotate the most core and important line of code within the function, explaining its role.

 ------------------ Example ------------------
 INPUT:
@@ -117,66 +116,10 @@ def zip_result(folder):
 '''


-revise_function_prompt_chinese = '''
-您需要阅读以下代码，并根据以下说明修订源代码({FILE_BASENAME}):
-1. 如果源代码中包含函数的话, 你应该分析给定函数实现了什么功能
-2. 如果源代码中包含函数的话, 你需要为函数添加docstring, docstring必须使用中文
-
-请注意：
-1. 你不得修改代码的缩进
-2. 你无权更改或翻译代码中的非注释部分，也不允许添加空行
-3. 使用 {LANG} 添加注释和文档字符串。不要翻译代码中已有的中文
-4. 除了添加docstring之外, 使用⭐符号给该函数中最核心、最重要的一行代码添加注释，并说明其作用
-
------------------ 示例 ------------------
-INPUT:
-```
-L0000 |
-L0001 |def zip_result(folder):
-L0002 |    t = gen_time_str()
-L0003 |    zip_folder(folder, get_log_folder(), f"result.zip")
-L0004 |    return os.path.join(get_log_folder(), f"result.zip")
-L0005 |
-L0006 |
-```
-
-OUTPUT:
-
-<instruction_1_purpose>
-该函数用于压缩指定文件夹，并返回生成的`zip`文件的路径。
-</instruction_1_purpose>
-<instruction_2_revised_code>
-```
-def zip_result(folder):
-    """
-    该函数将指定的文件夹压缩成ZIP文件, 并将其存储在日志文件夹中。
-
-    输入参数:
-        folder (str): 需要压缩的文件夹的路径。
-    返回值:
-        str: 日志文件夹中创建的ZIP文件的路径。
-    """
-    t = gen_time_str()
-    zip_folder(folder, get_log_folder(), f"result.zip")  # ⭐ 执行文件夹的压缩
-    return os.path.join(get_log_folder(), f"result.zip")
-```
-</instruction_2_revised_code>
------------------ End of Example ------------------
-
-
------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
-```
-{THE_CODE}
-```
-{INDENT_REMINDER}
-{BRIEF_REMINDER}
-{HINT_REMINDER}
-'''
-

 class PythonCodeComment():

-    def __init__(self, llm_kwargs, plugin_kwargs, language, observe_window_update) -> None:
+    def __init__(self, llm_kwargs, language) -> None:
        self.original_content = ""
        self.full_context = []
        self.full_context_with_line_no = []
@@ -184,13 +127,7 @@ class PythonCodeComment():
        self.page_limit = 100 # 100 lines of code each page
        self.ignore_limit = 20
        self.llm_kwargs = llm_kwargs
-        self.plugin_kwargs = plugin_kwargs
        self.language = language
-        self.observe_window_update = observe_window_update
-        if self.language == "chinese":
-            self.core_prompt = revise_function_prompt_chinese
-        else:
-            self.core_prompt = revise_function_prompt
        self.path = None
        self.file_basename = None
        self.file_brief = ""
@@ -222,7 +159,7 @@ class PythonCodeComment():
            history=[],
            sys_prompt="",
            observe_window=[],
-            console_silence=True
+            console_slience=True
        )

        def extract_number(text):
@@ -316,12 +253,12 @@ class PythonCodeComment():
    def tag_code(self, fn, hint):
        code = fn
        _, n_indent = self.dedent(code)
-        indent_reminder = "" if n_indent == 0 else "(Reminder: as you can see, this piece of code has indent made up with {n_indent} whitespace, please preserve them in the OUTPUT.)"
+        indent_reminder = "" if n_indent == 0 else "(Reminder: as you can see, this piece of code has indent made up with {n_indent} whitespace, please preseve them in the OUTPUT.)"
        brief_reminder = "" if self.file_brief == "" else f"({self.file_basename} abstract: {self.file_brief})"
        hint_reminder = "" if hint is None else f"(Reminder: do not ignore or modify code such as `{hint}`, provide complete code in the OUTPUT.)"
        self.llm_kwargs['temperature'] = 0
        result = predict_no_ui_long_connection(
-            inputs=self.core_prompt.format(
+            inputs=revise_funtion_prompt.format(
                LANG=self.language, 
                FILE_BASENAME=self.file_basename, 
                THE_CODE=code, 
@@ -333,7 +270,7 @@ class PythonCodeComment():
            history=[],
            sys_prompt="",
            observe_window=[],
-            console_silence=True
+            console_slience=True
        )

        def get_code_block(reply):
@@ -400,7 +337,7 @@ class PythonCodeComment():
        return revised

    def begin_comment_source_code(self, chatbot=None, history=None):
-        # from toolbox import update_ui_latest_msg
+        # from toolbox import update_ui_lastest_msg
        assert self.path is not None
        assert '.py' in self.path   # must be python source code
        # write_target = self.path + '.revised.py'
@@ -409,10 +346,9 @@ class PythonCodeComment():
        # with open(self.path + '.revised.py', 'w+', encoding='utf8') as f:
        while True:
            try:
-                # yield from update_ui_latest_msg(f"({self.file_basename}) 正在读取下一段代码片段:\n", chatbot=chatbot, history=history, delay=0)
+                # yield from update_ui_lastest_msg(f"({self.file_basename}) 正在读取下一段代码片段:\n", chatbot=chatbot, history=history, delay=0)
                next_batch, line_no_start, line_no_end = self.get_next_batch()
-                self.observe_window_update(f"正在处理{self.file_basename} - {line_no_start}/{len(self.full_context)}\n")
-                # yield from update_ui_latest_msg(f"({self.file_basename}) 处理代码片段:\n\n{next_batch}", chatbot=chatbot, history=history, delay=0)
+                # yield from update_ui_lastest_msg(f"({self.file_basename}) 处理代码片段:\n\n{next_batch}", chatbot=chatbot, history=history, delay=0)
                
                hint = None
                MAX_ATTEMPT = 2
--- a/crazy_functions/ast_fns/comment_remove.py
+++ b/crazy_functions/ast_fns/comment_remove.py
@@ -1,47 +1,39 @@
-import token
-import tokenize
-import copy
-import io
+import ast
+
+class CommentRemover(ast.NodeTransformer):
+    def visit_FunctionDef(self, node):
+        # 移除函数的文档字符串
+        if (node.body and isinstance(node.body[0], ast.Expr) and
+                isinstance(node.body[0].value, ast.Str)):
+            node.body = node.body[1:]
+        self.generic_visit(node)
+        return node
+
+    def visit_ClassDef(self, node):
+        # 移除类的文档字符串
+        if (node.body and isinstance(node.body[0], ast.Expr) and
+                isinstance(node.body[0].value, ast.Str)):
+            node.body = node.body[1:]
+        self.generic_visit(node)
+        return node
+
+    def visit_Module(self, node):
+        # 移除模块的文档字符串
+        if (node.body and isinstance(node.body[0], ast.Expr) and
+                isinstance(node.body[0].value, ast.Str)):
+            node.body = node.body[1:]
+        self.generic_visit(node)
+        return node
    

-def remove_python_comments(input_source: str) -> str:
-    source_flag = copy.copy(input_source)
-    source = io.StringIO(input_source)
-    ls = input_source.split('\n')
-    prev_toktype = token.INDENT
-    readline = source.readline
-
-    def get_char_index(lineno, col):
-        # find the index of the char in the source code
-        if lineno == 1:
-            return len('\n'.join(ls[:(lineno-1)])) + col
-        else:
-            return len('\n'.join(ls[:(lineno-1)])) + col + 1
-
-    def replace_char_between(start_lineno, start_col, end_lineno, end_col, source, replace_char, ls):
-        # replace char between start_lineno, start_col and end_lineno, end_col with replace_char, but keep '\n' and ' '
-        b = get_char_index(start_lineno, start_col)
-        e = get_char_index(end_lineno, end_col)
-        for i in range(b, e):
-            if source[i] == '\n':
-                source = source[:i] + '\n' + source[i+1:]
-            elif source[i] == ' ':
-                source = source[:i] + ' ' + source[i+1:]
-            else:
-                source = source[:i] + replace_char + source[i+1:]
-        return source
-
-    tokgen = tokenize.generate_tokens(readline)
-    for toktype, ttext, (slineno, scol), (elineno, ecol), ltext in tokgen:
-        if toktype == token.STRING and (prev_toktype == token.INDENT):
-            source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
-        elif toktype == token.STRING and (prev_toktype == token.NEWLINE):
-            source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
-        elif toktype == tokenize.COMMENT:
-            source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
-        prev_toktype = toktype
-    return source_flag
-
+def remove_python_comments(source_code):
+    # 解析源代码为 AST
+    tree = ast.parse(source_code)
+    # 移除注释
+    transformer = CommentRemover()
+    tree = transformer.visit(tree)
+    # 将处理后的 AST 转换回源代码
+    return ast.unparse(tree)

 # 示例使用
 if __name__ == "__main__":
--- a/crazy_functions/chatglm微调工具.py
+++ b/crazy_functions/chatglm微调工具.py
@@ -0,0 +1,141 @@
+from toolbox import CatchException, update_ui, promote_file_to_downloadzone
+from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+import datetime, json
+
+def fetch_items(list_of_items, batch_size):
+    for i in range(0, len(list_of_items), batch_size):
+        yield list_of_items[i:i + batch_size]
+
+def string_to_options(arguments):
+    import argparse
+    import shlex
+
+    # Create an argparse.ArgumentParser instance
+    parser = argparse.ArgumentParser()
+
+    # Add command-line arguments
+    parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
+    parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
+    parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
+    parser.add_argument("--batch", type=int, help="System prompt", default=50)
+    parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
+    parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
+    parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
+    parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
+    parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
+
+
+
+    # Parse the arguments
+    args = parser.parse_args(shlex.split(arguments))
+
+    return args
+
+@CatchException
+def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    user_request    当前用户的请求信息（IP地址等）
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    args = plugin_kwargs.get("advanced_arg", None)
+    if args is None:
+        chatbot.append(("没给定指令", "退出"))
+        yield from update_ui(chatbot=chatbot, history=history); return
+    else:
+        arguments = string_to_options(arguments=args)
+
+    dat = []
+    with open(txt, 'r', encoding='utf8') as f:
+        for line in f.readlines():
+            json_dat = json.loads(line)
+            dat.append(json_dat["content"])
+
+    llm_kwargs['llm_model'] = arguments.llm_to_learn
+    for batch in fetch_items(dat, arguments.batch):
+        res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+            inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
+            inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot,
+            history_array=[[] for _ in (batch)],
+            sys_prompt_array=[arguments.system_prompt for _ in (batch)],
+            max_workers=10  # OpenAI所允许的最大并行过载
+        )
+
+        with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
+            for b, r in zip(batch, res[1::2]):
+                f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
+
+    promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
+    return
+
+
+
+@CatchException
+def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    user_request    当前用户的请求信息（IP地址等）
+    """
+    import subprocess
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    args = plugin_kwargs.get("advanced_arg", None)
+    if args is None:
+        chatbot.append(("没给定指令", "退出"))
+        yield from update_ui(chatbot=chatbot, history=history); return
+    else:
+        arguments = string_to_options(arguments=args)
+
+
+
+    pre_seq_len = arguments.pre_seq_len             # 128
+    learning_rate = arguments.learning_rate                               # 2e-2
+    num_gpus = arguments.num_gpus                   # 1
+    json_dataset = arguments.json_dataset                 # 't_code.json'
+    ptuning_directory = arguments.ptuning_directory       # '/home/hmp/ChatGLM2-6B/ptuning'
+
+    command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
+        --do_train \
+        --train_file AdvertiseGen/{json_dataset} \
+        --validation_file AdvertiseGen/{json_dataset} \
+        --preprocessing_num_workers 20 \
+        --prompt_column content \
+        --response_column summary \
+        --overwrite_cache \
+        --model_name_or_path THUDM/chatglm2-6b \
+        --output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
+        --overwrite_output_dir \
+        --max_source_length 256 \
+        --max_target_length 256 \
+        --per_device_train_batch_size 1 \
+        --per_device_eval_batch_size 1 \
+        --gradient_accumulation_steps 16 \
+        --predict_with_generate \
+        --max_steps 100 \
+        --logging_steps 10 \
+        --save_steps 20 \
+        --learning_rate {learning_rate} \
+        --pre_seq_len {pre_seq_len} \
+        --quantization_bit 4"
+
+    process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
+    try:
+        process.communicate(timeout=3600*24)
+    except subprocess.TimeoutExpired:
+        process.kill()
+    return
--- a/crazy_functions/crazy_utils.py
+++ b/crazy_functions/crazy_utils.py
@@ -1,7 +1,7 @@
 import os
 import threading
 from loguru import logger
-from shared_utils.char_visual_effect import scrolling_visual_effect
+from shared_utils.char_visual_effect import scolling_visual_effect
 from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton

 def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
@@ -169,7 +169,6 @@ def can_multi_process(llm) -> bool:
    def default_condition(llm) -> bool:
        # legacy condition
        if llm.startswith('gpt-'): return True
-        if llm.startswith('chatgpt-'): return True
        if llm.startswith('api2d-'): return True
        if llm.startswith('azure-'): return True
        if llm.startswith('spark'): return True
@@ -256,7 +255,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                # 【第一种情况】：顺利完成
                gpt_say = predict_no_ui_long_connection(
                    inputs=inputs, llm_kwargs=llm_kwargs, history=history,
-                    sys_prompt=sys_prompt, observe_window=mutable[index], console_silence=True
+                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
                )
                mutable[index][2] = "已成功"
                return gpt_say
@@ -326,7 +325,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
            mutable[thread_index][1] = time.time()
        # 在前端打印些好玩的东西
        for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = f"[ ...`{scrolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
+            print_something_really_funny = f"[ ...`{scolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
            observe_win.append(print_something_really_funny)
        # 在前端打印些好玩的东西
        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
@@ -389,11 +388,11 @@ def read_and_clean_pdf_text(fp):
        """
        提取文本块主字体
        """
-        fsize_statistics = {}
+        fsize_statiscs = {}
        for wtf in l['spans']:
-            if wtf['size'] not in fsize_statistics: fsize_statistics[wtf['size']] = 0
-            fsize_statistics[wtf['size']] += len(wtf['text'])
-        return max(fsize_statistics, key=fsize_statistics.get)
+            if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
+            fsize_statiscs[wtf['size']] += len(wtf['text'])
+        return max(fsize_statiscs, key=fsize_statiscs.get)

    def ffsize_same(a,b):
        """
@@ -433,11 +432,11 @@ def read_and_clean_pdf_text(fp):

        ############################## <第 2 步，获取正文主字体> ##################################
        try:
-            fsize_statistics = {}
+            fsize_statiscs = {}
            for span in meta_span:
-                if span[1] not in fsize_statistics: fsize_statistics[span[1]] = 0
-                fsize_statistics[span[1]] += span[2]
-            main_fsize = max(fsize_statistics, key=fsize_statistics.get)
+                if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
+                fsize_statiscs[span[1]] += span[2]
+            main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
            if REMOVE_FOOT_NOTE:
                give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
        except:
@@ -610,9 +609,9 @@ class nougat_interface():


    def NOUGAT_parse_pdf(self, fp, chatbot, history):
-        from toolbox import update_ui_latest_msg
+        from toolbox import update_ui_lastest_msg

-        yield from update_ui_latest_msg("正在解析论文, 请稍候。进度：正在排队, 等待线程锁...",
+        yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在排队, 等待线程锁...",
                                         chatbot=chatbot, history=history, delay=0)
        self.threadLock.acquire()
        import glob, threading, os
@@ -620,7 +619,7 @@ class nougat_interface():
        dst = os.path.join(get_log_folder(plugin_name='nougat'), gen_time_str())
        os.makedirs(dst)

-        yield from update_ui_latest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
+        yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
                                         chatbot=chatbot, history=history, delay=0)
        command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
        self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
--- a/crazy_functions/doc_fns/AI_review_doc.py
+++ b/crazy_functions/doc_fns/AI_review_doc.py
@@ -1,812 +0,0 @@
-import os
-import time
-from abc import ABC, abstractmethod
-from datetime import datetime
-from docx import Document
-from docx.enum.style import WD_STYLE_TYPE
-from docx.enum.text import WD_PARAGRAPH_ALIGNMENT, WD_LINE_SPACING
-from docx.oxml.ns import qn
-from docx.shared import  Inches, Cm
-from docx.shared import Pt, RGBColor, Inches
-from typing import Dict, List, Tuple
-import markdown
-from crazy_functions.doc_fns.conversation_doc.word_doc import convert_markdown_to_word
-
-
-
-class DocumentFormatter(ABC):
-    """文档格式化基类，定义文档格式化的基本接口"""
-
-    def __init__(self, final_summary: str, file_summaries_map: Dict, failed_files: List[Tuple]):
-        self.final_summary = final_summary
-        self.file_summaries_map = file_summaries_map
-        self.failed_files = failed_files
-
-    @abstractmethod
-    def format_failed_files(self) -> str:
-        """格式化失败文件列表"""
-        pass
-
-    @abstractmethod
-    def format_file_summaries(self) -> str:
-        """格式化文件总结内容"""
-        pass
-
-    @abstractmethod
-    def create_document(self) -> str:
-        """创建完整文档"""
-        pass
-
-
-class WordFormatter(DocumentFormatter):
-    """Word格式文档生成器 - 符合中国政府公文格式规范(GB/T 9704-2012)，并进行了优化"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.doc = Document()
-        self._setup_document()
-        self._create_styles()
-        # 初始化三级标题编号系统
-        self.numbers = {
-            1: 0,  # 一级标题编号
-            2: 0,  # 二级标题编号
-            3: 0  # 三级标题编号
-        }
-
-    def _setup_document(self):
-        """设置文档基本格式，包括页面设置和页眉"""
-        sections = self.doc.sections
-        for section in sections:
-            # 设置页面大小为A4
-            section.page_width = Cm(21)
-            section.page_height = Cm(29.7)
-            # 设置页边距
-            section.top_margin = Cm(3.7)  # 上边距37mm
-            section.bottom_margin = Cm(3.5)  # 下边距35mm
-            section.left_margin = Cm(2.8)  # 左边距28mm
-            section.right_margin = Cm(2.6)  # 右边距26mm
-            # 设置页眉页脚距离
-            section.header_distance = Cm(2.0)
-            section.footer_distance = Cm(2.0)
-
-            # 添加页眉
-            header = section.header
-            header_para = header.paragraphs[0]
-            header_para.alignment = WD_PARAGRAPH_ALIGNMENT.RIGHT
-            header_run = header_para.add_run("该文档由GPT-academic生成")
-            header_run.font.name = '仿宋'
-            header_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-            header_run.font.size = Pt(9)
-
-    def _create_styles(self):
-        """创建文档样式"""
-        # 创建正文样式
-        style = self.doc.styles.add_style('Normal_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        style.font.name = '仿宋'
-        style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        style.font.size = Pt(14)
-        style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        style.paragraph_format.space_after = Pt(0)
-        style.paragraph_format.first_line_indent = Pt(28)
-
-        # 创建各级标题样式
-        self._create_heading_style('Title_Custom', '方正小标宋简体', 32, WD_PARAGRAPH_ALIGNMENT.CENTER)
-        self._create_heading_style('Heading1_Custom', '黑体', 22, WD_PARAGRAPH_ALIGNMENT.LEFT)
-        self._create_heading_style('Heading2_Custom', '黑体', 18, WD_PARAGRAPH_ALIGNMENT.LEFT)
-        self._create_heading_style('Heading3_Custom', '黑体', 16, WD_PARAGRAPH_ALIGNMENT.LEFT)
-
-    def _create_heading_style(self, style_name: str, font_name: str, font_size: int, alignment):
-        """创建标题样式"""
-        style = self.doc.styles.add_style(style_name, WD_STYLE_TYPE.PARAGRAPH)
-        style.font.name = font_name
-        style._element.rPr.rFonts.set(qn('w:eastAsia'), font_name)
-        style.font.size = Pt(font_size)
-        style.font.bold = True
-        style.paragraph_format.alignment = alignment
-        style.paragraph_format.space_before = Pt(12)
-        style.paragraph_format.space_after = Pt(12)
-        style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        return style
-
-    def _get_heading_number(self, level: int) -> str:
-        """
-        生成标题编号
-
-        Args:
-            level: 标题级别 (0-3)
-
-        Returns:
-            str: 格式化的标题编号
-        """
-        if level == 0:  # 主标题不需要编号
-            return ""
-
-        self.numbers[level] += 1  # 增加当前级别的编号
-
-        # 重置下级标题编号
-        for i in range(level + 1, 4):
-            self.numbers[i] = 0
-
-        # 根据级别返回不同格式的编号
-        if level == 1:
-            return f"{self.numbers[1]}. "
-        elif level == 2:
-            return f"{self.numbers[1]}.{self.numbers[2]} "
-        elif level == 3:
-            return f"{self.numbers[1]}.{self.numbers[2]}.{self.numbers[3]} "
-        return ""
-
-    def _add_heading(self, text: str, level: int):
-        """
-        添加带编号的标题
-
-        Args:
-            text: 标题文本
-            level: 标题级别 (0-3)
-        """
-        style_map = {
-            0: 'Title_Custom',
-            1: 'Heading1_Custom',
-            2: 'Heading2_Custom',
-            3: 'Heading3_Custom'
-        }
-
-        number = self._get_heading_number(level)
-        paragraph = self.doc.add_paragraph(style=style_map[level])
-
-        if number:
-            number_run = paragraph.add_run(number)
-            font_size = 22 if level == 1 else (18 if level == 2 else 16)
-            self._get_run_style(number_run, '黑体', font_size, True)
-
-        text_run = paragraph.add_run(text)
-        font_size = 32 if level == 0 else (22 if level == 1 else (18 if level == 2 else 16))
-        self._get_run_style(text_run, '黑体', font_size, True)
-
-        # 主标题添加日期
-        if level == 0:
-            date_paragraph = self.doc.add_paragraph()
-            date_paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-            date_run = date_paragraph.add_run(datetime.now().strftime('%Y年%m月%d日'))
-            self._get_run_style(date_run, '仿宋', 16, False)
-
-        return paragraph
-
-    def _get_run_style(self, run, font_name: str, font_size: int, bold: bool = False):
-        """设置文本运行对象的样式"""
-        run.font.name = font_name
-        run._element.rPr.rFonts.set(qn('w:eastAsia'), font_name)
-        run.font.size = Pt(font_size)
-        run.font.bold = bold
-
-    def format_failed_files(self) -> str:
-        """格式化失败文件列表"""
-        result = []
-        if not self.failed_files:
-            return "\n".join(result)
-
-        result.append("处理失败文件:")
-        for fp, reason in self.failed_files:
-            result.append(f"• {os.path.basename(fp)}: {reason}")
-
-        self._add_heading("处理失败文件", 1)
-        for fp, reason in self.failed_files:
-            self._add_content(f"• {os.path.basename(fp)}: {reason}", indent=False)
-        self.doc.add_paragraph()
-
-        return "\n".join(result)
-
-    def _add_content(self, text: str, indent: bool = True):
-        """添加正文内容，使用convert_markdown_to_word处理文本"""
-        # 使用convert_markdown_to_word处理markdown文本
-        processed_text = convert_markdown_to_word(text)
-        paragraph = self.doc.add_paragraph(processed_text, style='Normal_Custom')
-        if not indent:
-            paragraph.paragraph_format.first_line_indent = Pt(0)
-        return paragraph
-
-    def format_file_summaries(self) -> str:
-        """
-        格式化文件总结内容，确保正确的标题层级并处理markdown文本
-        """
-        result = []
-        # 首先对文件路径进行分组整理
-        file_groups = {}
-        for path in sorted(self.file_summaries_map.keys()):
-            dir_path = os.path.dirname(path)
-            if dir_path not in file_groups:
-                file_groups[dir_path] = []
-            file_groups[dir_path].append(path)
-
-        # 处理没有目录的文件
-        root_files = file_groups.get("", [])
-        if root_files:
-            for path in sorted(root_files):
-                file_name = os.path.basename(path)
-                result.append(f"\n📄 {file_name}")
-                result.append(self.file_summaries_map[path])
-                # 无目录的文件作为二级标题
-                self._add_heading(f"📄 {file_name}", 2)
-                # 使用convert_markdown_to_word处理文件内容
-                self._add_content(convert_markdown_to_word(self.file_summaries_map[path]))
-                self.doc.add_paragraph()
-
-        # 处理有目录的文件
-        for dir_path in sorted(file_groups.keys()):
-            if dir_path == "":  # 跳过已处理的根目录文件
-                continue
-
-            # 添加目录作为二级标题
-            result.append(f"\n📁 {dir_path}")
-            self._add_heading(f"📁 {dir_path}", 2)
-
-            # 该目录下的所有文件作为三级标题
-            for path in sorted(file_groups[dir_path]):
-                file_name = os.path.basename(path)
-                result.append(f"\n📄 {file_name}")
-                result.append(self.file_summaries_map[path])
-
-                # 添加文件名作为三级标题
-                self._add_heading(f"📄 {file_name}", 3)
-                # 使用convert_markdown_to_word处理文件内容
-                self._add_content(convert_markdown_to_word(self.file_summaries_map[path]))
-                self.doc.add_paragraph()
-
-        return "\n".join(result)
-
-
-    def create_document(self):
-        """创建完整Word文档并返回文档对象"""
-        # 重置所有编号
-        for level in self.numbers:
-            self.numbers[level] = 0
-
-        # 添加主标题
-        self._add_heading("文档总结报告", 0)
-        self.doc.add_paragraph()
-
-        # 添加总体摘要，使用convert_markdown_to_word处理
-        self._add_heading("总体摘要", 1)
-        self._add_content(convert_markdown_to_word(self.final_summary))
-        self.doc.add_paragraph()
-
-        # 添加失败文件列表（如果有）
-        if self.failed_files:
-            self.format_failed_files()
-
-        # 添加文件详细总结
-        self._add_heading("各文件详细总结", 1)
-        self.format_file_summaries()
-
-        return self.doc
-
-    def save_as_pdf(self, word_path, pdf_path=None):
-        """将生成的Word文档转换为PDF
-        
-        参数:
-            word_path: Word文档的路径
-            pdf_path: 可选，PDF文件的输出路径。如果未指定，将使用与Word文档相同的名称和位置
-            
-        返回:
-            生成的PDF文件路径，如果转换失败则返回None
-        """
-        from crazy_functions.doc_fns.conversation_doc.word2pdf import WordToPdfConverter
-        try:
-            pdf_path = WordToPdfConverter.convert_to_pdf(word_path, pdf_path)
-            return pdf_path
-        except Exception as e:
-            print(f"PDF转换失败: {str(e)}")
-            return None
-
-
-class MarkdownFormatter(DocumentFormatter):
-    """Markdown格式文档生成器"""
-
-    def format_failed_files(self) -> str:
-        if not self.failed_files:
-            return ""
-
-        formatted_text = ["\n## ⚠️ 处理失败的文件"]
-        for fp, reason in self.failed_files:
-            formatted_text.append(f"- {os.path.basename(fp)}: {reason}")
-        formatted_text.append("\n---")
-        return "\n".join(formatted_text)
-
-    def format_file_summaries(self) -> str:
-        formatted_text = []
-        sorted_paths = sorted(self.file_summaries_map.keys())
-        current_dir = ""
-
-        for path in sorted_paths:
-            dir_path = os.path.dirname(path)
-            if dir_path != current_dir:
-                if dir_path:
-                    formatted_text.append(f"\n## 📁 {dir_path}")
-                current_dir = dir_path
-
-            file_name = os.path.basename(path)
-            formatted_text.append(f"\n### 📄 {file_name}")
-            formatted_text.append(self.file_summaries_map[path])
-            formatted_text.append("\n---")
-
-        return "\n".join(formatted_text)
-
-    def create_document(self) -> str:
-        document = [
-            "# 📑 文档总结报告",
-            "\n## 总体摘要",
-            self.final_summary
-        ]
-
-        if self.failed_files:
-            document.append(self.format_failed_files())
-
-        document.extend([
-            "\n# 📚 各文件详细总结",
-            self.format_file_summaries()
-        ])
-
-        return "\n".join(document)
-
-
-
-class HtmlFormatter(DocumentFormatter):
-    """HTML格式文档生成器 - 优化版"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.md = markdown.Markdown(extensions=['extra','codehilite', 'tables','nl2br'])
-        self.css_styles = """
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-
-        @keyframes slideIn {
-            from { transform: translateX(-20px); opacity: 0; }
-            to { transform: translateX(0); opacity: 1; }
-        }
-
-        @keyframes pulse {
-            0% { transform: scale(1); }
-            50% { transform: scale(1.05); }
-            100% { transform: scale(1); }
-        }
-
-        :root {
-            /* Enhanced color palette */
-            --primary-color: #2563eb;
-            --primary-light: #eff6ff;
-            --secondary-color: #1e293b;
-            --background-color: #f8fafc;
-            --text-color: #334155;
-            --text-light: #64748b;
-            --border-color: #e2e8f0;
-            --error-color: #ef4444;
-            --error-light: #fef2f2;
-            --success-color: #22c55e;
-            --warning-color: #f59e0b;
-            --card-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
-            --hover-shadow: 0 20px 25px -5px rgb(0 0 0 / 0.1), 0 8px 10px -6px rgb(0 0 0 / 0.1);
-            
-            /* Typography */
-            --heading-font: "Plus Jakarta Sans", system-ui, sans-serif;
-            --body-font: "Inter", system-ui, sans-serif;
-        }
-
-        body {
-            font-family: var(--body-font);
-            line-height: 1.8;
-            max-width: 1200px;
-            margin: 0 auto;
-            padding: 2rem;
-            color: var(--text-color);
-            background-color: var(--background-color);
-            font-size: 16px;
-            -webkit-font-smoothing: antialiased;
-        }
-
-        .container {
-            background: white;
-            padding: 3rem;
-            border-radius: 24px;
-            box-shadow: var(--card-shadow);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            animation: fadeIn 0.6s ease-out;
-            border: 1px solid var(--border-color);
-        }
-
-        .container:hover {
-            box-shadow: var(--hover-shadow);
-            transform: translateY(-2px);
-        }
-
-        h1, h2, h3 {
-            font-family: var(--heading-font);
-            font-weight: 600;
-        }
-
-        h1 {
-            color: var(--primary-color);
-            font-size: 2.8em;
-            text-align: center;
-            margin: 2rem 0 3rem;
-            padding-bottom: 1.5rem;
-            border-bottom: 3px solid var(--primary-color);
-            letter-spacing: -0.03em;
-            position: relative;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            gap: 1rem;
-        }
-
-        h1::after {
-            content: '';
-            position: absolute;
-            bottom: -3px;
-            left: 50%;
-            transform: translateX(-50%);
-            width: 120px;
-            height: 3px;
-            background: linear-gradient(90deg, var(--primary-color), var(--primary-light));
-            border-radius: 3px;
-            transition: width 0.3s ease;
-        }
-
-        h1:hover::after {
-            width: 180px;
-        }
-
-        h2 {
-            color: var(--secondary-color);
-            font-size: 1.9em;
-            margin: 2.5rem 0 1.5rem;
-            padding-left: 1.2rem;
-            border-left: 4px solid var(--primary-color);
-            letter-spacing: -0.02em;
-            display: flex;
-            align-items: center;
-            gap: 1rem;
-            transition: all 0.3s ease;
-        }
-
-        h2:hover {
-            color: var(--primary-color);
-            transform: translateX(5px);
-        }
-
-        h3 {
-            color: var(--text-color);
-            font-size: 1.5em;
-            margin: 2rem 0 1rem;
-            padding-bottom: 0.8rem;
-            border-bottom: 2px solid var(--border-color);
-            transition: all 0.3s ease;
-            display: flex;
-            align-items: center;
-            gap: 0.8rem;
-        }
-
-        h3:hover {
-            color: var(--primary-color);
-            border-bottom-color: var(--primary-color);
-        }
-
-        .summary {
-            background: var(--primary-light);
-            padding: 2.5rem;
-            border-radius: 16px;
-            margin: 2.5rem 0;
-            box-shadow: 0 4px 6px -1px rgba(37, 99, 235, 0.1);
-            position: relative;
-            overflow: hidden;
-            transition: transform 0.3s ease, box-shadow 0.3s ease;
-            animation: slideIn 0.5s ease-out;
-        }
-
-        .summary:hover {
-            transform: translateY(-3px);
-            box-shadow: 0 8px 12px -2px rgba(37, 99, 235, 0.15);
-        }
-
-        .summary::before {
-            content: '';
-            position: absolute;
-            top: 0;
-            left: 0;
-            width: 4px;
-            height: 100%;
-            background: linear-gradient(to bottom, var(--primary-color), rgba(37, 99, 235, 0.6));
-        }
-
-        .summary p {
-            margin: 1.2rem 0;
-            line-height: 1.9;
-            color: var(--text-color);
-            transition: color 0.3s ease;
-        }
-
-        .summary:hover p {
-            color: var(--secondary-color);
-        }
-
-        .details {
-            margin-top: 3.5rem;
-            padding-top: 2.5rem;
-            border-top: 2px dashed var(--border-color);
-            animation: fadeIn 0.8s ease-out;
-        }
-
-        .failed-files {
-            background: var(--error-light);
-            padding: 2rem;
-            border-radius: 16px;
-            margin: 3rem 0;
-            border-left: 4px solid var(--error-color);
-            position: relative;
-            transition: all 0.3s ease;
-            animation: slideIn 0.5s ease-out;
-        }
-
-        .failed-files:hover {
-            transform: translateX(5px);
-            box-shadow: 0 8px 15px -3px rgba(239, 68, 68, 0.1);
-        }
-
-        .failed-files h2 {
-            color: var(--error-color);
-            border-left: none;
-            padding-left: 0;
-        }
-
-        .failed-files ul {
-            margin: 1.8rem 0;
-            padding-left: 1.2rem;
-            list-style-type: none;
-        }
-
-        .failed-files li {
-            margin: 1.2rem 0;
-            padding: 1.2rem 1.8rem;
-            background: rgba(239, 68, 68, 0.08);
-            border-radius: 12px;
-            transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-        }
-
-        .failed-files li:hover {
-            transform: translateX(8px);
-            background: rgba(239, 68, 68, 0.12);
-        }
-
-        .directory-section {
-            margin: 3.5rem 0;
-            padding: 2rem;
-            background: var(--background-color);
-            border-radius: 16px;
-            position: relative;
-            transition: all 0.3s ease;
-            animation: fadeIn 0.6s ease-out;
-        }
-
-        .directory-section:hover {
-            background: white;
-            box-shadow: var(--card-shadow);
-        }
-
-        .file-summary {
-            background: white;
-            padding: 2rem;
-            margin: 1.8rem 0;
-            border-radius: 16px;
-            box-shadow: var(--card-shadow);
-            border-left: 4px solid var(--border-color);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            position: relative;
-            overflow: hidden;
-        }
-
-        .file-summary:hover {
-            border-left-color: var(--primary-color);
-            transform: translateX(8px) translateY(-2px);
-            box-shadow: var(--hover-shadow);
-        }
-
-        .file-summary {
-            background: white;
-            padding: 2rem;
-            margin: 1.8rem 0;
-            border-radius: 16px;
-            box-shadow: var(--card-shadow);
-            border-left: 4px solid var(--border-color);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            position: relative;
-        }
-
-        .file-summary:hover {
-            border-left-color: var(--primary-color);
-            transform: translateX(8px) translateY(-2px);
-            box-shadow: var(--hover-shadow);
-        }
-
-        .icon {
-            display: inline-flex;
-            align-items: center;
-            justify-content: center;
-            width: 32px;
-            height: 32px;
-            border-radius: 8px;
-            background: var(--primary-light);
-            color: var(--primary-color);
-            font-size: 1.2em;
-            transition: all 0.3s ease;
-        }
-
-        .file-summary:hover .icon,
-        .directory-section:hover .icon {
-            transform: scale(1.1);
-            background: var(--primary-color);
-            color: white;
-        }
-
-        /* Smooth scrolling */
-        html {
-            scroll-behavior: smooth;
-        }
-
-        /* Selection style */
-        ::selection {
-            background: var(--primary-light);
-            color: var(--primary-color);
-        }
-
-        /* Print styles */
-        @media print {
-            body {
-                background: white;
-            }
-            .container {
-                box-shadow: none;
-                padding: 0;
-            }
-            .file-summary, .failed-files {
-                break-inside: avoid;
-                box-shadow: none;
-            }
-            .icon {
-                display: none;
-            }
-        }
-
-        /* Responsive design */
-        @media (max-width: 768px) {
-            body {
-                padding: 1rem;
-                font-size: 15px;
-            }
-            
-            .container {
-                padding: 1.5rem;
-            }
-
-            h1 {
-                font-size: 2.2em;
-                margin: 1.5rem 0 2rem;
-            }
-
-            h2 {
-                font-size: 1.7em;
-            }
-
-            h3 {
-                font-size: 1.4em;
-            }
-
-            .summary, .failed-files, .directory-section {
-                padding: 1.5rem;
-            }
-
-            .file-summary {
-                padding: 1.2rem;
-            }
-
-            .icon {
-                width: 28px;
-                height: 28px;
-            }
-        }
-
-        /* Dark mode support */
-        @media (prefers-color-scheme: dark) {
-            :root {
-                --primary-light: rgba(37, 99, 235, 0.15);
-                --background-color: #0f172a;
-                --text-color: #e2e8f0;
-                --text-light: #94a3b8;
-                --border-color: #1e293b;
-                --error-light: rgba(239, 68, 68, 0.15);
-            }
-
-            .container, .file-summary {
-                background: #1e293b;
-            }
-
-            .directory-section {
-                background: #0f172a;
-            }
-
-            .directory-section:hover {
-                background: #1e293b;
-            }
-        }
-        """
-
-    def format_failed_files(self) -> str:
-        if not self.failed_files:
-            return ""
-
-        failed_files_html = ['<div class="failed-files">']
-        failed_files_html.append('<h2><span class="icon">⚠️</span> 处理失败的文件</h2>')
-        failed_files_html.append("<ul>")
-        for fp, reason in self.failed_files:
-            failed_files_html.append(
-                f'<li><strong>📄 {os.path.basename(fp)}</strong><br><span style="color: var(--text-light)">{reason}</span></li>'
-            )
-        failed_files_html.append("</ul></div>")
-        return "\n".join(failed_files_html)
-
-    def format_file_summaries(self) -> str:
-        formatted_html = []
-        sorted_paths = sorted(self.file_summaries_map.keys())
-        current_dir = ""
-
-        for path in sorted_paths:
-            dir_path = os.path.dirname(path)
-            if dir_path != current_dir:
-                if dir_path:
-                    formatted_html.append('<div class="directory-section">')
-                    formatted_html.append(f'<h2><span class="icon">📁</span> {dir_path}</h2>')
-                    formatted_html.append('</div>')
-                current_dir = dir_path
-
-            file_name = os.path.basename(path)
-            formatted_html.append('<div class="file-summary">')
-            formatted_html.append(f'<h3><span class="icon">📄</span> {file_name}</h3>')
-            formatted_html.append(self.md.convert(self.file_summaries_map[path]))
-            formatted_html.append('</div>')
-
-        return "\n".join(formatted_html)
-
-    def create_document(self) -> str:
-        """生成HTML文档
-        Returns:
-            str: 完整的HTML文档字符串
-        """
-        return f"""
-        <!DOCTYPE html>
-        <html lang="zh-CN">
-        <head>
-            <meta charset="utf-8">
-            <meta name="viewport" content="width=device-width, initial-scale=1">
-            <title>文档总结报告</title>
-            <link href="https://cdnjs.cloudflare.com/ajax/libs/inter/3.19.3/inter.css" rel="stylesheet">
-            <link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;600&display=swap" rel="stylesheet">
-            <style>{self.css_styles}</style>
-        </head>
-        <body>
-            <div class="container">
-                <h1><span class="icon">📑</span> 文档总结报告</h1>
-                <div class="summary">
-                    <h2><span class="icon">📋</span> 总体摘要</h2>
-                    <p>{self.md.convert(self.final_summary)}</p>
-                </div>
-                {self.format_failed_files()}
-                <div class="details">
-                    <h2><span class="icon">📚</span> 各文件详细总结</h2>
-                    {self.format_file_summaries()}
-                </div>
-            </div>
-
-        </body>
-        </html>
-        """
--- a/crazy_functions/doc_fns/init.py
+++ b/crazy_functions/doc_fns/init.py
--- a/crazy_functions/doc_fns/batch_file_query_doc.py
+++ b/crazy_functions/doc_fns/batch_file_query_doc.py
@@ -1,812 +0,0 @@
-import os
-import time
-from abc import ABC, abstractmethod
-from datetime import datetime
-from docx import Document
-from docx.enum.style import WD_STYLE_TYPE
-from docx.enum.text import WD_PARAGRAPH_ALIGNMENT, WD_LINE_SPACING
-from docx.oxml.ns import qn
-from docx.shared import  Inches, Cm
-from docx.shared import Pt, RGBColor, Inches
-from typing import Dict, List, Tuple
-import markdown
-from crazy_functions.doc_fns.conversation_doc.word_doc import  convert_markdown_to_word
-
-
-
-class DocumentFormatter(ABC):
-    """文档格式化基类，定义文档格式化的基本接口"""
-
-    def __init__(self, final_summary: str, file_summaries_map: Dict, failed_files: List[Tuple]):
-        self.final_summary = final_summary
-        self.file_summaries_map = file_summaries_map
-        self.failed_files = failed_files
-
-    @abstractmethod
-    def format_failed_files(self) -> str:
-        """格式化失败文件列表"""
-        pass
-
-    @abstractmethod
-    def format_file_summaries(self) -> str:
-        """格式化文件总结内容"""
-        pass
-
-    @abstractmethod
-    def create_document(self) -> str:
-        """创建完整文档"""
-        pass
-
-
-class WordFormatter(DocumentFormatter):
-    """Word格式文档生成器 - 符合中国政府公文格式规范(GB/T 9704-2012)，并进行了优化"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.doc = Document()
-        self._setup_document()
-        self._create_styles()
-        # 初始化三级标题编号系统
-        self.numbers = {
-            1: 0,  # 一级标题编号
-            2: 0,  # 二级标题编号
-            3: 0  # 三级标题编号
-        }
-
-    def _setup_document(self):
-        """设置文档基本格式，包括页面设置和页眉"""
-        sections = self.doc.sections
-        for section in sections:
-            # 设置页面大小为A4
-            section.page_width = Cm(21)
-            section.page_height = Cm(29.7)
-            # 设置页边距
-            section.top_margin = Cm(3.7)  # 上边距37mm
-            section.bottom_margin = Cm(3.5)  # 下边距35mm
-            section.left_margin = Cm(2.8)  # 左边距28mm
-            section.right_margin = Cm(2.6)  # 右边距26mm
-            # 设置页眉页脚距离
-            section.header_distance = Cm(2.0)
-            section.footer_distance = Cm(2.0)
-
-            # 添加页眉
-            header = section.header
-            header_para = header.paragraphs[0]
-            header_para.alignment = WD_PARAGRAPH_ALIGNMENT.RIGHT
-            header_run = header_para.add_run("该文档由GPT-academic生成")
-            header_run.font.name = '仿宋'
-            header_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-            header_run.font.size = Pt(9)
-
-    def _create_styles(self):
-        """创建文档样式"""
-        # 创建正文样式
-        style = self.doc.styles.add_style('Normal_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        style.font.name = '仿宋'
-        style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        style.font.size = Pt(14)
-        style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        style.paragraph_format.space_after = Pt(0)
-        style.paragraph_format.first_line_indent = Pt(28)
-
-        # 创建各级标题样式
-        self._create_heading_style('Title_Custom', '方正小标宋简体', 32, WD_PARAGRAPH_ALIGNMENT.CENTER)
-        self._create_heading_style('Heading1_Custom', '黑体', 22, WD_PARAGRAPH_ALIGNMENT.LEFT)
-        self._create_heading_style('Heading2_Custom', '黑体', 18, WD_PARAGRAPH_ALIGNMENT.LEFT)
-        self._create_heading_style('Heading3_Custom', '黑体', 16, WD_PARAGRAPH_ALIGNMENT.LEFT)
-
-    def _create_heading_style(self, style_name: str, font_name: str, font_size: int, alignment):
-        """创建标题样式"""
-        style = self.doc.styles.add_style(style_name, WD_STYLE_TYPE.PARAGRAPH)
-        style.font.name = font_name
-        style._element.rPr.rFonts.set(qn('w:eastAsia'), font_name)
-        style.font.size = Pt(font_size)
-        style.font.bold = True
-        style.paragraph_format.alignment = alignment
-        style.paragraph_format.space_before = Pt(12)
-        style.paragraph_format.space_after = Pt(12)
-        style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        return style
-
-    def _get_heading_number(self, level: int) -> str:
-        """
-        生成标题编号
-
-        Args:
-            level: 标题级别 (0-3)
-
-        Returns:
-            str: 格式化的标题编号
-        """
-        if level == 0:  # 主标题不需要编号
-            return ""
-
-        self.numbers[level] += 1  # 增加当前级别的编号
-
-        # 重置下级标题编号
-        for i in range(level + 1, 4):
-            self.numbers[i] = 0
-
-        # 根据级别返回不同格式的编号
-        if level == 1:
-            return f"{self.numbers[1]}. "
-        elif level == 2:
-            return f"{self.numbers[1]}.{self.numbers[2]} "
-        elif level == 3:
-            return f"{self.numbers[1]}.{self.numbers[2]}.{self.numbers[3]} "
-        return ""
-
-    def _add_heading(self, text: str, level: int):
-        """
-        添加带编号的标题
-
-        Args:
-            text: 标题文本
-            level: 标题级别 (0-3)
-        """
-        style_map = {
-            0: 'Title_Custom',
-            1: 'Heading1_Custom',
-            2: 'Heading2_Custom',
-            3: 'Heading3_Custom'
-        }
-
-        number = self._get_heading_number(level)
-        paragraph = self.doc.add_paragraph(style=style_map[level])
-
-        if number:
-            number_run = paragraph.add_run(number)
-            font_size = 22 if level == 1 else (18 if level == 2 else 16)
-            self._get_run_style(number_run, '黑体', font_size, True)
-
-        text_run = paragraph.add_run(text)
-        font_size = 32 if level == 0 else (22 if level == 1 else (18 if level == 2 else 16))
-        self._get_run_style(text_run, '黑体', font_size, True)
-
-        # 主标题添加日期
-        if level == 0:
-            date_paragraph = self.doc.add_paragraph()
-            date_paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-            date_run = date_paragraph.add_run(datetime.now().strftime('%Y年%m月%d日'))
-            self._get_run_style(date_run, '仿宋', 16, False)
-
-        return paragraph
-
-    def _get_run_style(self, run, font_name: str, font_size: int, bold: bool = False):
-        """设置文本运行对象的样式"""
-        run.font.name = font_name
-        run._element.rPr.rFonts.set(qn('w:eastAsia'), font_name)
-        run.font.size = Pt(font_size)
-        run.font.bold = bold
-
-    def format_failed_files(self) -> str:
-        """格式化失败文件列表"""
-        result = []
-        if not self.failed_files:
-            return "\n".join(result)
-
-        result.append("处理失败文件:")
-        for fp, reason in self.failed_files:
-            result.append(f"• {os.path.basename(fp)}: {reason}")
-
-        self._add_heading("处理失败文件", 1)
-        for fp, reason in self.failed_files:
-            self._add_content(f"• {os.path.basename(fp)}: {reason}", indent=False)
-        self.doc.add_paragraph()
-
-        return "\n".join(result)
-
-    def _add_content(self, text: str, indent: bool = True):
-        """添加正文内容，使用convert_markdown_to_word处理文本"""
-        # 使用convert_markdown_to_word处理markdown文本
-        processed_text = convert_markdown_to_word(text)
-        paragraph = self.doc.add_paragraph(processed_text, style='Normal_Custom')
-        if not indent:
-            paragraph.paragraph_format.first_line_indent = Pt(0)
-        return paragraph
-
-    def format_file_summaries(self) -> str:
-        """
-        格式化文件总结内容，确保正确的标题层级并处理markdown文本
-        """
-        result = []
-        # 首先对文件路径进行分组整理
-        file_groups = {}
-        for path in sorted(self.file_summaries_map.keys()):
-            dir_path = os.path.dirname(path)
-            if dir_path not in file_groups:
-                file_groups[dir_path] = []
-            file_groups[dir_path].append(path)
-
-        # 处理没有目录的文件
-        root_files = file_groups.get("", [])
-        if root_files:
-            for path in sorted(root_files):
-                file_name = os.path.basename(path)
-                result.append(f"\n📄 {file_name}")
-                result.append(self.file_summaries_map[path])
-                # 无目录的文件作为二级标题
-                self._add_heading(f"📄 {file_name}", 2)
-                # 使用convert_markdown_to_word处理文件内容
-                self._add_content(convert_markdown_to_word(self.file_summaries_map[path]))
-                self.doc.add_paragraph()
-
-        # 处理有目录的文件
-        for dir_path in sorted(file_groups.keys()):
-            if dir_path == "":  # 跳过已处理的根目录文件
-                continue
-
-            # 添加目录作为二级标题
-            result.append(f"\n📁 {dir_path}")
-            self._add_heading(f"📁 {dir_path}", 2)
-
-            # 该目录下的所有文件作为三级标题
-            for path in sorted(file_groups[dir_path]):
-                file_name = os.path.basename(path)
-                result.append(f"\n📄 {file_name}")
-                result.append(self.file_summaries_map[path])
-
-                # 添加文件名作为三级标题
-                self._add_heading(f"📄 {file_name}", 3)
-                # 使用convert_markdown_to_word处理文件内容
-                self._add_content(convert_markdown_to_word(self.file_summaries_map[path]))
-                self.doc.add_paragraph()
-
-        return "\n".join(result)
-
-
-    def create_document(self):
-        """创建完整Word文档并返回文档对象"""
-        # 重置所有编号
-        for level in self.numbers:
-            self.numbers[level] = 0
-
-        # 添加主标题
-        self._add_heading("文档总结报告", 0)
-        self.doc.add_paragraph()
-
-        # 添加总体摘要，使用convert_markdown_to_word处理
-        self._add_heading("总体摘要", 1)
-        self._add_content(convert_markdown_to_word(self.final_summary))
-        self.doc.add_paragraph()
-
-        # 添加失败文件列表（如果有）
-        if self.failed_files:
-            self.format_failed_files()
-
-        # 添加文件详细总结
-        self._add_heading("各文件详细总结", 1)
-        self.format_file_summaries()
-
-        return self.doc
-
-    def save_as_pdf(self, word_path, pdf_path=None):
-        """将生成的Word文档转换为PDF
-        
-        参数:
-            word_path: Word文档的路径
-            pdf_path: 可选，PDF文件的输出路径。如果未指定，将使用与Word文档相同的名称和位置
-            
-        返回:
-            生成的PDF文件路径，如果转换失败则返回None
-        """
-        from crazy_functions.doc_fns.conversation_doc.word2pdf import WordToPdfConverter
-        try:
-            pdf_path = WordToPdfConverter.convert_to_pdf(word_path, pdf_path)
-            return pdf_path
-        except Exception as e:
-            print(f"PDF转换失败: {str(e)}")
-            return None
-
-
-class MarkdownFormatter(DocumentFormatter):
-    """Markdown格式文档生成器"""
-
-    def format_failed_files(self) -> str:
-        if not self.failed_files:
-            return ""
-
-        formatted_text = ["\n## ⚠️ 处理失败的文件"]
-        for fp, reason in self.failed_files:
-            formatted_text.append(f"- {os.path.basename(fp)}: {reason}")
-        formatted_text.append("\n---")
-        return "\n".join(formatted_text)
-
-    def format_file_summaries(self) -> str:
-        formatted_text = []
-        sorted_paths = sorted(self.file_summaries_map.keys())
-        current_dir = ""
-
-        for path in sorted_paths:
-            dir_path = os.path.dirname(path)
-            if dir_path != current_dir:
-                if dir_path:
-                    formatted_text.append(f"\n## 📁 {dir_path}")
-                current_dir = dir_path
-
-            file_name = os.path.basename(path)
-            formatted_text.append(f"\n### 📄 {file_name}")
-            formatted_text.append(self.file_summaries_map[path])
-            formatted_text.append("\n---")
-
-        return "\n".join(formatted_text)
-
-    def create_document(self) -> str:
-        document = [
-            "# 📑 文档总结报告",
-            "\n## 总体摘要",
-            self.final_summary
-        ]
-
-        if self.failed_files:
-            document.append(self.format_failed_files())
-
-        document.extend([
-            "\n# 📚 各文件详细总结",
-            self.format_file_summaries()
-        ])
-
-        return "\n".join(document)
-
-
-
-class HtmlFormatter(DocumentFormatter):
-    """HTML格式文档生成器 - 优化版"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.md = markdown.Markdown(extensions=['extra','codehilite', 'tables','nl2br'])
-        self.css_styles = """
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-
-        @keyframes slideIn {
-            from { transform: translateX(-20px); opacity: 0; }
-            to { transform: translateX(0); opacity: 1; }
-        }
-
-        @keyframes pulse {
-            0% { transform: scale(1); }
-            50% { transform: scale(1.05); }
-            100% { transform: scale(1); }
-        }
-
-        :root {
-            /* Enhanced color palette */
-            --primary-color: #2563eb;
-            --primary-light: #eff6ff;
-            --secondary-color: #1e293b;
-            --background-color: #f8fafc;
-            --text-color: #334155;
-            --text-light: #64748b;
-            --border-color: #e2e8f0;
-            --error-color: #ef4444;
-            --error-light: #fef2f2;
-            --success-color: #22c55e;
-            --warning-color: #f59e0b;
-            --card-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
-            --hover-shadow: 0 20px 25px -5px rgb(0 0 0 / 0.1), 0 8px 10px -6px rgb(0 0 0 / 0.1);
-            
-            /* Typography */
-            --heading-font: "Plus Jakarta Sans", system-ui, sans-serif;
-            --body-font: "Inter", system-ui, sans-serif;
-        }
-
-        body {
-            font-family: var(--body-font);
-            line-height: 1.8;
-            max-width: 1200px;
-            margin: 0 auto;
-            padding: 2rem;
-            color: var(--text-color);
-            background-color: var(--background-color);
-            font-size: 16px;
-            -webkit-font-smoothing: antialiased;
-        }
-
-        .container {
-            background: white;
-            padding: 3rem;
-            border-radius: 24px;
-            box-shadow: var(--card-shadow);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            animation: fadeIn 0.6s ease-out;
-            border: 1px solid var(--border-color);
-        }
-
-        .container:hover {
-            box-shadow: var(--hover-shadow);
-            transform: translateY(-2px);
-        }
-
-        h1, h2, h3 {
-            font-family: var(--heading-font);
-            font-weight: 600;
-        }
-
-        h1 {
-            color: var(--primary-color);
-            font-size: 2.8em;
-            text-align: center;
-            margin: 2rem 0 3rem;
-            padding-bottom: 1.5rem;
-            border-bottom: 3px solid var(--primary-color);
-            letter-spacing: -0.03em;
-            position: relative;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            gap: 1rem;
-        }
-
-        h1::after {
-            content: '';
-            position: absolute;
-            bottom: -3px;
-            left: 50%;
-            transform: translateX(-50%);
-            width: 120px;
-            height: 3px;
-            background: linear-gradient(90deg, var(--primary-color), var(--primary-light));
-            border-radius: 3px;
-            transition: width 0.3s ease;
-        }
-
-        h1:hover::after {
-            width: 180px;
-        }
-
-        h2 {
-            color: var(--secondary-color);
-            font-size: 1.9em;
-            margin: 2.5rem 0 1.5rem;
-            padding-left: 1.2rem;
-            border-left: 4px solid var(--primary-color);
-            letter-spacing: -0.02em;
-            display: flex;
-            align-items: center;
-            gap: 1rem;
-            transition: all 0.3s ease;
-        }
-
-        h2:hover {
-            color: var(--primary-color);
-            transform: translateX(5px);
-        }
-
-        h3 {
-            color: var(--text-color);
-            font-size: 1.5em;
-            margin: 2rem 0 1rem;
-            padding-bottom: 0.8rem;
-            border-bottom: 2px solid var(--border-color);
-            transition: all 0.3s ease;
-            display: flex;
-            align-items: center;
-            gap: 0.8rem;
-        }
-
-        h3:hover {
-            color: var(--primary-color);
-            border-bottom-color: var(--primary-color);
-        }
-
-        .summary {
-            background: var(--primary-light);
-            padding: 2.5rem;
-            border-radius: 16px;
-            margin: 2.5rem 0;
-            box-shadow: 0 4px 6px -1px rgba(37, 99, 235, 0.1);
-            position: relative;
-            overflow: hidden;
-            transition: transform 0.3s ease, box-shadow 0.3s ease;
-            animation: slideIn 0.5s ease-out;
-        }
-
-        .summary:hover {
-            transform: translateY(-3px);
-            box-shadow: 0 8px 12px -2px rgba(37, 99, 235, 0.15);
-        }
-
-        .summary::before {
-            content: '';
-            position: absolute;
-            top: 0;
-            left: 0;
-            width: 4px;
-            height: 100%;
-            background: linear-gradient(to bottom, var(--primary-color), rgba(37, 99, 235, 0.6));
-        }
-
-        .summary p {
-            margin: 1.2rem 0;
-            line-height: 1.9;
-            color: var(--text-color);
-            transition: color 0.3s ease;
-        }
-
-        .summary:hover p {
-            color: var(--secondary-color);
-        }
-
-        .details {
-            margin-top: 3.5rem;
-            padding-top: 2.5rem;
-            border-top: 2px dashed var(--border-color);
-            animation: fadeIn 0.8s ease-out;
-        }
-
-        .failed-files {
-            background: var(--error-light);
-            padding: 2rem;
-            border-radius: 16px;
-            margin: 3rem 0;
-            border-left: 4px solid var(--error-color);
-            position: relative;
-            transition: all 0.3s ease;
-            animation: slideIn 0.5s ease-out;
-        }
-
-        .failed-files:hover {
-            transform: translateX(5px);
-            box-shadow: 0 8px 15px -3px rgba(239, 68, 68, 0.1);
-        }
-
-        .failed-files h2 {
-            color: var(--error-color);
-            border-left: none;
-            padding-left: 0;
-        }
-
-        .failed-files ul {
-            margin: 1.8rem 0;
-            padding-left: 1.2rem;
-            list-style-type: none;
-        }
-
-        .failed-files li {
-            margin: 1.2rem 0;
-            padding: 1.2rem 1.8rem;
-            background: rgba(239, 68, 68, 0.08);
-            border-radius: 12px;
-            transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-        }
-
-        .failed-files li:hover {
-            transform: translateX(8px);
-            background: rgba(239, 68, 68, 0.12);
-        }
-
-        .directory-section {
-            margin: 3.5rem 0;
-            padding: 2rem;
-            background: var(--background-color);
-            border-radius: 16px;
-            position: relative;
-            transition: all 0.3s ease;
-            animation: fadeIn 0.6s ease-out;
-        }
-
-        .directory-section:hover {
-            background: white;
-            box-shadow: var(--card-shadow);
-        }
-
-        .file-summary {
-            background: white;
-            padding: 2rem;
-            margin: 1.8rem 0;
-            border-radius: 16px;
-            box-shadow: var(--card-shadow);
-            border-left: 4px solid var(--border-color);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            position: relative;
-            overflow: hidden;
-        }
-
-        .file-summary:hover {
-            border-left-color: var(--primary-color);
-            transform: translateX(8px) translateY(-2px);
-            box-shadow: var(--hover-shadow);
-        }
-
-        .file-summary {
-            background: white;
-            padding: 2rem;
-            margin: 1.8rem 0;
-            border-radius: 16px;
-            box-shadow: var(--card-shadow);
-            border-left: 4px solid var(--border-color);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            position: relative;
-        }
-
-        .file-summary:hover {
-            border-left-color: var(--primary-color);
-            transform: translateX(8px) translateY(-2px);
-            box-shadow: var(--hover-shadow);
-        }
-
-        .icon {
-            display: inline-flex;
-            align-items: center;
-            justify-content: center;
-            width: 32px;
-            height: 32px;
-            border-radius: 8px;
-            background: var(--primary-light);
-            color: var(--primary-color);
-            font-size: 1.2em;
-            transition: all 0.3s ease;
-        }
-
-        .file-summary:hover .icon,
-        .directory-section:hover .icon {
-            transform: scale(1.1);
-            background: var(--primary-color);
-            color: white;
-        }
-
-        /* Smooth scrolling */
-        html {
-            scroll-behavior: smooth;
-        }
-
-        /* Selection style */
-        ::selection {
-            background: var(--primary-light);
-            color: var(--primary-color);
-        }
-
-        /* Print styles */
-        @media print {
-            body {
-                background: white;
-            }
-            .container {
-                box-shadow: none;
-                padding: 0;
-            }
-            .file-summary, .failed-files {
-                break-inside: avoid;
-                box-shadow: none;
-            }
-            .icon {
-                display: none;
-            }
-        }
-
-        /* Responsive design */
-        @media (max-width: 768px) {
-            body {
-                padding: 1rem;
-                font-size: 15px;
-            }
-            
-            .container {
-                padding: 1.5rem;
-            }
-
-            h1 {
-                font-size: 2.2em;
-                margin: 1.5rem 0 2rem;
-            }
-
-            h2 {
-                font-size: 1.7em;
-            }
-
-            h3 {
-                font-size: 1.4em;
-            }
-
-            .summary, .failed-files, .directory-section {
-                padding: 1.5rem;
-            }
-
-            .file-summary {
-                padding: 1.2rem;
-            }
-
-            .icon {
-                width: 28px;
-                height: 28px;
-            }
-        }
-
-        /* Dark mode support */
-        @media (prefers-color-scheme: dark) {
-            :root {
-                --primary-light: rgba(37, 99, 235, 0.15);
-                --background-color: #0f172a;
-                --text-color: #e2e8f0;
-                --text-light: #94a3b8;
-                --border-color: #1e293b;
-                --error-light: rgba(239, 68, 68, 0.15);
-            }
-
-            .container, .file-summary {
-                background: #1e293b;
-            }
-
-            .directory-section {
-                background: #0f172a;
-            }
-
-            .directory-section:hover {
-                background: #1e293b;
-            }
-        }
-        """
-
-    def format_failed_files(self) -> str:
-        if not self.failed_files:
-            return ""
-
-        failed_files_html = ['<div class="failed-files">']
-        failed_files_html.append('<h2><span class="icon">⚠️</span> 处理失败的文件</h2>')
-        failed_files_html.append("<ul>")
-        for fp, reason in self.failed_files:
-            failed_files_html.append(
-                f'<li><strong>📄 {os.path.basename(fp)}</strong><br><span style="color: var(--text-light)">{reason}</span></li>'
-            )
-        failed_files_html.append("</ul></div>")
-        return "\n".join(failed_files_html)
-
-    def format_file_summaries(self) -> str:
-        formatted_html = []
-        sorted_paths = sorted(self.file_summaries_map.keys())
-        current_dir = ""
-
-        for path in sorted_paths:
-            dir_path = os.path.dirname(path)
-            if dir_path != current_dir:
-                if dir_path:
-                    formatted_html.append('<div class="directory-section">')
-                    formatted_html.append(f'<h2><span class="icon">📁</span> {dir_path}</h2>')
-                    formatted_html.append('</div>')
-                current_dir = dir_path
-
-            file_name = os.path.basename(path)
-            formatted_html.append('<div class="file-summary">')
-            formatted_html.append(f'<h3><span class="icon">📄</span> {file_name}</h3>')
-            formatted_html.append(self.md.convert(self.file_summaries_map[path]))
-            formatted_html.append('</div>')
-
-        return "\n".join(formatted_html)
-
-    def create_document(self) -> str:
-        """生成HTML文档
-        Returns:
-            str: 完整的HTML文档字符串
-        """
-        return f"""
-        <!DOCTYPE html>
-        <html lang="zh-CN">
-        <head>
-            <meta charset="utf-8">
-            <meta name="viewport" content="width=device-width, initial-scale=1">
-            <title>文档总结报告</title>
-            <link href="https://cdnjs.cloudflare.com/ajax/libs/inter/3.19.3/inter.css" rel="stylesheet">
-            <link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;600&display=swap" rel="stylesheet">
-            <style>{self.css_styles}</style>
-        </head>
-        <body>
-            <div class="container">
-                <h1><span class="icon">📑</span> 文档总结报告</h1>
-                <div class="summary">
-                    <h2><span class="icon">📋</span> 总体摘要</h2>
-                    <p>{self.md.convert(self.final_summary)}</p>
-                </div>
-                {self.format_failed_files()}
-                <div class="details">
-                    <h2><span class="icon">📚</span> 各文件详细总结</h2>
-                    {self.format_file_summaries()}
-                </div>
-            </div>
-
-        </body>
-        </html>
-        """
--- a/crazy_functions/doc_fns/content_folder.py
+++ b/crazy_functions/doc_fns/content_folder.py
@@ -1,237 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, Type, TypeVar, Generic, Union
-
-from dataclasses import dataclass
-from enum import Enum, auto
-import logging
-from datetime import datetime
-
-# 设置日志
-logger = logging.getLogger(__name__)
-
-
-# 自定义异常类定义
-class FoldingError(Exception):
-    """折叠相关的自定义异常基类"""
-    pass
-
-
-class FormattingError(FoldingError):
-    """格式化过程中的错误"""
-    pass
-
-
-class MetadataError(FoldingError):
-    """元数据相关的错误"""
-    pass
-
-
-class ValidationError(FoldingError):
-    """验证错误"""
-    pass
-
-
-class FoldingStyle(Enum):
-    """折叠样式枚举"""
-    SIMPLE = auto()  # 简单折叠
-    DETAILED = auto()  # 详细折叠（带有额外信息）
-    NESTED = auto()  # 嵌套折叠
-
-
-@dataclass
-class FoldingOptions:
-    """折叠选项配置"""
-    style: FoldingStyle = FoldingStyle.DETAILED
-    code_language: Optional[str] = None  # 代码块的语言
-    show_timestamp: bool = False  # 是否显示时间戳
-    indent_level: int = 0  # 缩进级别
-    custom_css: Optional[str] = None  # 自定义CSS类
-
-
-T = TypeVar('T')  # 用于泛型类型
-
-
-class BaseMetadata(ABC):
-    """元数据基类"""
-
-    @abstractmethod
-    def validate(self) -> bool:
-        """验证元数据的有效性"""
-        pass
-
-    def _validate_non_empty_str(self, value: Optional[str]) -> bool:
-        """验证字符串非空"""
-        return bool(value and value.strip())
-
-
-@dataclass
-class FileMetadata(BaseMetadata):
-    """文件元数据"""
-    rel_path: str
-    size: float
-    last_modified: Optional[datetime] = None
-    mime_type: Optional[str] = None
-    encoding: str = 'utf-8'
-
-    def validate(self) -> bool:
-        """验证文件元数据的有效性"""
-        try:
-            if not self._validate_non_empty_str(self.rel_path):
-                return False
-            if self.size < 0:
-                return False
-            return True
-        except Exception as e:
-            logger.error(f"File metadata validation error: {str(e)}")
-            return False
-
-
-
-
-class ContentFormatter(ABC, Generic[T]):
-    """内容格式化抽象基类
-
-    支持泛型类型参数，可以指定具体的元数据类型。
-    """
-
-    @abstractmethod
-    def format(self,
-               content: str,
-               metadata: T,
-               options: Optional[FoldingOptions] = None) -> str:
-        """格式化内容
-
-        Args:
-            content: 需要格式化的内容
-            metadata: 类型化的元数据
-            options: 折叠选项
-
-        Returns:
-            str: 格式化后的内容
-
-        Raises:
-            FormattingError: 格式化过程中的错误
-        """
-        pass
-
-    def _create_summary(self, metadata: T) -> str:
-        """创建折叠摘要，可被子类重写"""
-        return str(metadata)
-
-    def _format_content_block(self,
-                              content: str,
-                              options: Optional[FoldingOptions]) -> str:
-        """格式化内容块，处理代码块等特殊格式"""
-        if not options:
-            return content
-
-        if options.code_language:
-            return f"```{options.code_language}\n{content}\n```"
-        return content
-
-    def _add_indent(self, text: str, level: int) -> str:
-        """添加缩进"""
-        if level <= 0:
-            return text
-        indent = "  " * level
-        return "\n".join(indent + line for line in text.splitlines())
-
-
-class FileContentFormatter(ContentFormatter[FileMetadata]):
-    """文件内容格式化器"""
-
-    def format(self,
-               content: str,
-               metadata: FileMetadata,
-               options: Optional[FoldingOptions] = None) -> str:
-        """格式化文件内容"""
-        if not metadata.validate():
-            raise MetadataError("Invalid file metadata")
-
-        try:
-            options = options or FoldingOptions()
-
-            # 构建摘要信息
-            summary_parts = [
-                f"{metadata.rel_path} ({metadata.size:.2f}MB)",
-                f"Type: {metadata.mime_type}" if metadata.mime_type else None,
-                (f"Modified: {metadata.last_modified.strftime('%Y-%m-%d %H:%M:%S')}"
-                 if metadata.last_modified and options.show_timestamp else None)
-            ]
-            summary = " | ".join(filter(None, summary_parts))
-
-            # 构建HTML类
-            css_class = f' class="{options.custom_css}"' if options.custom_css else ''
-
-            # 格式化内容
-            formatted_content = self._format_content_block(content, options)
-
-            # 组装最终结果
-            result = (
-                f'<details{css_class}><summary>{summary}</summary>\n\n'
-                f'{formatted_content}\n\n'
-                f'</details>\n\n'
-            )
-
-            return self._add_indent(result, options.indent_level)
-
-        except Exception as e:
-            logger.error(f"Error formatting file content: {str(e)}")
-            raise FormattingError(f"Failed to format file content: {str(e)}")
-
-
-class ContentFoldingManager:
-    """内容折叠管理器"""
-
-    def __init__(self):
-        """初始化折叠管理器"""
-        self._formatters: Dict[str, ContentFormatter] = {}
-        self._register_default_formatters()
-
-    def _register_default_formatters(self) -> None:
-        """注册默认的格式化器"""
-        self.register_formatter('file', FileContentFormatter())
-
-    def register_formatter(self, name: str, formatter: ContentFormatter) -> None:
-        """注册新的格式化器"""
-        if not isinstance(formatter, ContentFormatter):
-            raise TypeError("Formatter must implement ContentFormatter interface")
-        self._formatters[name] = formatter
-
-    def _guess_language(self, extension: str) -> Optional[str]:
-        """根据文件扩展名猜测编程语言"""
-        extension = extension.lower().lstrip('.')
-        language_map = {
-            'py': 'python',
-            'js': 'javascript',
-            'java': 'java',
-            'cpp': 'cpp',
-            'cs': 'csharp',
-            'html': 'html',
-            'css': 'css',
-            'md': 'markdown',
-            'json': 'json',
-            'xml': 'xml',
-            'sql': 'sql',
-            'sh': 'bash',
-            'yaml': 'yaml',
-            'yml': 'yaml',
-            'txt': None  # 纯文本不需要语言标识
-        }
-        return language_map.get(extension)
-
-    def format_content(self,
-                       content: str,
-                       formatter_type: str,
-                       metadata: Union[FileMetadata],
-                       options: Optional[FoldingOptions] = None) -> str:
-        """格式化内容"""
-        formatter = self._formatters.get(formatter_type)
-        if not formatter:
-            raise KeyError(f"No formatter registered for type: {formatter_type}")
-
-        if not isinstance(metadata, FileMetadata):
-            raise TypeError("Invalid metadata type")
-
-        return formatter.format(content, metadata, options)
-
--- a/crazy_functions/doc_fns/conversation_doc/excel_doc.py
+++ b/crazy_functions/doc_fns/conversation_doc/excel_doc.py
@@ -1,211 +0,0 @@
-import re
-import os
-import pandas as pd
-from datetime import datetime
-from openpyxl import Workbook
-
-
-class ExcelTableFormatter:
-    """聊天记录中Markdown表格转Excel生成器"""
-
-    def __init__(self):
-        """初始化Excel文档对象"""
-        self.workbook = Workbook()
-        self._table_count = 0
-        self._current_sheet = None
-
-    def _normalize_table_row(self, row):
-        """标准化表格行，处理不同的分隔符情况"""
-        row = row.strip()
-        if row.startswith('|'):
-            row = row[1:]
-        if row.endswith('|'):
-            row = row[:-1]
-        return [cell.strip() for cell in row.split('|')]
-
-    def _is_separator_row(self, row):
-        """检查是否是分隔行（由 - 或 : 组成）"""
-        clean_row = re.sub(r'[\s|]', '', row)
-        return bool(re.match(r'^[-:]+$', clean_row))
-
-    def _extract_tables_from_text(self, text):
-        """从文本中提取所有表格内容"""
-        if not isinstance(text, str):
-            return []
-
-        tables = []
-        current_table = []
-        is_in_table = False
-
-        for line in text.split('\n'):
-            line = line.strip()
-            if not line:
-                if is_in_table and current_table:
-                    if len(current_table) >= 2:
-                        tables.append(current_table)
-                    current_table = []
-                    is_in_table = False
-                continue
-
-            if '|' in line:
-                if not is_in_table:
-                    is_in_table = True
-                current_table.append(line)
-            else:
-                if is_in_table and current_table:
-                    if len(current_table) >= 2:
-                        tables.append(current_table)
-                    current_table = []
-                    is_in_table = False
-
-        if is_in_table and current_table and len(current_table) >= 2:
-            tables.append(current_table)
-
-        return tables
-
-    def _parse_table(self, table_lines):
-        """解析表格内容为结构化数据"""
-        try:
-            headers = self._normalize_table_row(table_lines[0])
-
-            separator_index = next(
-                (i for i, line in enumerate(table_lines) if self._is_separator_row(line)),
-                1
-            )
-
-            data_rows = []
-            for line in table_lines[separator_index + 1:]:
-                cells = self._normalize_table_row(line)
-                # 确保单元格数量与表头一致
-                while len(cells) < len(headers):
-                    cells.append('')
-                cells = cells[:len(headers)]
-                data_rows.append(cells)
-
-            if headers and data_rows:
-                return {
-                    'headers': headers,
-                    'data': data_rows
-                }
-        except Exception as e:
-            print(f"解析表格时发生错误: {str(e)}")
-
-        return None
-
-    def _create_sheet(self, question_num, table_num):
-        """创建新的工作表"""
-        sheet_name = f'Q{question_num}_T{table_num}'
-        if len(sheet_name) > 31:
-            sheet_name = f'Table{self._table_count}'
-
-        if sheet_name in self.workbook.sheetnames:
-            sheet_name = f'{sheet_name}_{datetime.now().strftime("%H%M%S")}'
-
-        return self.workbook.create_sheet(title=sheet_name)
-
-    def create_document(self, history):
-        """
-        处理聊天历史中的所有表格并创建Excel文档
-
-        Args:
-            history: 聊天历史列表
-
-        Returns:
-            Workbook: 处理完成的Excel工作簿对象，如果没有表格则返回None
-        """
-        has_tables = False
-
-        # 删除默认创建的工作表
-        default_sheet = self.workbook['Sheet']
-        self.workbook.remove(default_sheet)
-
-        # 遍历所有回答
-        for i in range(1, len(history), 2):
-            answer = history[i]
-            tables = self._extract_tables_from_text(answer)
-
-            for table_lines in tables:
-                parsed_table = self._parse_table(table_lines)
-                if parsed_table:
-                    self._table_count += 1
-                    sheet = self._create_sheet(i // 2 + 1, self._table_count)
-
-                    # 写入表头
-                    for col, header in enumerate(parsed_table['headers'], 1):
-                        sheet.cell(row=1, column=col, value=header)
-
-                    # 写入数据
-                    for row_idx, row_data in enumerate(parsed_table['data'], 2):
-                        for col_idx, value in enumerate(row_data, 1):
-                            sheet.cell(row=row_idx, column=col_idx, value=value)
-
-                    has_tables = True
-
-        return self.workbook if has_tables else None
-
-
-def save_chat_tables(history, save_dir, base_name):
-    """
-    保存聊天历史中的表格到Excel文件
-
-    Args:
-        history: 聊天历史列表
-        save_dir: 保存目录
-        base_name: 基础文件名
-
-    Returns:
-        list: 保存的文件路径列表
-    """
-    result_files = []
-
-    try:
-        # 创建Excel格式
-        excel_formatter = ExcelTableFormatter()
-        workbook = excel_formatter.create_document(history)
-
-        if workbook is not None:
-            # 确保保存目录存在
-            os.makedirs(save_dir, exist_ok=True)
-
-            # 生成Excel文件路径
-            excel_file = os.path.join(save_dir, base_name + '.xlsx')
-
-            # 保存Excel文件
-            workbook.save(excel_file)
-            result_files.append(excel_file)
-            print(f"已保存表格到Excel文件: {excel_file}")
-    except Exception as e:
-        print(f"保存Excel格式失败: {str(e)}")
-
-    return result_files
-
-
-# 使用示例
-if __name__ == "__main__":
-    # 示例聊天历史
-    history = [
-        "问题1",
-        """这是第一个表格：
-        | A | B | C |
-        |---|---|---|
-        | 1 | 2 | 3 |""",
-
-        "问题2",
-        "这是没有表格的回答",
-
-        "问题3",
-        """回答包含多个表格：
-        | Name | Age |
-        |------|-----|
-        | Tom  | 20  |
-
-        第二个表格：
-        | X | Y |
-        |---|---|
-        | 1 | 2 |"""
-    ]
-
-    # 保存表格
-    save_dir = "output"
-    base_name = "chat_tables"
-    saved_files = save_chat_tables(history, save_dir, base_name)
--- a/crazy_functions/doc_fns/conversation_doc/html_doc.py
+++ b/crazy_functions/doc_fns/conversation_doc/html_doc.py
@@ -1,190 +0,0 @@
-
-
-class HtmlFormatter:
-    """聊天记录HTML格式生成器"""
-    
-    def __init__(self, chatbot, history):
-        self.chatbot = chatbot
-        self.history = history
-        self.css_styles = """
-        :root {
-            --primary-color: #2563eb;
-            --primary-light: #eff6ff;
-            --secondary-color: #1e293b;
-            --background-color: #f8fafc;
-            --text-color: #334155;
-            --border-color: #e2e8f0;
-            --card-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
-        }
-
-        body {
-            font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
-            line-height: 1.8;
-            margin: 0;
-            padding: 2rem;
-            color: var(--text-color);
-            background-color: var(--background-color);
-        }
-
-        .container {
-            max-width: 1200px;
-            margin: 0 auto;
-            background: white;
-            padding: 2rem;
-            border-radius: 16px;
-            box-shadow: var(--card-shadow);
-        }
-        ::selection {
-            background: var(--primary-light);
-            color: var(--primary-color);
-        }
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-        
-        @keyframes slideIn {
-            from { transform: translateX(-20px); opacity: 0; }
-            to { transform: translateX(0); opacity: 1; }
-        }
-        
-        .container {
-            animation: fadeIn 0.6s ease-out;
-        }
-        
-        .QaBox {
-            animation: slideIn 0.5s ease-out;
-            transition: all 0.3s ease;
-        }
-        
-        .QaBox:hover {
-            transform: translateX(5px);
-        }
-        .Question, .Answer, .historyBox {
-            transition: all 0.3s ease;
-        }
-        .chat-title {
-            color: var(--primary-color);
-            font-size: 2em;
-            text-align: center;
-            margin: 1rem 0 2rem;
-            padding-bottom: 1rem;
-            border-bottom: 2px solid var(--primary-color);
-        }
-
-        .chat-body {
-            display: flex;
-            flex-direction: column;
-            gap: 1.5rem;
-            margin: 2rem 0;
-        }
-
-        .QaBox {
-            background: white;
-            padding: 1.5rem;
-            border-radius: 8px;
-            border-left: 4px solid var(--primary-color);
-            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-            margin-bottom: 1.5rem;
-        }
-
-        .Question {
-            color: var(--secondary-color);
-            font-weight: 500;
-            margin-bottom: 1rem;
-        }
-
-        .Answer {
-            color: var(--text-color);
-            background: var(--primary-light);
-            padding: 1rem;
-            border-radius: 6px;
-        }
-
-        .history-section {
-            margin-top: 3rem;
-            padding-top: 2rem;
-            border-top: 2px solid var(--border-color);
-        }
-
-        .history-title {
-            color: var(--secondary-color);
-            font-size: 1.5em;
-            margin-bottom: 1.5rem;
-            text-align: center;
-        }
-
-        .historyBox {
-            background: white;
-            padding: 1rem;
-            margin: 0.5rem 0;
-            border-radius: 6px;
-            border: 1px solid var(--border-color);
-        }
-
-        @media (prefers-color-scheme: dark) {
-            :root {
-                --background-color: #0f172a;
-                --text-color: #e2e8f0;
-                --border-color: #1e293b;
-            }
-            
-            .container, .QaBox {
-                background: #1e293b;
-            }
-        }
-        """
-
-    def format_chat_content(self) -> str:
-        """格式化聊天内容"""
-        chat_content = []
-        for q, a in self.chatbot:
-            question = str(q) if q is not None else ""
-            answer = str(a) if a is not None else ""
-            chat_content.append(f'''
-            <div class="QaBox">
-                <div class="Question">{question}</div>
-                <div class="Answer">{answer}</div>
-            </div>
-            ''')
-        return "\n".join(chat_content)
-
-    def format_history_content(self) -> str:
-        """格式化历史记录内容"""
-        if not self.history:
-            return ""
-            
-        history_content = []
-        for entry in self.history:
-            history_content.append(f'''
-            <div class="historyBox">
-                <div class="entry">{entry}</div>
-            </div>
-            ''')
-        return "\n".join(history_content)
-
-    def create_document(self) -> str:
-        """生成完整的HTML文档
-        
-        Returns:
-            str: 完整的HTML文档字符串
-        """
-        return f"""
-        <!DOCTYPE html>
-        <html lang="zh-CN">
-        <head>
-            <meta charset="utf-8">
-            <meta name="viewport" content="width=device-width, initial-scale=1">
-            <title>对话存档</title>
-            <style>{self.css_styles}</style>
-        </head>
-        <body>
-            <div class="container">
-                <h1 class="chat-title">对话存档</h1>
-                <div class="chat-body">
-                    {self.format_chat_content()}
-                </div>
-            </div>
-        </body>
-        </html>
-        """
--- a/crazy_functions/doc_fns/conversation_doc/markdown_doc.py
+++ b/crazy_functions/doc_fns/conversation_doc/markdown_doc.py
@@ -1,39 +0,0 @@
-
-class MarkdownFormatter:
-    """Markdown格式文档生成器 - 用于生成对话记录的markdown文档"""
-
-    def __init__(self):
-        self.content = []
-
-    def _add_content(self, text: str):
-        """添加正文内容"""
-        if text:
-            self.content.append(f"\n{text}\n")
-
-    def create_document(self, history: list) -> str:
-        """
-        创建完整的Markdown文档
-        Args:
-            history: 历史记录列表，偶数位置为问题，奇数位置为答案
-        Returns:
-            str: 生成的Markdown文本
-        """
-        self.content = []
-        
-        # 处理问答对
-        for i in range(0, len(history), 2):
-            question = history[i]
-            answer = history[i + 1]
-            
-            # 添加问题
-            self.content.append(f"\n### 问题 {i//2 + 1}")
-            self._add_content(question)
-            
-            # 添加回答
-            self.content.append(f"\n### 回答 {i//2 + 1}")
-            self._add_content(answer)
-            
-            # 添加分隔线
-            self.content.append("\n---\n")
-
-        return "\n".join(self.content)
--- a/crazy_functions/doc_fns/conversation_doc/pdf_doc.py
+++ b/crazy_functions/doc_fns/conversation_doc/pdf_doc.py
@@ -1,172 +0,0 @@
-from datetime import datetime
-import os
-import re
-from reportlab.pdfbase import pdfmetrics
-from reportlab.pdfbase.ttfonts import TTFont
-
-def convert_markdown_to_pdf(markdown_text):
-    """将Markdown文本转换为PDF格式的纯文本"""
-    if not markdown_text:
-        return ""
-
-    # 标准化换行符
-    markdown_text = markdown_text.replace('\r\n', '\n').replace('\r', '\n')
-
-    # 处理标题、粗体、斜体
-    markdown_text = re.sub(r'^#\s+(.+)$', r'\1', markdown_text, flags=re.MULTILINE)
-    markdown_text = re.sub(r'\*\*(.+?)\*\*', r'\1', markdown_text)
-    markdown_text = re.sub(r'\*(.+?)\*', r'\1', markdown_text)
-
-    # 处理列表
-    markdown_text = re.sub(r'^\s*[-*+]\s+(.+?)(?=\n|$)', r'• \1', markdown_text, flags=re.MULTILINE)
-    markdown_text = re.sub(r'^\s*\d+\.\s+(.+?)(?=\n|$)', r'\1', markdown_text, flags=re.MULTILINE)
-
-    # 处理链接
-    markdown_text = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', r'\1', markdown_text)
-
-    # 处理段落
-    markdown_text = re.sub(r'\n{2,}', '\n', markdown_text)
-    markdown_text = re.sub(r'(?<!\n)(?<!^)(?<!•\s)(?<!\d\.\s)\n(?![\s•\d])', '\n\n', markdown_text, flags=re.MULTILINE)
-
-    # 清理空白
-    markdown_text = re.sub(r' +', ' ', markdown_text)
-    markdown_text = re.sub(r'(?m)^\s+|\s+$', '', markdown_text)
-
-    return markdown_text.strip()
-
-class PDFFormatter:
-    """聊天记录PDF文档生成器 - 使用 Noto Sans CJK 字体"""
-
-    def __init__(self):
-        self._init_reportlab()
-        self._register_fonts()
-        self.styles = self._get_reportlab_lib()['getSampleStyleSheet']()
-        self._create_styles()
-
-    def _init_reportlab(self):
-        """初始化 ReportLab 相关组件"""
-        from reportlab.lib.pagesizes import A4
-        from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-        from reportlab.lib.units import cm
-        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
-        
-        self._lib = {
-            'A4': A4,
-            'getSampleStyleSheet': getSampleStyleSheet,
-            'ParagraphStyle': ParagraphStyle,
-            'cm': cm
-        }
-        
-        self._platypus = {
-            'SimpleDocTemplate': SimpleDocTemplate,
-            'Paragraph': Paragraph,
-            'Spacer': Spacer
-        }
-
-    def _get_reportlab_lib(self):
-        return self._lib
-
-    def _get_reportlab_platypus(self):
-        return self._platypus
-
-    def _register_fonts(self):
-        """注册 Noto Sans CJK 字体"""
-        possible_font_paths = [
-            '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc',
-            '/usr/share/fonts/noto-cjk/NotoSansCJK-Regular.ttc',
-            '/usr/share/fonts/noto/NotoSansCJK-Regular.ttc'
-        ]
-
-        font_registered = False
-        for path in possible_font_paths:
-            if os.path.exists(path):
-                try:
-                    pdfmetrics.registerFont(TTFont('NotoSansCJK', path))
-                    font_registered = True
-                    break
-                except:
-                    continue
-
-        if not font_registered:
-            print("Warning: Could not find Noto Sans CJK font. Using fallback font.")
-            self.font_name = 'Helvetica'
-        else:
-            self.font_name = 'NotoSansCJK'
-
-    def _create_styles(self):
-        """创建文档样式"""
-        ParagraphStyle = self._lib['ParagraphStyle']
-
-        # 标题样式
-        self.styles.add(ParagraphStyle(
-            name='Title_Custom',
-            fontName=self.font_name,
-            fontSize=24,
-            leading=38,
-            alignment=1,
-            spaceAfter=32
-        ))
-
-        # 日期样式
-        self.styles.add(ParagraphStyle(
-            name='Date_Style',
-            fontName=self.font_name,
-            fontSize=16,
-            leading=20,
-            alignment=1,
-            spaceAfter=20
-        ))
-
-        # 问题样式
-        self.styles.add(ParagraphStyle(
-            name='Question_Style',
-            fontName=self.font_name,
-            fontSize=12,
-            leading=18,
-            leftIndent=28,
-            spaceAfter=6
-        ))
-
-        # 回答样式
-        self.styles.add(ParagraphStyle(
-            name='Answer_Style',
-            fontName=self.font_name,
-            fontSize=12,
-            leading=18,
-            leftIndent=28,
-            spaceAfter=12
-        ))
-
-    def create_document(self, history, output_path):
-        """生成PDF文档"""
-        # 创建PDF文档
-        doc = self._platypus['SimpleDocTemplate'](
-            output_path,
-            pagesize=self._lib['A4'],
-            rightMargin=2.6 * self._lib['cm'],
-            leftMargin=2.8 * self._lib['cm'],
-            topMargin=3.7 * self._lib['cm'],
-            bottomMargin=3.5 * self._lib['cm']
-        )
-
-        # 构建内容
-        story = []
-        Paragraph = self._platypus['Paragraph']
-
-        # 添加对话内容
-        for i in range(0, len(history), 2):
-            question = history[i]
-            answer = convert_markdown_to_pdf(history[i + 1]) if i + 1 < len(history) else ""
-
-            if question:
-                q_text = f'问题 {i // 2 + 1}：{str(question)}'
-                story.append(Paragraph(q_text, self.styles['Question_Style']))
-
-            if answer:
-                a_text = f'回答 {i // 2 + 1}：{str(answer)}'
-                story.append(Paragraph(a_text, self.styles['Answer_Style']))
-
-        # 构建PDF
-        doc.build(story)
-
-        return doc
--- a/crazy_functions/doc_fns/conversation_doc/txt_doc.py
+++ b/crazy_functions/doc_fns/conversation_doc/txt_doc.py
@@ -1,79 +0,0 @@
-
-import re
-
-
-def convert_markdown_to_txt(markdown_text):
-    """Convert markdown text to plain text while preserving formatting"""
-    # Standardize line endings
-    markdown_text = markdown_text.replace('\r\n', '\n').replace('\r', '\n')
-
-    # 1. Handle headers but keep their formatting instead of removing them
-    markdown_text = re.sub(r'^#\s+(.+)$', r'# \1', markdown_text, flags=re.MULTILINE)
-    markdown_text = re.sub(r'^##\s+(.+)$', r'## \1', markdown_text, flags=re.MULTILINE)
-    markdown_text = re.sub(r'^###\s+(.+)$', r'### \1', markdown_text, flags=re.MULTILINE)
-
-    # 2. Handle bold and italic - simply remove markers
-    markdown_text = re.sub(r'\*\*(.+?)\*\*', r'\1', markdown_text)
-    markdown_text = re.sub(r'\*(.+?)\*', r'\1', markdown_text)
-
-    # 3. Handle lists but preserve formatting
-    markdown_text = re.sub(r'^\s*[-*+]\s+(.+?)(?=\n|$)', r'• \1', markdown_text, flags=re.MULTILINE)
-
-    # 4. Handle links - keep only the text
-    markdown_text = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', r'\1 (\2)', markdown_text)
-
-    # 5. Handle HTML links - convert to user-friendly format
-    markdown_text = re.sub(r'<a href=[\'"]([^\'"]+)[\'"](?:\s+target=[\'"][^\'"]+[\'"])?>([^<]+)</a>', r'\2 (\1)',
-                           markdown_text)
-
-    # 6. Preserve paragraph breaks
-    markdown_text = re.sub(r'\n{3,}', '\n\n', markdown_text)  # normalize multiple newlines to double newlines
-
-    # 7. Clean up extra spaces but maintain indentation
-    markdown_text = re.sub(r' +', ' ', markdown_text)
-
-    return markdown_text.strip()
-
-
-class TxtFormatter:
-    """Chat history TXT document generator"""
-
-    def __init__(self):
-        self.content = []
-        self._setup_document()
-
-    def _setup_document(self):
-        """Initialize document with header"""
-        self.content.append("=" * 50)
-        self.content.append("GPT-Academic对话记录".center(48))
-        self.content.append("=" * 50)
-
-    def _format_header(self):
-        """Create document header with current date"""
-        from datetime import datetime
-        date_str = datetime.now().strftime('%Y年%m月%d日')
-        return [
-            date_str.center(48),
-            "\n"  # Add blank line after date
-        ]
-
-    def create_document(self, history):
-        """Generate document from chat history"""
-        # Add header with date
-        self.content.extend(self._format_header())
-
-        # Add conversation content
-        for i in range(0, len(history), 2):
-            question = history[i]
-            answer = convert_markdown_to_txt(history[i + 1]) if i + 1 < len(history) else ""
-
-            if question:
-                self.content.append(f"问题 {i // 2 + 1}：{str(question)}")
-                self.content.append("")  # Add blank line
-
-            if answer:
-                self.content.append(f"回答 {i // 2 + 1}：{str(answer)}")
-                self.content.append("")  # Add blank line
-
-        # Join all content with newlines
-        return "\n".join(self.content)
--- a/crazy_functions/doc_fns/conversation_doc/word2pdf.py
+++ b/crazy_functions/doc_fns/conversation_doc/word2pdf.py
@@ -1,155 +0,0 @@
-from docx2pdf import convert
-import os
-import platform
-import subprocess
-from typing import Union
-from pathlib import Path
-from datetime import datetime
-
-class WordToPdfConverter:
-    """Word文档转PDF转换器"""
-    
-    @staticmethod
-    def convert_to_pdf(word_path: Union[str, Path], pdf_path: Union[str, Path] = None) -> str:
-        """
-        将Word文档转换为PDF
-        
-        参数:
-            word_path: Word文档的路径
-            pdf_path: 可选，PDF文件的输出路径。如果未指定，将使用与Word文档相同的名称和位置
-            
-        返回:
-            生成的PDF文件路径
-        
-        异常:
-            如果转换失败，将抛出相应异常
-        """
-        try:
-            # 确保输入路径是Path对象
-            word_path = Path(word_path)
-            
-            # 如果未指定pdf_path，则使用与word文档相同的名称
-            if pdf_path is None:
-                pdf_path = word_path.with_suffix('.pdf')
-            else:
-                pdf_path = Path(pdf_path)
-            
-            # 检查操作系统
-            if platform.system() == 'Linux':
-                # Linux系统需要安装libreoffice
-                which_result = subprocess.run(['which', 'libreoffice'], capture_output=True, text=True)
-                if which_result.returncode != 0:
-                    raise RuntimeError("请先安装LibreOffice: sudo apt-get install libreoffice")
-                
-                print(f"开始转换Word文档: {word_path} 到 PDF")
-                
-                # 使用subprocess代替os.system
-                result = subprocess.run(
-                    ['libreoffice', '--headless', '--convert-to', 'pdf:writer_pdf_Export', 
-                     str(word_path), '--outdir', str(pdf_path.parent)],
-                    capture_output=True, text=True
-                )
-                
-                if result.returncode != 0:
-                    error_msg = result.stderr or "未知错误"
-                    print(f"LibreOffice转换失败，错误信息: {error_msg}")
-                    raise RuntimeError(f"LibreOffice转换失败: {error_msg}")
-                
-                print(f"LibreOffice转换输出: {result.stdout}")
-                
-                # 如果输出路径与默认生成的不同，则重命名
-                default_pdf = word_path.with_suffix('.pdf')
-                if default_pdf != pdf_path and default_pdf.exists():
-                    os.rename(default_pdf, pdf_path)
-                    print(f"已将PDF从 {default_pdf} 重命名为 {pdf_path}")
-                
-                # 验证PDF是否成功生成
-                if not pdf_path.exists() or pdf_path.stat().st_size == 0:
-                    raise RuntimeError("PDF生成失败或文件为空")
-                
-                print(f"PDF转换成功，文件大小: {pdf_path.stat().st_size} 字节")
-            else:
-                # Windows和MacOS使用docx2pdf
-                print(f"使用docx2pdf转换 {word_path} 到 {pdf_path}")
-                convert(word_path, pdf_path)
-                
-                # 验证PDF是否成功生成
-                if not pdf_path.exists() or pdf_path.stat().st_size == 0:
-                    raise RuntimeError("PDF生成失败或文件为空")
-                    
-                print(f"PDF转换成功，文件大小: {pdf_path.stat().st_size} 字节")
-            
-            return str(pdf_path)
-            
-        except Exception as e:
-            print(f"PDF转换异常: {str(e)}")
-            raise Exception(f"转换PDF失败: {str(e)}")
-
-    @staticmethod
-    def batch_convert(word_dir: Union[str, Path], pdf_dir: Union[str, Path] = None) -> list:
-        """
-        批量转换目录下的所有Word文档
-        
-        参数:
-            word_dir: 包含Word文档的目录路径
-            pdf_dir: 可选，PDF文件的输出目录。如果未指定，将使用与Word文档相同的目录
-            
-        返回:
-            生成的PDF文件路径列表
-        """
-        word_dir = Path(word_dir)
-        if pdf_dir:
-            pdf_dir = Path(pdf_dir)
-            pdf_dir.mkdir(parents=True, exist_ok=True)
-            
-        converted_files = []
-        
-        for word_file in word_dir.glob("*.docx"):
-            try:
-                if pdf_dir:
-                    pdf_path = pdf_dir / word_file.with_suffix('.pdf').name
-                else:
-                    pdf_path = word_file.with_suffix('.pdf')
-                    
-                pdf_file = WordToPdfConverter.convert_to_pdf(word_file, pdf_path)
-                converted_files.append(pdf_file)
-                
-            except Exception as e:
-                print(f"转换 {word_file} 失败: {str(e)}")
-                
-        return converted_files
-
-    @staticmethod
-    def convert_doc_to_pdf(doc, output_dir: Union[str, Path] = None) -> str:
-        """
-        将docx对象直接转换为PDF
-        
-        参数:
-            doc: python-docx的Document对象
-            output_dir: 可选，输出目录。如果未指定，将使用当前目录
-            
-        返回:
-            生成的PDF文件路径
-        """
-        try:
-            # 设置临时文件路径和输出路径
-            output_dir = Path(output_dir) if output_dir else Path.cwd()
-            output_dir.mkdir(parents=True, exist_ok=True)
-            
-            # 生成临时word文件
-            temp_docx = output_dir / f"temp_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx"
-            doc.save(temp_docx)
-            
-            # 转换为PDF
-            pdf_path = temp_docx.with_suffix('.pdf')
-            WordToPdfConverter.convert_to_pdf(temp_docx, pdf_path)
-            
-            # 删除临时word文件
-            temp_docx.unlink()
-            
-            return str(pdf_path)
-            
-        except Exception as e:
-            if temp_docx.exists():
-                temp_docx.unlink()
-            raise Exception(f"转换PDF失败: {str(e)}") 
--- a/crazy_functions/doc_fns/conversation_doc/word_doc.py
+++ b/crazy_functions/doc_fns/conversation_doc/word_doc.py
@@ -1,177 +0,0 @@
-import re
-from docx import Document
-from docx.shared import Cm, Pt
-from docx.enum.text import WD_PARAGRAPH_ALIGNMENT, WD_LINE_SPACING
-from docx.enum.style import WD_STYLE_TYPE
-from docx.oxml.ns import qn
-from datetime import datetime
-
-
-def convert_markdown_to_word(markdown_text):
-    # 0. 首先标准化所有换行符为\n
-    markdown_text = markdown_text.replace('\r\n', '\n').replace('\r', '\n')
-
-    # 1. 处理标题 - 支持更多级别的标题，使用更精确的正则
-    # 保留标题标记，以便后续处理时还能识别出标题级别
-    markdown_text = re.sub(r'^(#{1,6})\s+(.+?)(?:\s+#+)?$', r'\1 \2', markdown_text, flags=re.MULTILINE)
-
-    # 2. 处理粗体、斜体和加粗斜体
-    markdown_text = re.sub(r'\*\*\*(.+?)\*\*\*', r'\1', markdown_text)  # 加粗斜体
-    markdown_text = re.sub(r'\*\*(.+?)\*\*', r'\1', markdown_text)  # 加粗
-    markdown_text = re.sub(r'\*(.+?)\*', r'\1', markdown_text)  # 斜体
-    markdown_text = re.sub(r'_(.+?)_', r'\1', markdown_text)  # 下划线斜体
-    markdown_text = re.sub(r'__(.+?)__', r'\1', markdown_text)  # 下划线加粗
-
-    # 3. 处理代码块 - 不移除，而是简化格式
-    # 多行代码块
-    markdown_text = re.sub(r'```(?:\w+)?\n([\s\S]*?)```', r'[代码块]\n\1[/代码块]', markdown_text)
-    # 单行代码
-    markdown_text = re.sub(r'`([^`]+)`', r'[代码]\1[/代码]', markdown_text)
-
-    # 4. 处理列表 - 保留列表结构
-    # 匹配无序列表
-    markdown_text = re.sub(r'^(\s*)[-*+]\s+(.+?)$', r'\1• \2', markdown_text, flags=re.MULTILINE)
-
-    # 5. 处理Markdown链接
-    markdown_text = re.sub(r'\[([^\]]+)\]\(([^)]+?)\s*(?:"[^"]*")?\)', r'\1 (\2)', markdown_text)
-
-    # 6. 处理HTML链接
-    markdown_text = re.sub(r'<a href=[\'"]([^\'"]+)[\'"](?:\s+target=[\'"][^\'"]+[\'"])?>([^<]+)</a>', r'\2 (\1)',
-                           markdown_text)
-
-    # 7. 处理图片
-    markdown_text = re.sub(r'!\[([^\]]*)\]\([^)]+\)', r'[图片：\1]', markdown_text)
-
-    return markdown_text
-
-
-class WordFormatter:
-    """聊天记录Word文档生成器 - 符合中国政府公文格式规范(GB/T 9704-2012)"""
-
-    def __init__(self):
-        self.doc = Document()
-        self._setup_document()
-        self._create_styles()
-
-    def _setup_document(self):
-        """设置文档基本格式，包括页面设置和页眉"""
-        sections = self.doc.sections
-        for section in sections:
-            # 设置页面大小为A4
-            section.page_width = Cm(21)
-            section.page_height = Cm(29.7)
-            # 设置页边距
-            section.top_margin = Cm(3.7)  # 上边距37mm
-            section.bottom_margin = Cm(3.5)  # 下边距35mm
-            section.left_margin = Cm(2.8)  # 左边距28mm
-            section.right_margin = Cm(2.6)  # 右边距26mm
-            # 设置页眉页脚距离
-            section.header_distance = Cm(2.0)
-            section.footer_distance = Cm(2.0)
-
-            # 添加页眉
-            header = section.header
-            header_para = header.paragraphs[0]
-            header_para.alignment = WD_PARAGRAPH_ALIGNMENT.RIGHT
-            header_run = header_para.add_run("GPT-Academic对话记录")
-            header_run.font.name = '仿宋'
-            header_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-            header_run.font.size = Pt(9)
-
-    def _create_styles(self):
-        """创建文档样式"""
-        # 创建正文样式
-        style = self.doc.styles.add_style('Normal_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        style.font.name = '仿宋'
-        style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        style.font.size = Pt(12)  # 调整为12磅
-        style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        style.paragraph_format.space_after = Pt(0)
-
-        # 创建问题样式
-        question_style = self.doc.styles.add_style('Question_Style', WD_STYLE_TYPE.PARAGRAPH)
-        question_style.font.name = '黑体'
-        question_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        question_style.font.size = Pt(14)  # 调整为14磅
-        question_style.font.bold = True
-        question_style.paragraph_format.space_before = Pt(12)  # 减小段前距
-        question_style.paragraph_format.space_after = Pt(6)
-        question_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        question_style.paragraph_format.left_indent = Pt(0)  # 移除左缩进
-
-        # 创建回答样式
-        answer_style = self.doc.styles.add_style('Answer_Style', WD_STYLE_TYPE.PARAGRAPH)
-        answer_style.font.name = '仿宋'
-        answer_style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        answer_style.font.size = Pt(12)  # 调整为12磅
-        answer_style.paragraph_format.space_before = Pt(6)
-        answer_style.paragraph_format.space_after = Pt(12)
-        answer_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        answer_style.paragraph_format.left_indent = Pt(0)  # 移除左缩进
-
-        # 创建标题样式
-        title_style = self.doc.styles.add_style('Title_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        title_style.font.name = '黑体'  # 改用黑体
-        title_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        title_style.font.size = Pt(22)  # 调整为22磅
-        title_style.font.bold = True
-        title_style.paragraph_format.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        title_style.paragraph_format.space_before = Pt(0)
-        title_style.paragraph_format.space_after = Pt(24)
-        title_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-
-        # 添加参考文献样式
-        ref_style = self.doc.styles.add_style('Reference_Style', WD_STYLE_TYPE.PARAGRAPH)
-        ref_style.font.name = '宋体'
-        ref_style._element.rPr.rFonts.set(qn('w:eastAsia'), '宋体')
-        ref_style.font.size = Pt(10.5)  # 参考文献使用小号字体
-        ref_style.paragraph_format.space_before = Pt(3)
-        ref_style.paragraph_format.space_after = Pt(3)
-        ref_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.SINGLE
-        ref_style.paragraph_format.left_indent = Pt(21)
-        ref_style.paragraph_format.first_line_indent = Pt(-21)
-
-        # 添加参考文献标题样式
-        ref_title_style = self.doc.styles.add_style('Reference_Title_Style', WD_STYLE_TYPE.PARAGRAPH)
-        ref_title_style.font.name = '黑体'
-        ref_title_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        ref_title_style.font.size = Pt(16)
-        ref_title_style.font.bold = True
-        ref_title_style.paragraph_format.space_before = Pt(24)
-        ref_title_style.paragraph_format.space_after = Pt(12)
-        ref_title_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-
-    def create_document(self,  history):
-        """写入聊天历史"""
-        # 添加标题
-        title_para = self.doc.add_paragraph(style='Title_Custom')
-        title_run = title_para.add_run('GPT-Academic 对话记录')
-
-        # 添加日期
-        date_para = self.doc.add_paragraph()
-        date_para.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        date_run = date_para.add_run(datetime.now().strftime('%Y年%m月%d日'))
-        date_run.font.name = '仿宋'
-        date_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        date_run.font.size = Pt(16)
-
-        self.doc.add_paragraph()  # 添加空行
-
-        # 添加对话内容
-        for i in range(0, len(history), 2):
-            question = history[i]
-            answer = convert_markdown_to_word(history[i + 1])
-
-            if question:
-                q_para = self.doc.add_paragraph(style='Question_Style')
-                q_para.add_run(f'问题 {i//2 + 1}：').bold = True
-                q_para.add_run(str(question))
-
-            if answer:
-                a_para = self.doc.add_paragraph(style='Answer_Style')
-                a_para.add_run(f'回答 {i//2 + 1}：').bold = True
-                a_para.add_run(str(answer))
-
-
-        return self.doc
-
--- a/crazy_functions/doc_fns/read_fns/init.py
+++ b/crazy_functions/doc_fns/read_fns/init.py
--- a/crazy_functions/doc_fns/read_fns/docx_reader.py
+++ b/crazy_functions/doc_fns/read_fns/docx_reader.py
@@ -1,4 +0,0 @@
-import nltk
-nltk.data.path.append('~/nltk_data')
-nltk.download('averaged_perceptron_tagger', download_dir='~/nltk_data')
-nltk.download('punkt', download_dir='~/nltk_data')
--- a/crazy_functions/doc_fns/read_fns/excel_reader.py
+++ b/crazy_functions/doc_fns/read_fns/excel_reader.py
@@ -1,286 +0,0 @@
-from __future__ import annotations
-
-import pandas as pd
-import numpy as np
-from pathlib import Path
-from typing import Optional, List, Set, Dict, Union, Iterator, Tuple
-from dataclasses import dataclass, field
-import logging
-from concurrent.futures import ThreadPoolExecutor, as_completed
-import chardet
-from functools import lru_cache
-import os
-
-
-@dataclass
-class ExtractorConfig:
-    """提取器配置类"""
-    encoding: str = 'auto'
-    na_filter: bool = True
-    skip_blank_lines: bool = True
-    chunk_size: int = 10000
-    max_workers: int = 4
-    preserve_format: bool = True
-    read_all_sheets: bool = True  # 新增：是否读取所有工作表
-    text_cleanup: Dict[str, bool] = field(default_factory=lambda: {
-        'remove_extra_spaces': True,
-        'normalize_whitespace': False,
-        'remove_special_chars': False,
-        'lowercase': False
-    })
-
-
-class ExcelTextExtractor:
-    """增强的Excel格式文件文本内容提取器"""
-
-    SUPPORTED_EXTENSIONS: Set[str] = {
-        '.xlsx', '.xls', '.csv', '.tsv', '.xlsm', '.xltx', '.xltm', '.ods'
-    }
-
-    def __init__(self, config: Optional[ExtractorConfig] = None):
-        self.config = config or ExtractorConfig()
-        self._setup_logging()
-        self._detect_encoding = lru_cache(maxsize=128)(self._detect_encoding)
-
-    def _setup_logging(self) -> None:
-        """配置日志记录器"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-        )
-        self.logger = logging.getLogger(__name__)
-        fh = logging.FileHandler('excel_extractor.log')
-        fh.setLevel(logging.ERROR)
-        self.logger.addHandler(fh)
-
-    def _detect_encoding(self, file_path: Path) -> str:
-        if self.config.encoding != 'auto':
-            return self.config.encoding
-
-        try:
-            with open(file_path, 'rb') as f:
-                raw_data = f.read(10000)
-                result = chardet.detect(raw_data)
-                return result['encoding'] or 'utf-8'
-        except Exception as e:
-            self.logger.warning(f"Encoding detection failed: {e}. Using utf-8")
-            return 'utf-8'
-
-    def _validate_file(self, file_path: Union[str, Path]) -> Path:
-        path = Path(file_path).resolve()
-
-        if not path.exists():
-            raise ValueError(f"File not found: {path}")
-
-        if not path.is_file():
-            raise ValueError(f"Not a file: {path}")
-
-        if not os.access(path, os.R_OK):
-            raise PermissionError(f"No read permission: {path}")
-
-        if path.suffix.lower() not in self.SUPPORTED_EXTENSIONS:
-            raise ValueError(
-                f"Unsupported format: {path.suffix}. "
-                f"Supported: {', '.join(sorted(self.SUPPORTED_EXTENSIONS))}"
-            )
-
-        return path
-
-    def _format_value(self, value: Any) -> str:
-        if pd.isna(value) or value is None:
-            return ''
-        if isinstance(value, (int, float)):
-            return str(value)
-        return str(value).strip()
-
-    def _process_chunk(self, chunk: pd.DataFrame, columns: Optional[List[str]] = None, sheet_name: str = '') -> str:
-        """处理数据块，新增sheet_name参数"""
-        try:
-            if columns:
-                chunk = chunk[columns]
-
-            if self.config.preserve_format:
-                formatted_chunk = chunk.applymap(self._format_value)
-                rows = []
-
-                # 添加工作表名称作为标题
-                if sheet_name:
-                    rows.append(f"[Sheet: {sheet_name}]")
-
-                # 添加表头
-                headers = [str(col) for col in formatted_chunk.columns]
-                rows.append('\t'.join(headers))
-
-                # 添加数据行
-                for _, row in formatted_chunk.iterrows():
-                    rows.append('\t'.join(row.values))
-
-                return '\n'.join(rows)
-            else:
-                flat_values = (
-                    chunk.astype(str)
-                    .replace({'nan': '', 'None': '', 'NaN': ''})
-                    .values.flatten()
-                )
-                return ' '.join(v for v in flat_values if v)
-
-        except Exception as e:
-            self.logger.error(f"Error processing chunk: {e}")
-            raise
-
-    def _read_file(self, file_path: Path) -> Union[pd.DataFrame, Iterator[pd.DataFrame], Dict[str, pd.DataFrame]]:
-        """读取文件，支持多工作表"""
-        try:
-            encoding = self._detect_encoding(file_path)
-
-            if file_path.suffix.lower() in {'.csv', '.tsv'}:
-                sep = '\t' if file_path.suffix.lower() == '.tsv' else ','
-
-                # 对大文件使用分块读取
-                if file_path.stat().st_size > self.config.chunk_size * 1024:
-                    return pd.read_csv(
-                        file_path,
-                        encoding=encoding,
-                        na_filter=self.config.na_filter,
-                        skip_blank_lines=self.config.skip_blank_lines,
-                        sep=sep,
-                        chunksize=self.config.chunk_size,
-                        on_bad_lines='warn'
-                    )
-                else:
-                    return pd.read_csv(
-                        file_path,
-                        encoding=encoding,
-                        na_filter=self.config.na_filter,
-                        skip_blank_lines=self.config.skip_blank_lines,
-                        sep=sep
-                    )
-            else:
-                # Excel文件处理，支持多工作表
-                if self.config.read_all_sheets:
-                    # 读取所有工作表
-                    return pd.read_excel(
-                        file_path,
-                        na_filter=self.config.na_filter,
-                        keep_default_na=self.config.na_filter,
-                        engine='openpyxl',
-                        sheet_name=None  # None表示读取所有工作表
-                    )
-                else:
-                    # 只读取第一个工作表
-                    return pd.read_excel(
-                        file_path,
-                        na_filter=self.config.na_filter,
-                        keep_default_na=self.config.na_filter,
-                        engine='openpyxl',
-                        sheet_name=0  # 读取第一个工作表
-                    )
-
-        except Exception as e:
-            self.logger.error(f"Error reading file {file_path}: {e}")
-            raise
-
-    def extract_text(
-            self,
-            file_path: Union[str, Path],
-            columns: Optional[List[str]] = None,
-            separator: str = '\n'
-    ) -> str:
-        """提取文本，支持多工作表"""
-        try:
-            path = self._validate_file(file_path)
-            self.logger.info(f"Processing: {path}")
-
-            reader = self._read_file(path)
-            texts = []
-
-            # 处理Excel多工作表
-            if isinstance(reader, dict):
-                for sheet_name, df in reader.items():
-                    sheet_text = self._process_chunk(df, columns, sheet_name)
-                    if sheet_text:
-                        texts.append(sheet_text)
-                return separator.join(texts)
-
-            # 处理单个DataFrame
-            elif isinstance(reader, pd.DataFrame):
-                return self._process_chunk(reader, columns)
-
-            # 处理DataFrame迭代器
-            else:
-                with ThreadPoolExecutor(max_workers=self.config.max_workers) as executor:
-                    futures = {
-                        executor.submit(self._process_chunk, chunk, columns): i
-                        for i, chunk in enumerate(reader)
-                    }
-
-                    chunk_texts = []
-                    for future in as_completed(futures):
-                        try:
-                            text = future.result()
-                            if text:
-                                chunk_texts.append((futures[future], text))
-                        except Exception as e:
-                            self.logger.error(f"Error in chunk {futures[future]}: {e}")
-
-                    # 按块的顺序排序
-                    chunk_texts.sort(key=lambda x: x[0])
-                    texts = [text for _, text in chunk_texts]
-
-                # 合并文本，保留格式
-                if texts and self.config.preserve_format:
-                    result = texts[0]  # 第一块包含表头
-                    if len(texts) > 1:
-                        # 跳过后续块的表头行
-                        for text in texts[1:]:
-                            result += '\n' + '\n'.join(text.split('\n')[1:])
-                    return result
-                else:
-                    return separator.join(texts)
-
-        except Exception as e:
-            self.logger.error(f"Extraction failed: {e}")
-            raise
-
-    @staticmethod
-    def get_supported_formats() -> List[str]:
-        """获取支持的文件格式列表"""
-        return sorted(ExcelTextExtractor.SUPPORTED_EXTENSIONS)
-
-
-def main():
-    """主函数：演示用法"""
-    config = ExtractorConfig(
-        encoding='auto',
-        preserve_format=True,
-        read_all_sheets=True,  # 启用多工作表读取
-        text_cleanup={
-            'remove_extra_spaces': True,
-            'normalize_whitespace': False,
-            'remove_special_chars': False,
-            'lowercase': False
-        }
-    )
-
-    extractor = ExcelTextExtractor(config)
-
-    try:
-        sample_file = 'example.xlsx'
-        if Path(sample_file).exists():
-            text = extractor.extract_text(
-                sample_file,
-                columns=['title', 'content']
-            )
-            print("提取的文本:")
-            print(text)
-        else:
-            print(f"示例文件 {sample_file} 不存在")
-
-        print("\n支持的格式:", extractor.get_supported_formats())
-
-    except Exception as e:
-        print(f"错误: {e}")
-
-
-if __name__ == "__main__":
-    main()
--- a/crazy_functions/doc_fns/read_fns/markitdown/markdown_reader.py
+++ b/crazy_functions/doc_fns/read_fns/markitdown/markdown_reader.py
@@ -1,359 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Optional, Set, Dict, Union, List
-from dataclasses import dataclass, field
-import logging
-import os
-import re
-import subprocess
-import tempfile
-import shutil
-
-@dataclass
-class MarkdownConverterConfig:
-    """PDF 到 Markdown 转换器配置类
-
-    Attributes:
-        extract_images: 是否提取图片
-        extract_tables: 是否尝试保留表格结构
-        extract_code_blocks: 是否识别代码块
-        extract_math: 是否转换数学公式
-        output_dir: 输出目录路径
-        image_dir: 图片保存目录路径
-        paragraph_separator: 段落之间的分隔符
-        text_cleanup: 文本清理选项字典
-        docintel_endpoint: Document Intelligence端点URL (可选)
-        enable_plugins: 是否启用插件
-        llm_client: LLM客户端对象 (例如OpenAI client)
-        llm_model: 要使用的LLM模型名称
-    """
-    extract_images: bool = True
-    extract_tables: bool = True
-    extract_code_blocks: bool = True
-    extract_math: bool = True
-    output_dir: str = ""
-    image_dir: str = "images"
-    paragraph_separator: str = '\n\n'
-    text_cleanup: Dict[str, bool] = field(default_factory=lambda: {
-        'remove_extra_spaces': True,
-        'normalize_whitespace': True,
-        'remove_special_chars': False,
-        'lowercase': False
-    })
-    docintel_endpoint: str = ""
-    enable_plugins: bool = False
-    llm_client: Optional[object] = None
-    llm_model: str = ""
-
-
-class MarkdownConverter:
-    """PDF 到 Markdown 转换器
-
-    使用 markitdown 库实现 PDF 到 Markdown 的转换，支持多种配置选项。
-    """
-
-    SUPPORTED_EXTENSIONS: Set[str] = {
-        '.pdf',
-    }
-
-    def __init__(self, config: Optional[MarkdownConverterConfig] = None):
-        """初始化转换器
-
-        Args:
-            config: 转换器配置对象，如果为None则使用默认配置
-        """
-        self.config = config or MarkdownConverterConfig()
-        self._setup_logging()
-        
-        # 检查是否安装了 markitdown
-        self._check_markitdown_installation()
-
-    def _setup_logging(self) -> None:
-        """配置日志记录器"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-        )
-        self.logger = logging.getLogger(__name__)
-
-        # 添加文件处理器
-        fh = logging.FileHandler('markdown_converter.log')
-        fh.setLevel(logging.ERROR)
-        self.logger.addHandler(fh)
-    
-    def _check_markitdown_installation(self) -> None:
-        """检查是否安装了 markitdown"""
-        try:
-            # 尝试导入 markitdown 库
-            from markitdown import MarkItDown
-            self.logger.info("markitdown 库已安装")
-        except ImportError:
-            self.logger.warning("markitdown 库未安装，尝试安装...")
-            try:
-                subprocess.check_call(["pip", "install", "markitdown"])
-                self.logger.info("markitdown 库安装成功")
-                from markitdown import MarkItDown
-            except (subprocess.SubprocessError, ImportError):
-                self.logger.error("无法安装 markitdown 库，请手动安装")
-                self.markitdown_available = False
-                return
-        
-        self.markitdown_available = True
-
-    def _validate_file(self, file_path: Union[str, Path], max_size_mb: int = 100) -> Path:
-        """验证文件
-
-        Args:
-            file_path: 文件路径
-            max_size_mb: 允许的最大文件大小(MB)
-
-        Returns:
-            Path: 验证后的Path对象
-
-        Raises:
-            ValueError: 文件不存在、格式不支持或大小超限
-            PermissionError: 没有读取权限
-        """
-        path = Path(file_path).resolve()
-
-        if not path.exists():
-            raise ValueError(f"文件不存在: {path}")
-
-        if not path.is_file():
-            raise ValueError(f"不是一个文件: {path}")
-
-        if not os.access(path, os.R_OK):
-            raise PermissionError(f"没有读取权限: {path}")
-
-        file_size_mb = path.stat().st_size / (1024 * 1024)
-        if file_size_mb > max_size_mb:
-            raise ValueError(
-                f"文件大小 ({file_size_mb:.1f}MB) 超过限制 {max_size_mb}MB"
-            )
-
-        if path.suffix.lower() not in self.SUPPORTED_EXTENSIONS:
-            raise ValueError(
-                f"不支持的格式: {path.suffix}. "
-                f"支持的格式: {', '.join(sorted(self.SUPPORTED_EXTENSIONS))}"
-            )
-
-        return path
-
-    def _cleanup_text(self, text: str) -> str:
-        """清理文本
-
-        Args:
-            text: 原始文本
-
-        Returns:
-            str: 清理后的文本
-        """
-        if self.config.text_cleanup['remove_extra_spaces']:
-            text = ' '.join(text.split())
-
-        if self.config.text_cleanup['normalize_whitespace']:
-            text = text.replace('\t', ' ').replace('\r', '\n')
-
-        if self.config.text_cleanup['lowercase']:
-            text = text.lower()
-
-        return text.strip()
-
-    @staticmethod
-    def get_supported_formats() -> List[str]:
-        """获取支持的文件格式列表"""
-        return sorted(MarkdownConverter.SUPPORTED_EXTENSIONS)
-
-    def convert_to_markdown(
-            self,
-            file_path: Union[str, Path],
-            output_path: Optional[Union[str, Path]] = None
-    ) -> str:
-        """将 PDF 转换为 Markdown
-
-        Args:
-            file_path: PDF 文件路径
-            output_path: 输出 Markdown 文件路径，如果为 None 则返回内容而不保存
-
-        Returns:
-            str: 转换后的 Markdown 内容
-
-        Raises:
-            Exception: 转换过程中的错误
-        """
-        try:
-            path = self._validate_file(file_path)
-            self.logger.info(f"处理: {path}")
-
-            if not self.markitdown_available:
-                raise ImportError("markitdown 库未安装，无法进行转换")
-
-            # 导入 markitdown 库
-            from markitdown import MarkItDown
-
-            # 准备输出目录
-            if output_path:
-                output_path = Path(output_path)
-                output_dir = output_path.parent
-                output_dir.mkdir(parents=True, exist_ok=True)
-            else:
-                # 创建临时目录作为输出目录
-                temp_dir = tempfile.mkdtemp()
-                output_dir = Path(temp_dir)
-                output_path = output_dir / f"{path.stem}.md"
-
-            # 图片目录
-            image_dir = output_dir / self.config.image_dir
-            image_dir.mkdir(parents=True, exist_ok=True)
-
-            # 创建 MarkItDown 实例并进行转换
-            if self.config.docintel_endpoint:
-                md = MarkItDown(docintel_endpoint=self.config.docintel_endpoint)
-            elif self.config.llm_client and self.config.llm_model:
-                md = MarkItDown(
-                    enable_plugins=self.config.enable_plugins,
-                    llm_client=self.config.llm_client,
-                    llm_model=self.config.llm_model
-                )
-            else:
-                md = MarkItDown(enable_plugins=self.config.enable_plugins)
-            
-            # 执行转换
-            result = md.convert(str(path))
-            markdown_content = result.text_content
-            
-            # 清理文本
-            markdown_content = self._cleanup_text(markdown_content)
-            
-            # 如果需要保存到文件
-            if output_path:
-                with open(output_path, 'w', encoding='utf-8') as f:
-                    f.write(markdown_content)
-                self.logger.info(f"转换成功，输出到: {output_path}")
-            
-            return markdown_content
-
-        except Exception as e:
-            self.logger.error(f"转换失败: {e}")
-            raise
-        finally:
-            # 如果使用了临时目录且没有指定输出路径，则清理临时目录
-            if 'temp_dir' in locals() and not output_path:
-                shutil.rmtree(temp_dir, ignore_errors=True)
-
-    def convert_to_markdown_and_save(
-            self,
-            file_path: Union[str, Path],
-            output_path: Union[str, Path]
-    ) -> Path:
-        """将 PDF 转换为 Markdown 并保存到指定路径
-
-        Args:
-            file_path: PDF 文件路径
-            output_path: 输出 Markdown 文件路径
-
-        Returns:
-            Path: 输出文件的 Path 对象
-
-        Raises:
-            Exception: 转换过程中的错误
-        """
-        self.convert_to_markdown(file_path, output_path)
-        return Path(output_path)
-    
-    def batch_convert(
-            self, 
-            file_paths: List[Union[str, Path]], 
-            output_dir: Union[str, Path]
-    ) -> List[Path]:
-        """批量转换多个 PDF 文件为 Markdown
-
-        Args:
-            file_paths: PDF 文件路径列表
-            output_dir: 输出目录路径
-
-        Returns:
-            List[Path]: 输出文件路径列表
-
-        Raises:
-            Exception: 转换过程中的错误
-        """
-        output_dir = Path(output_dir)
-        output_dir.mkdir(parents=True, exist_ok=True)
-        
-        output_paths = []
-        for file_path in file_paths:
-            path = Path(file_path)
-            output_path = output_dir / f"{path.stem}.md"
-            
-            try:
-                self.convert_to_markdown(file_path, output_path)
-                output_paths.append(output_path)
-                self.logger.info(f"成功转换: {path} -> {output_path}")
-            except Exception as e:
-                self.logger.error(f"转换失败 {path}: {e}")
-        
-        return output_paths
-
-
-def main():
-    """主函数：演示用法"""
-    # 配置
-    config = MarkdownConverterConfig(
-        extract_images=True,
-        extract_tables=True,
-        extract_code_blocks=True,
-        extract_math=True,
-        enable_plugins=False,
-        text_cleanup={
-            'remove_extra_spaces': True,
-            'normalize_whitespace': True,
-            'remove_special_chars': False,
-            'lowercase': False
-        }
-    )
-
-    # 创建转换器
-    converter = MarkdownConverter(config)
-
-    # 使用示例
-    try:
-        # 替换为实际的文件路径
-        sample_file = './crazy_functions/doc_fns/read_fns/paper/2501.12599v1.pdf'
-        if Path(sample_file).exists():
-            # 转换为 Markdown 并打印内容
-            markdown_content = converter.convert_to_markdown(sample_file)
-            print("转换后的 Markdown 内容:")
-            print(markdown_content[:500] + "...")  # 只打印前500个字符
-            
-            # 转换并保存到文件
-            output_file = f"./output_{Path(sample_file).stem}.md"
-            output_path = converter.convert_to_markdown_and_save(sample_file, output_file)
-            print(f"\n已保存到: {output_path}")
-            
-            # 使用LLM增强的示例 (需要添加相应的导入和配置)
-            # try:
-            #     from openai import OpenAI
-            #     client = OpenAI()
-            #     llm_config = MarkdownConverterConfig(
-            #         llm_client=client,
-            #         llm_model="gpt-4o"
-            #     )
-            #     llm_converter = MarkdownConverter(llm_config)
-            #     llm_result = llm_converter.convert_to_markdown("example.jpg")
-            #     print("LLM增强的结果:")
-            #     print(llm_result[:500] + "...")
-            # except ImportError:
-            #     print("未安装OpenAI库，跳过LLM示例")
-        else:
-            print(f"示例文件 {sample_file} 不存在")
-
-        print("\n支持的格式:", converter.get_supported_formats())
-
-    except Exception as e:
-        print(f"错误: {e}")
-
-
-if __name__ == "__main__":
-    main() 
--- a/crazy_functions/doc_fns/read_fns/unstructured_all/init.py
+++ b/crazy_functions/doc_fns/read_fns/unstructured_all/init.py
--- a/crazy_functions/doc_fns/read_fns/unstructured_all/paper_metadata_extractor.py
+++ b/crazy_functions/doc_fns/read_fns/unstructured_all/paper_metadata_extractor.py
@@ -1,493 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Optional, Set, Dict, Union, List
-from dataclasses import dataclass, field
-import logging
-import os
-import re
-
-from unstructured.partition.auto import partition
-from unstructured.documents.elements import (
-    Text, Title, NarrativeText, ListItem, Table,
-    Footer, Header, PageBreak, Image, Address
-)
-
-
-@dataclass
-class PaperMetadata:
-    """论文元数据类"""
-    title: str = ""
-    authors: List[str] = field(default_factory=list)
-    affiliations: List[str] = field(default_factory=list)
-    journal: str = ""
-    volume: str = ""
-    issue: str = ""
-    year: str = ""
-    doi: str = ""
-    date: str = ""
-    publisher: str = ""
-    conference: str = ""
-    abstract: str = ""
-    keywords: List[str] = field(default_factory=list)
-
-
-@dataclass
-class ExtractorConfig:
-    """元数据提取器配置类"""
-    paragraph_separator: str = '\n\n'
-    text_cleanup: Dict[str, bool] = field(default_factory=lambda: {
-        'remove_extra_spaces': True,
-        'normalize_whitespace': True,
-        'remove_special_chars': False,
-        'lowercase': False
-    })
-
-
-class PaperMetadataExtractor:
-    """论文元数据提取器
-    
-    使用unstructured库从多种文档格式中提取论文的标题、作者、摘要等元数据信息。
-    """
-
-    SUPPORTED_EXTENSIONS: Set[str] = {
-        '.pdf', '.docx', '.doc', '.txt', '.ppt', '.pptx',
-        '.xlsx', '.xls', '.md', '.org', '.odt', '.rst',
-        '.rtf', '.epub', '.html', '.xml', '.json'
-    }
-
-    # 定义论文各部分的关键词模式
-    SECTION_PATTERNS = {
-        'abstract': r'\b(摘要|abstract|summary|概要|résumé|zusammenfassung|аннотация)\b',
-        'keywords': r'\b(关键词|keywords|key\s+words|关键字|mots[- ]clés|schlüsselwörter|ключевые слова)\b',
-    }
-
-    def __init__(self, config: Optional[ExtractorConfig] = None):
-        """初始化提取器
-        
-        Args:
-            config: 提取器配置对象，如果为None则使用默认配置
-        """
-        self.config = config or ExtractorConfig()
-        self._setup_logging()
-
-    def _setup_logging(self) -> None:
-        """配置日志记录器"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-        )
-        self.logger = logging.getLogger(__name__)
-
-        # 添加文件处理器
-        fh = logging.FileHandler('paper_metadata_extractor.log')
-        fh.setLevel(logging.ERROR)
-        self.logger.addHandler(fh)
-
-    def _validate_file(self, file_path: Union[str, Path], max_size_mb: int = 100) -> Path:
-        """验证文件
-        
-        Args:
-            file_path: 文件路径
-            max_size_mb: 允许的最大文件大小(MB)
-            
-        Returns:
-            Path: 验证后的Path对象
-            
-        Raises:
-            ValueError: 文件不存在、格式不支持或大小超限
-            PermissionError: 没有读取权限
-        """
-        path = Path(file_path).resolve()
-
-        if not path.exists():
-            raise ValueError(f"文件不存在: {path}")
-
-        if not path.is_file():
-            raise ValueError(f"不是文件: {path}")
-
-        if not os.access(path, os.R_OK):
-            raise PermissionError(f"没有读取权限: {path}")
-
-        file_size_mb = path.stat().st_size / (1024 * 1024)
-        if file_size_mb > max_size_mb:
-            raise ValueError(
-                f"文件大小 ({file_size_mb:.1f}MB) 超过限制 {max_size_mb}MB"
-            )
-
-        if path.suffix.lower() not in self.SUPPORTED_EXTENSIONS:
-            raise ValueError(
-                f"不支持的文件格式: {path.suffix}. "
-                f"支持的格式: {', '.join(sorted(self.SUPPORTED_EXTENSIONS))}"
-            )
-
-        return path
-
-    def _cleanup_text(self, text: str) -> str:
-        """清理文本
-        
-        Args:
-            text: 原始文本
-            
-        Returns:
-            str: 清理后的文本
-        """
-        if self.config.text_cleanup['remove_extra_spaces']:
-            text = ' '.join(text.split())
-
-        if self.config.text_cleanup['normalize_whitespace']:
-            text = text.replace('\t', ' ').replace('\r', '\n')
-
-        if self.config.text_cleanup['lowercase']:
-            text = text.lower()
-
-        return text.strip()
-
-    @staticmethod
-    def get_supported_formats() -> List[str]:
-        """获取支持的文件格式列表"""
-        return sorted(PaperMetadataExtractor.SUPPORTED_EXTENSIONS)
-
-    def extract_metadata(self, file_path: Union[str, Path], strategy: str = "fast") -> PaperMetadata:
-        """提取论文元数据
-        
-        Args:
-            file_path: 文件路径
-            strategy: 提取策略 ("fast" 或 "accurate")
-            
-        Returns:
-            PaperMetadata: 提取的论文元数据
-            
-        Raises:
-            Exception: 提取过程中的错误
-        """
-        try:
-            path = self._validate_file(file_path)
-            self.logger.info(f"正在处理: {path}")
-
-            # 使用unstructured库分解文档
-            elements = partition(
-                str(path),
-                strategy=strategy,
-                include_metadata=True,
-                nlp=False,
-            )
-
-            # 提取元数据
-            metadata = PaperMetadata()
-            
-            # 提取标题和作者
-            self._extract_title_and_authors(elements, metadata)
-            
-            # 提取摘要和关键词
-            self._extract_abstract_and_keywords(elements, metadata)
-            
-            # 提取其他元数据
-            self._extract_additional_metadata(elements, metadata)
-            
-            return metadata
-
-        except Exception as e:
-            self.logger.error(f"元数据提取失败: {e}")
-            raise
-
-    def _extract_title_and_authors(self, elements, metadata: PaperMetadata) -> None:
-        """从文档中提取标题和作者信息 - 改进版"""
-        # 收集所有潜在的标题候选
-        title_candidates = []
-        all_text = []
-        raw_text = []
-        
-        # 首先收集文档前30个元素的文本，用于辅助判断
-        for i, element in enumerate(elements[:30]):
-            if isinstance(element, (Text, Title, NarrativeText)):
-                text = str(element).strip()
-                if text:
-                    all_text.append(text)
-                    raw_text.append(text)
-        
-        # 打印出原始文本，用于调试
-        print("原始文本前10行:")
-        for i, text in enumerate(raw_text[:10]):
-            print(f"{i}: {text}")
-        
-        # 1. 尝试查找连续的标题片段并合并它们
-        i = 0
-        while i < len(all_text) - 1:
-            current = all_text[i]
-            next_text = all_text[i + 1]
-            
-            # 检查是否存在标题分割情况：一行以冒号结尾，下一行像是标题的延续
-            if current.endswith(':') and len(current) < 50 and len(next_text) > 5 and next_text[0].isupper():
-                # 合并这两行文本
-                combined_title = f"{current} {next_text}"
-                # 查找合并前的文本并替换
-                all_text[i] = combined_title
-                all_text.pop(i + 1)
-                # 给合并后的标题很高的分数
-                title_candidates.append((combined_title, 15, i))
-            else:
-                i += 1
-        
-        # 2. 首先尝试从标题元素中查找
-        for i, element in enumerate(elements[:15]):  # 只检查前15个元素
-            if isinstance(element, Title):
-                title_text = str(element).strip()
-                # 排除常见的非标题内容
-                if title_text.lower() not in ['abstract', '摘要', 'introduction', '引言']:
-                    # 计算标题分数（越高越可能是真正的标题）
-                    score = self._evaluate_title_candidate(title_text, i, element)
-                    title_candidates.append((title_text, score, i))
-        
-        # 3. 特别处理常见的论文标题格式
-        for i, text in enumerate(all_text[:15]):
-            # 特别检查"KIMI K1.5:"类型的前缀标题
-            if re.match(r'^[A-Z][A-Z0-9\s\.]+(\s+K\d+(\.\d+)?)?:', text):
-                score = 12  # 给予很高的分数
-                title_candidates.append((text, score, i))
-                
-                # 如果下一行也是全大写，很可能是标题的延续
-                if i+1 < len(all_text) and all_text[i+1].isupper() and len(all_text[i+1]) > 10:
-                    combined_title = f"{text} {all_text[i+1]}"
-                    title_candidates.append((combined_title, 15, i))  # 给合并标题更高分数
-            
-            # 匹配全大写的标题行
-            elif text.isupper() and len(text) > 10 and len(text) < 100:
-                score = 10 - i * 0.5  # 越靠前越可能是标题
-                title_candidates.append((text, score, i))
-        
-        # 对标题候选按分数排序并选取最佳候选
-        if title_candidates:
-            title_candidates.sort(key=lambda x: x[1], reverse=True)
-            metadata.title = title_candidates[0][0]
-            title_position = title_candidates[0][2]
-            print(f"所有标题候选: {title_candidates[:3]}")
-        else:
-            # 如果没有找到合适的标题，使用一个备选策略
-            for text in all_text[:10]:
-                if text.isupper() and len(text) > 10 and len(text) < 200:  # 大写且适当长度的文本
-                    metadata.title = text
-                    break
-            title_position = 0
-        
-        # 提取作者信息 - 改进后的作者提取逻辑
-        author_candidates = []
-        
-        # 1. 特别处理"TECHNICAL REPORT OF"之后的行，通常是作者或团队
-        for i, text in enumerate(all_text):
-            if "TECHNICAL REPORT" in text.upper() and i+1 < len(all_text):
-                team_text = all_text[i+1].strip()
-                if re.search(r'\b(team|group|lab)\b', team_text, re.IGNORECASE):
-                    author_candidates.append((team_text, 15))
-        
-        # 2. 查找包含Team的文本
-        for text in all_text[:20]:
-            if "Team" in text and len(text) < 30:
-                # 这很可能是团队名
-                author_candidates.append((text, 12))
-        
-        # 添加作者到元数据
-        if author_candidates:
-            # 按分数排序
-            author_candidates.sort(key=lambda x: x[1], reverse=True)
-            
-            # 去重
-            seen_authors = set()
-            for author, _ in author_candidates:
-                if author.lower() not in seen_authors and not author.isdigit():
-                    seen_authors.add(author.lower())
-                    metadata.authors.append(author)
-        
-        # 如果没有找到作者，尝试查找隶属机构信息中的团队名称
-        if not metadata.authors:
-            for text in all_text[:20]:
-                if re.search(r'\b(team|group|lab|laboratory|研究组|团队)\b', text, re.IGNORECASE):
-                    if len(text) < 50:  # 避免太长的文本
-                        metadata.authors.append(text.strip())
-                        break
-        
-        # 提取隶属机构信息
-        for i, element in enumerate(elements[:30]):
-            element_text = str(element).strip()
-            if re.search(r'(university|institute|department|school|laboratory|college|center|centre|\d{5,}|^[a-zA-Z]+@|学院|大学|研究所|研究院)', element_text, re.IGNORECASE):
-                # 可能是隶属机构
-                if element_text not in metadata.affiliations and len(element_text) > 10:
-                    metadata.affiliations.append(element_text)
-
-    def _evaluate_title_candidate(self, text, position, element):
-        """评估标题候选项的可能性分数"""
-        score = 0
-        
-        # 位置因素：越靠前越可能是标题
-        score += max(0, 10 - position) * 0.5
-        
-        # 长度因素：标题通常不会太短也不会太长
-        if 10 <= len(text) <= 150:
-            score += 3
-        elif len(text) < 10:
-            score -= 2
-        elif len(text) > 150:
-            score -= 3
-        
-        # 格式因素
-        if text.isupper():  # 全大写可能是标题
-            score += 2
-        if re.match(r'^[A-Z]', text):  # 首字母大写
-            score += 1
-        if ':' in text:  # 标题常包含冒号
-            score += 1.5
-        
-        # 内容因素
-        if re.search(r'\b(scaling|learning|model|approach|method|system|framework|analysis)\b', text.lower()):
-            score += 2  # 包含常见的学术论文关键词
-            
-        # 避免误判
-        if re.match(r'^\d+$', text):  # 纯数字
-            score -= 10
-        if re.search(r'^(http|www|doi)', text.lower()):  # URL或DOI
-            score -= 5
-        if len(text.split()) <= 2 and len(text) < 15:  # 太短的短语
-            score -= 3
-            
-        # 元数据因素(如果有)
-        if hasattr(element, 'metadata') and element.metadata:
-            # 修复：正确处理ElementMetadata对象
-            try:
-                # 尝试通过getattr安全地获取属性
-                font_size = getattr(element.metadata, 'font_size', None)
-                if font_size is not None and font_size > 14:  # 假设标准字体大小是12
-                    score += 3
-                    
-                font_weight = getattr(element.metadata, 'font_weight', None)
-                if font_weight == 'bold':
-                    score += 2  # 粗体加分
-            except (AttributeError, TypeError):
-                # 如果metadata的访问方式不正确，尝试其他可能的访问方式
-                try:
-                    metadata_dict = element.metadata.__dict__ if hasattr(element.metadata, '__dict__') else {}
-                    if 'font_size' in metadata_dict and metadata_dict['font_size'] > 14:
-                        score += 3
-                    if 'font_weight' in metadata_dict and metadata_dict['font_weight'] == 'bold':
-                        score += 2
-                except Exception:
-                    # 如果所有尝试都失败，忽略元数据处理
-                    pass
-        
-        return score
-
-    def _extract_abstract_and_keywords(self, elements, metadata: PaperMetadata) -> None:
-        """从文档中提取摘要和关键词"""
-        abstract_found = False
-        keywords_found = False
-        abstract_text = []
-        
-        for i, element in enumerate(elements):
-            element_text = str(element).strip().lower()
-            
-            # 寻找摘要部分
-            if not abstract_found and (
-                isinstance(element, Title) and 
-                re.search(self.SECTION_PATTERNS['abstract'], element_text, re.IGNORECASE)
-            ):
-                abstract_found = True
-                continue
-            
-            # 如果找到摘要部分，收集内容直到遇到关键词部分或新章节
-            if abstract_found and not keywords_found:
-                # 检查是否遇到关键词部分或新章节
-                if (
-                    isinstance(element, Title) or 
-                    re.search(self.SECTION_PATTERNS['keywords'], element_text, re.IGNORECASE) or
-                    re.match(r'\b(introduction|引言|method|方法)\b', element_text, re.IGNORECASE)
-                ):
-                    keywords_found = re.search(self.SECTION_PATTERNS['keywords'], element_text, re.IGNORECASE)
-                    abstract_found = False  # 停止收集摘要
-                else:
-                    # 收集摘要文本
-                    if isinstance(element, (Text, NarrativeText)) and element_text:
-                        abstract_text.append(element_text)
-            
-            # 如果找到关键词部分，提取关键词
-            if keywords_found and not abstract_found and not metadata.keywords:
-                if isinstance(element, (Text, NarrativeText)):
-                    # 清除可能的"关键词:"/"Keywords:"前缀
-                    cleaned_text = re.sub(r'^\s*(关键词|keywords|key\s+words)\s*[：:]\s*', '', element_text, flags=re.IGNORECASE)
-                    
-                    # 尝试按不同分隔符分割
-                    for separator in [';', '；', ',', '，']:
-                        if separator in cleaned_text:
-                            metadata.keywords = [k.strip() for k in cleaned_text.split(separator) if k.strip()]
-                            break
-                    
-                    # 如果未能分割，将整个文本作为一个关键词
-                    if not metadata.keywords and cleaned_text:
-                        metadata.keywords = [cleaned_text]
-                    
-                    keywords_found = False  # 已提取关键词，停止处理
-        
-        # 设置摘要文本
-        if abstract_text:
-            metadata.abstract = self.config.paragraph_separator.join(abstract_text)
-
-    def _extract_additional_metadata(self, elements, metadata: PaperMetadata) -> None:
-        """提取其他元数据信息"""
-        for element in elements[:30]:  # 只检查文档前部分
-            element_text = str(element).strip()
-            
-            # 尝试匹配DOI
-            doi_match = re.search(r'(doi|DOI):\s*(10\.\d{4,}\/[a-zA-Z0-9.-]+)', element_text)
-            if doi_match and not metadata.doi:
-                metadata.doi = doi_match.group(2)
-            
-            # 尝试匹配日期
-            date_match = re.search(r'(published|received|accepted|submitted):\s*(\d{1,2}\s+[a-zA-Z]+\s+\d{4}|\d{4}[-/]\d{1,2}[-/]\d{1,2})', element_text, re.IGNORECASE)
-            if date_match and not metadata.date:
-                metadata.date = date_match.group(2)
-                
-            # 尝试匹配年份
-            year_match = re.search(r'\b(19|20)\d{2}\b', element_text)
-            if year_match and not metadata.year:
-                metadata.year = year_match.group(0)
-                
-            # 尝试匹配期刊/会议名称
-            journal_match = re.search(r'(journal|conference):\s*([^,;.]+)', element_text, re.IGNORECASE)
-            if journal_match:
-                if "journal" in journal_match.group(1).lower() and not metadata.journal:
-                    metadata.journal = journal_match.group(2).strip()
-                elif not metadata.conference:
-                    metadata.conference = journal_match.group(2).strip()
-
-
-def main():
-    """主函数：演示用法"""
-    # 创建提取器
-    extractor = PaperMetadataExtractor()
-
-    # 使用示例
-    try:
-        # 替换为实际的文件路径
-        sample_file = '/Users/boyin.liu/Documents/示例文档/论文/3.pdf'
-        if Path(sample_file).exists():
-            metadata = extractor.extract_metadata(sample_file)
-            print("提取的元数据:")
-            print(f"标题: {metadata.title}")
-            print(f"作者: {', '.join(metadata.authors)}")
-            print(f"机构: {', '.join(metadata.affiliations)}")
-            print(f"摘要: {metadata.abstract[:200]}...")
-            print(f"关键词: {', '.join(metadata.keywords)}")
-            print(f"DOI: {metadata.doi}")
-            print(f"日期: {metadata.date}")
-            print(f"年份: {metadata.year}")
-            print(f"期刊: {metadata.journal}")
-            print(f"会议: {metadata.conference}")
-        else:
-            print(f"示例文件 {sample_file} 不存在")
-
-        print("\n支持的格式:", extractor.get_supported_formats())
-
-    except Exception as e:
-        print(f"错误: {e}")
-
-
-if __name__ == "__main__":
-    main() 
--- a/crazy_functions/doc_fns/read_fns/unstructured_all/paper_structure_extractor.py
+++ b/crazy_functions/doc_fns/read_fns/unstructured_all/paper_structure_extractor.py
--- a/crazy_functions/doc_fns/read_fns/unstructured_all/unstructured_md.py
+++ b/crazy_functions/doc_fns/read_fns/unstructured_all/unstructured_md.py
@@ -1,86 +0,0 @@
-from pathlib import Path
-from crazy_functions.doc_fns.read_fns.unstructured_all.paper_structure_extractor import PaperStructureExtractor
-
-def extract_and_save_as_markdown(paper_path, output_path=None):
-    """
-    提取论文结构并保存为Markdown格式
-    
-    参数:
-        paper_path: 论文文件路径
-        output_path: 输出的Markdown文件路径，如果不指定，将使用与输入相同的文件名但扩展名为.md
-    
-    返回:
-        保存的Markdown文件路径
-    """
-    # 创建提取器
-    extractor = PaperStructureExtractor()
-    
-    # 解析文件路径
-    paper_path = Path(paper_path)
-    
-    # 如果未指定输出路径，使用相同文件名但扩展名为.md
-    if output_path is None:
-        output_path = paper_path.with_suffix('.md')
-    else:
-        output_path = Path(output_path)
-    
-    # 确保输出目录存在
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    
-    print(f"正在处理论文: {paper_path}")
-    
-    try:
-        # 提取论文结构
-        paper = extractor.extract_paper_structure(paper_path)
-        
-        # 生成Markdown内容
-        markdown_content = extractor.generate_markdown(paper)
-        
-        # 保存到文件
-        with open(output_path, 'w', encoding='utf-8') as f:
-            f.write(markdown_content)
-        
-        print(f"已成功保存Markdown文件: {output_path}")
-        
-        # 打印摘要信息
-        print("\n论文摘要信息:")
-        print(f"标题: {paper.metadata.title}")
-        print(f"作者: {', '.join(paper.metadata.authors)}")
-        print(f"关键词: {', '.join(paper.keywords)}")
-        print(f"章节数: {len(paper.sections)}")
-        print(f"图表数: {len(paper.figures)}")
-        print(f"表格数: {len(paper.tables)}")
-        print(f"公式数: {len(paper.formulas)}")
-        print(f"参考文献数: {len(paper.references)}")
-        
-        return output_path
-    
-    except Exception as e:
-        print(f"处理论文时出错: {e}")
-        import traceback
-        traceback.print_exc()
-        return None
-
-# 使用示例
-if __name__ == "__main__":
-    # 替换为实际的论文文件路径
-    sample_paper = "crazy_functions/doc_fns/read_fns/paper/2501.12599v1.pdf"
-    
-    # 可以指定输出路径，也可以使用默认路径
-    # output_file = "/path/to/output/paper_structure.md"
-    # extract_and_save_as_markdown(sample_paper, output_file)
-    
-    # 使用默认输出路径（与输入文件同名但扩展名为.md）
-    extract_and_save_as_markdown(sample_paper)
-    
-    # # 批量处理多个论文的示例
-    # paper_dir = Path("/path/to/papers/folder")
-    # output_dir = Path("/path/to/output/folder")
-    #
-    # # 确保输出目录存在
-    # output_dir.mkdir(parents=True, exist_ok=True)
-    #
-    # # 处理目录中的所有PDF文件
-    # for paper_file in paper_dir.glob("*.pdf"):
-    #     output_file = output_dir / f"{paper_file.stem}.md"
-    #     extract_and_save_as_markdown(paper_file, output_file)
--- a/crazy_functions/doc_fns/read_fns/unstructured_all/unstructured_reader.py
+++ b/crazy_functions/doc_fns/read_fns/unstructured_all/unstructured_reader.py
@@ -1,275 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Optional, Set, Dict, Union, List
-from dataclasses import dataclass, field
-import logging
-import os
-
-from unstructured.partition.auto import partition
-from unstructured.documents.elements import (
-    Text, Title, NarrativeText, ListItem, Table,
-    Footer, Header, PageBreak, Image, Address
-)
-
-
-@dataclass
-class TextExtractorConfig:
-    """通用文档提取器配置类
-
-    Attributes:
-        extract_headers_footers: 是否提取页眉页脚
-        extract_tables: 是否提取表格内容
-        extract_lists: 是否提取列表内容
-        extract_titles: 是否提取标题
-        paragraph_separator: 段落之间的分隔符
-        text_cleanup: 文本清理选项字典
-    """
-    extract_headers_footers: bool = False
-    extract_tables: bool = True
-    extract_lists: bool = True
-    extract_titles: bool = True
-    paragraph_separator: str = '\n\n'
-    text_cleanup: Dict[str, bool] = field(default_factory=lambda: {
-        'remove_extra_spaces': True,
-        'normalize_whitespace': True,
-        'remove_special_chars': False,
-        'lowercase': False
-    })
-
-
-class UnstructuredTextExtractor:
-    """通用文档文本内容提取器
-
-    使用 unstructured 库支持多种文档格式的文本提取，提供统一的接口和配置选项。
-    """
-
-    SUPPORTED_EXTENSIONS: Set[str] = {
-        # 文档格式
-        '.pdf', '.docx', '.doc', '.txt',
-        # 演示文稿
-        '.ppt', '.pptx',
-        # 电子表格
-        '.xlsx', '.xls', '.csv',
-        # 图片
-        '.png', '.jpg', '.jpeg', '.tiff',
-        # 邮件
-        '.eml', '.msg', '.p7s',
-        # Markdown
-        ".md",
-        # Org Mode
-        ".org",
-        # Open Office
-        ".odt",
-        # reStructured Text
-        ".rst",
-        # Rich Text
-        ".rtf",
-        # TSV
-        ".tsv",
-        # EPUB
-        '.epub',
-        # 其他格式
-        '.html', '.xml',  '.json',
-    }
-
-    def __init__(self, config: Optional[TextExtractorConfig] = None):
-        """初始化提取器
-
-        Args:
-            config: 提取器配置对象，如果为None则使用默认配置
-        """
-        self.config = config or TextExtractorConfig()
-        self._setup_logging()
-
-    def _setup_logging(self) -> None:
-        """配置日志记录器"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-        )
-        self.logger = logging.getLogger(__name__)
-
-        # 添加文件处理器
-        fh = logging.FileHandler('text_extractor.log')
-        fh.setLevel(logging.ERROR)
-        self.logger.addHandler(fh)
-
-    def _validate_file(self, file_path: Union[str, Path], max_size_mb: int = 100) -> Path:
-        """验证文件
-
-        Args:
-            file_path: 文件路径
-            max_size_mb: 允许的最大文件大小(MB)
-
-        Returns:
-            Path: 验证后的Path对象
-
-        Raises:
-            ValueError: 文件不存在、格式不支持或大小超限
-            PermissionError: 没有读取权限
-        """
-        path = Path(file_path).resolve()
-
-        if not path.exists():
-            raise ValueError(f"File not found: {path}")
-
-        if not path.is_file():
-            raise ValueError(f"Not a file: {path}")
-
-        if not os.access(path, os.R_OK):
-            raise PermissionError(f"No read permission: {path}")
-
-        file_size_mb = path.stat().st_size / (1024 * 1024)
-        if file_size_mb > max_size_mb:
-            raise ValueError(
-                f"File size ({file_size_mb:.1f}MB) exceeds limit of {max_size_mb}MB"
-            )
-
-        if path.suffix.lower() not in self.SUPPORTED_EXTENSIONS:
-            raise ValueError(
-                f"Unsupported format: {path.suffix}. "
-                f"Supported: {', '.join(sorted(self.SUPPORTED_EXTENSIONS))}"
-            )
-
-        return path
-
-    def _cleanup_text(self, text: str) -> str:
-        """清理文本
-
-        Args:
-            text: 原始文本
-
-        Returns:
-            str: 清理后的文本
-        """
-        if self.config.text_cleanup['remove_extra_spaces']:
-            text = ' '.join(text.split())
-
-        if self.config.text_cleanup['normalize_whitespace']:
-            text = text.replace('\t', ' ').replace('\r', '\n')
-
-        if self.config.text_cleanup['lowercase']:
-            text = text.lower()
-
-        return text.strip()
-
-    def _should_extract_element(self, element) -> bool:
-        """判断是否应该提取某个元素
-
-        Args:
-            element: 文档元素
-
-        Returns:
-            bool: 是否应该提取
-        """
-        if isinstance(element, (Text, NarrativeText)):
-            return True
-
-        if isinstance(element, Title) and self.config.extract_titles:
-            return True
-
-        if isinstance(element, ListItem) and self.config.extract_lists:
-            return True
-
-        if isinstance(element, Table) and self.config.extract_tables:
-            return True
-
-        if isinstance(element, (Header, Footer)) and self.config.extract_headers_footers:
-            return True
-
-        return False
-
-    @staticmethod
-    def get_supported_formats() -> List[str]:
-        """获取支持的文件格式列表"""
-        return sorted(UnstructuredTextExtractor.SUPPORTED_EXTENSIONS)
-
-    def extract_text(
-            self,
-            file_path: Union[str, Path],
-            strategy: str = "fast"
-    ) -> str:
-        """提取文本
-
-        Args:
-            file_path: 文件路径
-            strategy: 提取策略 ("fast" 或 "accurate")
-
-        Returns:
-            str: 提取的文本内容
-
-        Raises:
-            Exception: 提取过程中的错误
-        """
-        try:
-            path = self._validate_file(file_path)
-            self.logger.info(f"Processing: {path}")
-
-            # 修改这里：添加 nlp=False 参数来禁用 NLTK
-            elements = partition(
-                str(path),
-                strategy=strategy,
-                include_metadata=True,
-                nlp=True,
-            )
-
-            # 其余代码保持不变
-            text_parts = []
-            for element in elements:
-                if self._should_extract_element(element):
-                    text = str(element)
-                    cleaned_text = self._cleanup_text(text)
-                    if cleaned_text:
-                        if isinstance(element, (Header, Footer)):
-                            prefix = "[Header] " if isinstance(element, Header) else "[Footer] "
-                            text_parts.append(f"{prefix}{cleaned_text}")
-                        else:
-                            text_parts.append(cleaned_text)
-
-            return self.config.paragraph_separator.join(text_parts)
-
-        except Exception as e:
-            self.logger.error(f"Extraction failed: {e}")
-            raise
-
-
-
-def main():
-    """主函数：演示用法"""
-    # 配置
-    config = TextExtractorConfig(
-        extract_headers_footers=True,
-        extract_tables=True,
-        extract_lists=True,
-        extract_titles=True,
-        text_cleanup={
-            'remove_extra_spaces': True,
-            'normalize_whitespace': True,
-            'remove_special_chars': False,
-            'lowercase': False
-        }
-    )
-
-    # 创建提取器
-    extractor = UnstructuredTextExtractor(config)
-
-    # 使用示例
-    try:
-        # 替换为实际的文件路径
-        sample_file = './crazy_functions/doc_fns/read_fns/paper/2501.12599v1.pdf'
-        if Path(sample_file).exists() or True:
-            text = extractor.extract_text(sample_file)
-            print("提取的文本:")
-            print(text)
-        else:
-            print(f"示例文件 {sample_file} 不存在")
-
-        print("\n支持的格式:", extractor.get_supported_formats())
-
-    except Exception as e:
-        print(f"错误: {e}")
-
-
-if __name__ == "__main__":
-    main()
--- a/crazy_functions/doc_fns/read_fns/web_reader.py
+++ b/crazy_functions/doc_fns/read_fns/web_reader.py
@@ -1,219 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, Optional, Union
-from urllib.parse import urlparse
-import logging
-import trafilatura
-import requests
-from pathlib import Path
-
-
-@dataclass
-class WebExtractorConfig:
-    """网页内容提取器配置类
-
-    Attributes:
-        extract_comments: 是否提取评论
-        extract_tables: 是否提取表格
-        extract_links: 是否保留链接信息
-        paragraph_separator: 段落分隔符
-        timeout: 网络请求超时时间(秒)
-        max_retries: 最大重试次数
-        user_agent: 自定义User-Agent
-        text_cleanup: 文本清理选项
-    """
-    extract_comments: bool = False
-    extract_tables: bool = True
-    extract_links: bool = False
-    paragraph_separator: str = '\n\n'
-    timeout: int = 10
-    max_retries: int = 3
-    user_agent: str = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
-    text_cleanup: Dict[str, bool] = field(default_factory=lambda: {
-        'remove_extra_spaces': True,
-        'normalize_whitespace': True,
-        'remove_special_chars': False,
-        'lowercase': False
-    })
-
-
-class WebTextExtractor:
-    """网页文本内容提取器
-    
-    使用trafilatura库提取网页中的主要文本内容，去除广告、导航等无关内容。
-    """
-
-    def __init__(self, config: Optional[WebExtractorConfig] = None):
-        """初始化提取器
-
-        Args:
-            config: 提取器配置对象，如果为None则使用默认配置
-        """
-        self.config = config or WebExtractorConfig()
-        self._setup_logging()
-
-    def _setup_logging(self) -> None:
-        """配置日志记录器"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-        )
-        self.logger = logging.getLogger(__name__)
-
-        # 添加文件处理器
-        fh = logging.FileHandler('web_extractor.log')
-        fh.setLevel(logging.ERROR)
-        self.logger.addHandler(fh)
-
-    def _validate_url(self, url: str) -> bool:
-        """验证URL格式是否有效
-
-        Args:
-            url: 网页URL
-
-        Returns:
-            bool: URL是否有效
-        """
-        try:
-            result = urlparse(url)
-            return all([result.scheme, result.netloc])
-        except Exception:
-            return False
-
-    def _download_webpage(self, url: str) -> Optional[str]:
-        """下载网页内容
-
-        Args:
-            url: 网页URL
-
-        Returns:
-            Optional[str]: 网页HTML内容，失败返回None
-
-        Raises:
-            Exception: 下载失败时抛出异常
-        """
-        headers = {'User-Agent': self.config.user_agent}
-        
-        for attempt in range(self.config.max_retries):
-            try:
-                response = requests.get(
-                    url, 
-                    headers=headers,
-                    timeout=self.config.timeout
-                )
-                response.raise_for_status()
-                return response.text
-            except requests.RequestException as e:
-                self.logger.warning(f"Attempt {attempt + 1} failed: {e}")
-                if attempt == self.config.max_retries - 1:
-                    raise Exception(f"Failed to download webpage after {self.config.max_retries} attempts: {e}")
-        return None
-
-    def _cleanup_text(self, text: str) -> str:
-        """清理文本
-
-        Args:
-            text: 原始文本
-
-        Returns:
-            str: 清理后的文本
-        """
-        if not text:
-            return ""
-
-        if self.config.text_cleanup['remove_extra_spaces']:
-            text = ' '.join(text.split())
-
-        if self.config.text_cleanup['normalize_whitespace']:
-            text = text.replace('\t', ' ').replace('\r', '\n')
-
-        if self.config.text_cleanup['lowercase']:
-            text = text.lower()
-
-        return text.strip()
-
-    def extract_text(self, url: str) -> str:
-        """提取网页文本内容
-
-        Args:
-            url: 网页URL
-
-        Returns:
-            str: 提取的文本内容
-
-        Raises:
-            ValueError: URL无效时抛出
-            Exception: 提取失败时抛出
-        """
-        try:
-            if not self._validate_url(url):
-                raise ValueError(f"Invalid URL: {url}")
-
-            self.logger.info(f"Processing URL: {url}")
-            
-            # 下载网页
-            html_content = self._download_webpage(url)
-            if not html_content:
-                raise Exception("Failed to download webpage")
-
-            # 配置trafilatura提取选项
-            extract_config = {
-                'include_comments': self.config.extract_comments,
-                'include_tables': self.config.extract_tables,
-                'include_links': self.config.extract_links,
-                'no_fallback': False,  # 允许使用后备提取器
-            }
-
-            # 提取文本
-            extracted_text = trafilatura.extract(
-                html_content,
-                **extract_config
-            )
-
-            if not extracted_text:
-                raise Exception("No content could be extracted")
-
-            # 清理文本
-            cleaned_text = self._cleanup_text(extracted_text)
-            
-            return cleaned_text
-
-        except Exception as e:
-            self.logger.error(f"Extraction failed: {e}")
-            raise
-
-
-def main():
-    """主函数：演示用法"""
-    # 配置
-    config = WebExtractorConfig(
-        extract_comments=False,
-        extract_tables=True,
-        extract_links=False,
-        timeout=10,
-        text_cleanup={
-            'remove_extra_spaces': True,
-            'normalize_whitespace': True,
-            'remove_special_chars': False,
-            'lowercase': False
-        }
-    )
-
-    # 创建提取器
-    extractor = WebTextExtractor(config)
-
-    # 使用示例
-    try:
-        # 替换为实际的URL
-        sample_url = 'https://arxiv.org/abs/2412.00036'
-        text = extractor.extract_text(sample_url)
-        print("提取的文本:")
-        print(text)
-
-    except Exception as e:
-        print(f"错误: {e}")
-
-
-if __name__ == "__main__":
-    main()
--- a/crazy_functions/doc_fns/text_content_loader.py
+++ b/crazy_functions/doc_fns/text_content_loader.py
@@ -1,451 +0,0 @@
-import os
-import re
-import glob
-import time
-import queue
-import threading
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import List, Generator, Tuple, Set, Optional, Dict
-from dataclasses import dataclass
-from loguru import logger
-from toolbox import update_ui
-from crazy_functions.rag_fns.rag_file_support import extract_text
-from crazy_functions.doc_fns.content_folder import ContentFoldingManager, FileMetadata, FoldingOptions, FoldingStyle, FoldingError
-from shared_utils.fastapi_server import validate_path_safety
-from datetime import datetime
-import mimetypes
-
-@dataclass
-class FileInfo:
-    """文件信息数据类"""
-    path: str  # 完整路径
-    rel_path: str  # 相对路径
-    size: float  # 文件大小(MB)
-    extension: str  # 文件扩展名
-    last_modified: str  # 最后修改时间
-
-
-class TextContentLoader:
-    """优化版本的文本内容加载器 - 保持原有接口"""
-
-    # 压缩文件扩展名
-    COMPRESSED_EXTENSIONS: Set[str] = {'.zip', '.rar', '.7z', '.tar', '.gz', '.bz2', '.xz'}
-
-    # 系统配置
-    MAX_FILE_SIZE: int = 100 * 1024 * 1024  # 最大文件大小（100MB）
-    MAX_TOTAL_SIZE: int = 100 * 1024 * 1024  # 最大总大小（100MB）
-    MAX_FILES: int = 100  # 最大文件数量
-    CHUNK_SIZE: int = 1024 * 1024  # 文件读取块大小（1MB）
-    MAX_WORKERS: int = min(32, (os.cpu_count() or 1) * 4)  # 最大工作线程数
-    BATCH_SIZE: int = 5  # 批处理大小
-
-    def __init__(self, chatbot: List, history: List):
-        """初始化加载器"""
-        self.chatbot = chatbot
-        self.history = history
-        self.failed_files: List[Tuple[str, str]] = []
-        self.processed_size: int = 0
-        self.start_time: float = 0
-        self.file_cache: Dict[str, str] = {}
-        self._lock = threading.Lock()
-        self.executor = ThreadPoolExecutor(max_workers=self.MAX_WORKERS)
-        self.results_queue = queue.Queue()
-        self.folding_manager = ContentFoldingManager()
-
-    def _create_file_info(self, entry: os.DirEntry, root_path: str) -> FileInfo:
-        """优化的文件信息创建
-
-        Args:
-            entry: 目录入口对象
-            root_path: 根路径
-
-        Returns:
-            FileInfo: 文件信息对象
-        """
-        try:
-            stats = entry.stat()  # 使用缓存的文件状态
-            return FileInfo(
-                path=entry.path,
-                rel_path=os.path.relpath(entry.path, root_path),
-                size=stats.st_size / (1024 * 1024),
-                extension=os.path.splitext(entry.path)[1].lower(),
-                last_modified=time.strftime('%Y-%m-%d %H:%M:%S',
-                                          time.localtime(stats.st_mtime))
-            )
-        except (OSError, ValueError) as e:
-            return None
-
-    def _process_file_batch(self, file_batch: List[FileInfo]) -> List[Tuple[FileInfo, Optional[str]]]:
-        """批量处理文件
-
-        Args:
-            file_batch: 要处理的文件信息列表
-
-        Returns:
-            List[Tuple[FileInfo, Optional[str]]]: 处理结果列表
-        """
-        results = []
-        futures = {}
-
-        for file_info in file_batch:
-            if file_info.path in self.file_cache:
-                results.append((file_info, self.file_cache[file_info.path]))
-                continue
-
-            if file_info.size * 1024 * 1024 > self.MAX_FILE_SIZE:
-                with self._lock:
-                    self.failed_files.append(
-                        (file_info.rel_path,
-                         f"文件过大（{file_info.size:.2f}MB > {self.MAX_FILE_SIZE / (1024 * 1024)}MB）")
-                    )
-                continue
-
-            future = self.executor.submit(self._read_file_content, file_info)
-            futures[future] = file_info
-
-        for future in as_completed(futures):
-            file_info = futures[future]
-            try:
-                content = future.result()
-                if content:
-                    with self._lock:
-                        self.file_cache[file_info.path] = content
-                        self.processed_size += file_info.size * 1024 * 1024
-                    results.append((file_info, content))
-            except Exception as e:
-                with self._lock:
-                    self.failed_files.append((file_info.rel_path, f"读取失败: {str(e)}"))
-
-        return results
-
-    def _read_file_content(self, file_info: FileInfo) -> Optional[str]:
-        """读取单个文件内容
-
-        Args:
-            file_info: 文件信息对象
-
-        Returns:
-            Optional[str]: 文件内容
-        """
-        try:
-            content = extract_text(file_info.path)
-            if not content or not content.strip():
-                return None
-            return content
-        except Exception as e:
-            logger.exception(f"读取文件失败: {str(e)}")
-            raise Exception(f"读取文件失败: {str(e)}")
-
-    def _is_valid_file(self, file_path: str) -> bool:
-        """检查文件是否有效
-
-        Args:
-            file_path: 文件路径
-
-        Returns:
-            bool: 是否为有效文件
-        """
-        if not os.path.isfile(file_path):
-            return False
-
-        extension = os.path.splitext(file_path)[1].lower()
-        if (extension in self.COMPRESSED_EXTENSIONS or
-            os.path.basename(file_path).startswith('.') or
-            not os.access(file_path, os.R_OK)):
-            return False
-
-        # 只要文件可以访问且不在排除列表中就认为是有效的
-        return True
-
-    def _collect_files(self, path: str) -> List[FileInfo]:
-        """收集文件信息
-
-        Args:
-            path: 目标路径
-
-        Returns:
-            List[FileInfo]: 有效文件信息列表
-        """
-        files = []
-        total_size = 0
-
-        # 处理单个文件的情况
-        if os.path.isfile(path):
-            if self._is_valid_file(path):
-                file_info = self._create_file_info(os.DirEntry(os.path.dirname(path)), os.path.dirname(path))
-                if file_info:
-                    return [file_info]
-            return []
-
-        # 处理目录的情况
-        try:
-            # 使用os.walk来递归遍历目录
-            for root, _, filenames in os.walk(path):
-                for filename in filenames:
-                    if len(files) >= self.MAX_FILES:
-                        self.failed_files.append((filename, f"超出最大文件数限制({self.MAX_FILES})"))
-                        continue
-
-                    file_path = os.path.join(root, filename)
-
-                    if not self._is_valid_file(file_path):
-                        continue
-
-                    try:
-                        stats = os.stat(file_path)
-                        file_size = stats.st_size / (1024 * 1024)  # 转换为MB
-
-                        if file_size * 1024 * 1024 > self.MAX_FILE_SIZE:
-                            self.failed_files.append((file_path,
-                                f"文件过大（{file_size:.2f}MB > {self.MAX_FILE_SIZE / (1024 * 1024)}MB）"))
-                            continue
-
-                        if total_size + file_size * 1024 * 1024 > self.MAX_TOTAL_SIZE:
-                            self.failed_files.append((file_path, "超出总大小限制"))
-                            continue
-
-                        file_info = FileInfo(
-                            path=file_path,
-                            rel_path=os.path.relpath(file_path, path),
-                            size=file_size,
-                            extension=os.path.splitext(file_path)[1].lower(),
-                            last_modified=time.strftime('%Y-%m-%d %H:%M:%S',
-                                                      time.localtime(stats.st_mtime))
-                        )
-
-                        total_size += file_size * 1024 * 1024
-                        files.append(file_info)
-
-                    except Exception as e:
-                        self.failed_files.append((file_path, f"处理文件失败: {str(e)}"))
-                        continue
-
-        except Exception as e:
-            self.failed_files.append(("目录扫描", f"扫描失败: {str(e)}"))
-            return []
-
-        return sorted(files, key=lambda x: x.rel_path)
-
-    def _format_content_with_fold(self, file_info, content: str) -> str:
-        """使用折叠管理器格式化文件内容"""
-        try:
-            metadata = FileMetadata(
-                rel_path=file_info.rel_path,
-                size=file_info.size,
-                last_modified=datetime.fromtimestamp(
-                    os.path.getmtime(file_info.path)
-                ),
-                mime_type=mimetypes.guess_type(file_info.path)[0]
-            )
-
-            options = FoldingOptions(
-                style=FoldingStyle.DETAILED,
-                code_language=self.folding_manager._guess_language(
-                    os.path.splitext(file_info.path)[1]
-                ),
-                show_timestamp=True
-            )
-
-            return self.folding_manager.format_content(
-                content=content,
-                formatter_type='file',
-                metadata=metadata,
-                options=options
-            )
-
-        except Exception as e:
-            return f"Error formatting content: {str(e)}"
-
-    def _format_content_for_llm(self, file_infos: List[FileInfo], contents: List[str]) -> str:
-        """格式化用于LLM的内容
-
-        Args:
-            file_infos: 文件信息列表
-            contents: 内容列表
-
-        Returns:
-            str: 格式化后的内容
-        """
-        if len(file_infos) != len(contents):
-            raise ValueError("文件信息和内容数量不匹配")
-
-        result = [
-            "以下是多个文件的内容集合。每个文件的内容都以 '===== 文件 {序号}: {文件名} =====' 开始，",
-            "以 '===== 文件 {序号} 结束 =====' 结束。你可以根据这些分隔符来识别不同文件的内容。\n\n"
-        ]
-
-        for idx, (file_info, content) in enumerate(zip(file_infos, contents), 1):
-            result.extend([
-                f"===== 文件 {idx}: {file_info.rel_path} =====",
-                "文件内容:",
-                content.strip(),
-                f"===== 文件 {idx} 结束 =====\n"
-            ])
-
-        return "\n".join(result)
-
-    def execute(self, txt: str) -> Generator:
-        """执行文本加载和显示 - 保持原有接口
-
-        Args:
-            txt: 目标路径
-
-        Yields:
-            Generator: UI更新生成器
-        """
-        try:
-            # 首先显示正在处理的提示信息
-            self.chatbot.append(["提示", "正在提取文本内容，请稍作等待..."])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            user_name = self.chatbot.get_user()
-            validate_path_safety(txt, user_name)
-            self.start_time = time.time()
-            self.processed_size = 0
-            self.failed_files.clear()
-            successful_files = []
-            successful_contents = []
-
-            # 收集文件
-            files = self._collect_files(txt)
-            if not files:
-                # 移除之前的提示信息
-                self.chatbot.pop()
-                self.chatbot.append(["提示", "未找到任何有效文件"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return
-
-            # 批量处理文件
-            content_blocks = []
-            for i in range(0, len(files), self.BATCH_SIZE):
-                batch = files[i:i + self.BATCH_SIZE]
-                results = self._process_file_batch(batch)
-
-                for file_info, content in results:
-                    if content:
-                        content_blocks.append(self._format_content_with_fold(file_info, content))
-                        successful_files.append(file_info)
-                        successful_contents.append(content)
-
-            # 显示文件内容，替换之前的提示信息
-            if content_blocks:
-                # 移除之前的提示信息
-                self.chatbot.pop()
-                self.chatbot.append(["文件内容", "\n".join(content_blocks)])
-                self.history.extend([
-                    self._format_content_for_llm(successful_files, successful_contents),
-                    "我已经接收到你上传的文件的内容，请提问"
-                ])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        except Exception as e:
-            # 发生错误时，移除之前的提示信息
-            if len(self.chatbot) > 0 and self.chatbot[-1][0] == "提示":
-                self.chatbot.pop()
-            self.chatbot.append(["错误", f"处理过程中出现错误: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        finally:
-            self.executor.shutdown(wait=False)
-            self.file_cache.clear()
-
-    def execute_single_file(self, file_path: str) -> Generator:
-        """执行单个文件的加载和显示
-
-        Args:
-            file_path: 文件路径
-
-        Yields:
-            Generator: UI更新生成器
-        """
-        try:
-            # 首先显示正在处理的提示信息
-            self.chatbot.append(["提示", "正在提取文本内容，请稍作等待..."])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            user_name = self.chatbot.get_user()
-            validate_path_safety(file_path, user_name)
-            self.start_time = time.time()
-            self.processed_size = 0
-            self.failed_files.clear()
-
-            # 验证文件是否存在且可读
-            if not os.path.isfile(file_path):
-                self.chatbot.pop()
-                self.chatbot.append(["错误", f"指定路径不是文件: {file_path}"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return
-
-            if not self._is_valid_file(file_path):
-                self.chatbot.pop()
-                self.chatbot.append(["错误", f"无效的文件类型或无法读取: {file_path}"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return
-
-            # 创建文件信息
-            try:
-                stats = os.stat(file_path)
-                file_size = stats.st_size / (1024 * 1024)  # 转换为MB
-
-                if file_size * 1024 * 1024 > self.MAX_FILE_SIZE:
-                    self.chatbot.pop()
-                    self.chatbot.append(["错误", f"文件过大（{file_size:.2f}MB > {self.MAX_FILE_SIZE / (1024 * 1024)}MB）"])
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                    return
-
-                file_info = FileInfo(
-                    path=file_path,
-                    rel_path=os.path.basename(file_path),
-                    size=file_size,
-                    extension=os.path.splitext(file_path)[1].lower(),
-                    last_modified=time.strftime('%Y-%m-%d %H:%M:%S',
-                                              time.localtime(stats.st_mtime))
-                )
-            except Exception as e:
-                self.chatbot.pop()
-                self.chatbot.append(["错误", f"处理文件失败: {str(e)}"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return
-
-            # 读取文件内容
-            try:
-                content = self._read_file_content(file_info)
-                if not content:
-                    self.chatbot.pop()
-                    self.chatbot.append(["提示", f"文件内容为空或无法提取: {file_path}"])
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                    return
-            except Exception as e:
-                self.chatbot.pop()
-                self.chatbot.append(["错误", f"读取文件失败: {str(e)}"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return
-
-            # 格式化内容并更新UI
-            formatted_content = self._format_content_with_fold(file_info, content)
-
-            # 移除之前的提示信息
-            self.chatbot.pop()
-            self.chatbot.append(["文件内容", formatted_content])
-
-            # 更新历史记录，便于LLM处理
-            llm_content = self._format_content_for_llm([file_info], [content])
-            self.history.extend([llm_content, "我已经接收到你上传的文件的内容，请提问"])
-
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        except Exception as e:
-            # 发生错误时，移除之前的提示信息
-            if len(self.chatbot) > 0 and self.chatbot[-1][0] == "提示":
-                self.chatbot.pop()
-            self.chatbot.append(["错误", f"处理过程中出现错误: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-    def __del__(self):
-        """析构函数 - 确保资源被正确释放"""
-        if hasattr(self, 'executor'):
-            self.executor.shutdown(wait=False)
-        if hasattr(self, 'file_cache'):
-            self.file_cache.clear()
--- a/crazy_functions/game_fns/game_ascii_art.py
+++ b/crazy_functions/game_fns/game_ascii_art.py
@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, update_ui_latest_msg
+from toolbox import CatchException, update_ui, update_ui_lastest_msg
 from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from request_llms.bridge_all import predict_no_ui_long_connection
@@ -13,7 +13,7 @@ class MiniGame_ASCII_Art(GptAcademicGameBaseState):
        else:
            if prompt.strip() == 'exit':
                self.delete_game = True
-                yield from update_ui_latest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
                return
            chatbot.append([prompt, ""])
        yield from update_ui(chatbot=chatbot, history=history)
@@ -31,12 +31,12 @@ class MiniGame_ASCII_Art(GptAcademicGameBaseState):
            self.cur_task = 'identify user guess'
            res = get_code_block(raw_res)
            history += ['', f'the answer is {self.obj}', inputs, res]
-            yield from update_ui_latest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)

        elif self.cur_task == 'identify user guess':
            if is_same_thing(self.obj, prompt, self.llm_kwargs):
                self.delete_game = True
-                yield from update_ui_latest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
            else:
                self.cur_task = 'identify user guess'
-                yield from update_ui_latest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
--- a/crazy_functions/game_fns/game_interactive_story.py
+++ b/crazy_functions/game_fns/game_interactive_story.py
@@ -63,7 +63,7 @@ prompts_terminate = """小说的前文回顾：
 """


-from toolbox import CatchException, update_ui, update_ui_latest_msg
+from toolbox import CatchException, update_ui, update_ui_lastest_msg
 from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from request_llms.bridge_all import predict_no_ui_long_connection
@@ -112,7 +112,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
            if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
                # should we terminate game here?
                self.delete_game = True
-                yield from update_ui_latest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
                return
            if '剧情收尾' in prompt:
                self.cur_task = 'story_terminate'
@@ -137,8 +137,8 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
            )
            self.story.append(story_paragraph)
            # # 配图
-            yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)

            # # 构建后续剧情引导
            previously_on_story = ""
@@ -171,8 +171,8 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
            )
            self.story.append(story_paragraph)
            # # 配图
-            yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)

            # # 构建后续剧情引导
            previously_on_story = ""
@@ -204,8 +204,8 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
                chatbot, history_, self.sys_prompt_
            )
            # # 配图
-            yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)

            # terminate game
            self.delete_game = True
--- a/crazy_functions/gen_fns/gen_fns_shared.py
+++ b/crazy_functions/gen_fns/gen_fns_shared.py
@@ -2,7 +2,7 @@ import time
 import importlib
 from toolbox import trimmed_format_exc, gen_time_str, get_log_folder
 from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
-from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_latest_msg
+from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
 import multiprocessing

 def get_class_name(class_string):
--- a/crazy_functions/json_fns/pydantic_io.py
+++ b/crazy_functions/json_fns/pydantic_io.py
@@ -102,10 +102,10 @@ class GptJsonIO():
                logging.info(f'Repairing json：{response}')
                repair_prompt = self.generate_repair_prompt(broken_json = response, error=repr(e))
                result = self.generate_output(gpt_gen_fn(repair_prompt, self.format_instructions))
-                logging.info('Repair json success.')
+                logging.info('Repaire json success.')
            except Exception as e:
                # 没辙了，放弃治疗
-                logging.info('Repair json fail.')
+                logging.info('Repaire json fail.')
                raise JsonStringError('Cannot repair json.', str(e))
        return result

--- a/crazy_functions/latex_fns/latex_actions.py
+++ b/crazy_functions/latex_fns/latex_actions.py
@@ -3,7 +3,7 @@ import re
 import shutil
 import numpy as np
 from loguru import logger
-from toolbox import update_ui, update_ui_latest_msg, get_log_folder, gen_time_str
+from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
 from toolbox import get_conf, promote_file_to_downloadzone
 from crazy_functions.latex_fns.latex_toolbox import PRESERVE, TRANSFORM
 from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
@@ -20,7 +20,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
    """
    break down latex file to a linked list,
    each node use a preserve flag to indicate whether it should
-    be processed by GPT.
+    be proccessed by GPT.
    """
    text = txt
    mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
@@ -85,14 +85,14 @@ class LatexPaperSplit():
    """
    break down latex file to a linked list,
    each node use a preserve flag to indicate whether it should
-    be processed by GPT.
+    be proccessed by GPT.
    """
    def __init__(self) -> None:
        self.nodes = None
        self.msg = "*{\\scriptsize\\textbf{警告：该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成，" + \
            "版权归原文作者所有。翻译内容可靠性无保障，请仔细鉴别并以原文为准。" + \
            "项目Github地址 \\url{https://github.com/binary-husky/gpt_academic/}。"
-        # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加README中的QQ联系开发者）
+        # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
        self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\"
        self.title = "unknown"
        self.abstract = "unknown"
@@ -151,7 +151,7 @@ class LatexPaperSplit():
        """
        break down latex file to a linked list,
        each node use a preserve flag to indicate whether it should
-        be processed by GPT.
+        be proccessed by GPT.
        P.S. use multiprocessing to avoid timeout error
        """
        import multiprocessing
@@ -300,8 +300,7 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
    write_html(pfg.sp_file_contents, pfg.sp_file_result, chatbot=chatbot, project_folder=project_folder)

    #  <-------- 写出文件 ---------->
-    model_name = llm_kwargs['llm_model'].replace('_', '\\_')  # 替换LLM模型名称中的下划线为转义字符
-    msg = f"当前大语言模型: {model_name}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
+    msg = f"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
    final_tex = lps.merge_result(pfg.file_result, mode, msg)
    objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))

@@ -351,42 +350,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
    max_try = 32
    chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
    chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
-    yield from update_ui_latest_msg('编译已经开始...', chatbot, history)   # 刷新Gradio前端界面
-    # 检查是否需要使用xelatex
-    def check_if_need_xelatex(tex_path):
-        try:
-            with open(tex_path, 'r', encoding='utf-8', errors='replace') as f:
-                content = f.read(5000)
-                # 检查是否有使用xelatex的宏包
-                need_xelatex = any(
-                    pkg in content 
-                    for pkg in ['fontspec', 'xeCJK', 'xetex', 'unicode-math', 'xltxtra', 'xunicode']
-                )
-                if need_xelatex:
-                    logger.info(f"检测到宏包需要xelatex编译, 切换至xelatex编译")
-                else:
-                    logger.info(f"未检测到宏包需要xelatex编译, 使用pdflatex编译")
-                return need_xelatex
-        except Exception:
-            return False
-
-    # 根据编译器类型返回编译命令
-    def get_compile_command(compiler, filename):
-        compile_command = f'{compiler} -interaction=batchmode -file-line-error {filename}.tex'
-        logger.info('Latex 编译指令: ' + compile_command)
-        return compile_command
-
-    # 确定使用的编译器
-    compiler = 'pdflatex'
-    if check_if_need_xelatex(pj(work_folder_modified, f'{main_file_modified}.tex')):
-        logger.info("检测到宏包需要xelatex编译，切换至xelatex编译")
-        # Check if xelatex is installed
-        try:
-            import subprocess
-            subprocess.run(['xelatex', '--version'], capture_output=True, check=True)
-            compiler = 'xelatex'
-        except (subprocess.CalledProcessError, FileNotFoundError):
-            raise RuntimeError("检测到需要使用xelatex编译，但系统中未安装xelatex。请先安装texlive或其他提供xelatex的LaTeX发行版。")
+    yield from update_ui_lastest_msg('编译已经开始...', chatbot, history)   # 刷新Gradio前端界面

    while True:
        import os
@@ -396,36 +360,36 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
            shutil.copyfile(may_exist_bbl, target_bbl)

        # https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
-        yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history)   # 刷新Gradio前端界面
-        ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_original), work_folder_original)
+        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history)   # 刷新Gradio前端界面
+        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)

-        yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history)   # 刷新Gradio前端界面
-        ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_modified), work_folder_modified)
+        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history)   # 刷新Gradio前端界面
+        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)

        if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):
            # 只有第二步成功，才能继续下面的步骤
-            yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history)    # 刷新Gradio前端界面
+            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history)    # 刷新Gradio前端界面
            if not os.path.exists(pj(work_folder_original, f'{main_file_original}.bbl')):
                ok = compile_latex_with_timeout(f'bibtex  {main_file_original}.aux', work_folder_original)
            if not os.path.exists(pj(work_folder_modified, f'{main_file_modified}.bbl')):
                ok = compile_latex_with_timeout(f'bibtex  {main_file_modified}.aux', work_folder_modified)

-            yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history)  # 刷新Gradio前端界面
-            ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_original), work_folder_original)
-            ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_modified), work_folder_modified)
-            ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_original), work_folder_original)
-            ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_modified), work_folder_modified)
+            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history)  # 刷新Gradio前端界面
+            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
+            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
+            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
+            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)

            if mode!='translate_zh':
-                yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
+                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
                logger.info(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
                ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex', os.getcwd())

-                yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面
-                ok = compile_latex_with_timeout(get_compile_command(compiler, 'merge_diff'), work_folder)
+                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面
+                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
                ok = compile_latex_with_timeout(f'bibtex    merge_diff.aux', work_folder)
-                ok = compile_latex_with_timeout(get_compile_command(compiler, 'merge_diff'), work_folder)
-                ok = compile_latex_with_timeout(get_compile_command(compiler, 'merge_diff'), work_folder)
+                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
+                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)

        # <---------- 检查结果 ----------->
        results_ = ""
@@ -435,13 +399,13 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
        results_ += f"原始PDF编译是否成功: {original_pdf_success};"
        results_ += f"转化PDF编译是否成功: {modified_pdf_success};"
        results_ += f"对比PDF编译是否成功: {diff_pdf_success};"
-        yield from update_ui_latest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
+        yield from update_ui_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面

        if diff_pdf_success:
            result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path
            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
        if modified_pdf_success:
-            yield from update_ui_latest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history)    # 刷新Gradio前端界面
+            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history)    # 刷新Gradio前端界面
            result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
            origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
            if os.path.exists(pj(work_folder, '..', 'translation')):
@@ -472,7 +436,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
                work_folder_modified=work_folder_modified,
                fixed_line=fixed_line
            )
-            yield from update_ui_latest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history)   # 刷新Gradio前端界面
+            yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history)   # 刷新Gradio前端界面
            if not can_retry: break

    return False # 失败啦
@@ -504,70 +468,3 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
    except:
        from toolbox import trimmed_format_exc
        logger.error('writing html result failed:', trimmed_format_exc())
-
-
-def upload_to_gptac_cloud_if_user_allow(chatbot, arxiv_id):
-    try:
-        # 如果用户允许，我们将arxiv论文PDF上传到GPTAC学术云
-        from toolbox import map_file_to_sha256
-        # 检查是否顺利，如果没有生成预期的文件，则跳过
-        is_result_good = False
-        for file_path in chatbot._cookies.get("files_to_promote", []):
-            if file_path.endswith('translate_zh.pdf'):
-                is_result_good = True
-        if not is_result_good:
-            return
-        # 上传文件
-        for file_path in chatbot._cookies.get("files_to_promote", []):
-            align_name = None
-            # normalized name
-            for name in ['translate_zh.pdf', 'comparison.pdf']:
-                if file_path.endswith(name): align_name = name
-            # if match any align name
-            if align_name:
-                logger.info(f'Uploading to GPTAC cloud as the user has set `allow_cloud_io`: {file_path}')
-                with open(file_path, 'rb') as f:
-                    import requests
-                    url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_upload'
-                    files = {'file': (align_name, f, 'application/octet-stream')}
-                    data = {
-                        'arxiv_id': arxiv_id,
-                        'file_hash': map_file_to_sha256(file_path),
-                        'language': 'zh',
-                        'trans_prompt': 'to_be_implemented',
-                        'llm_model': 'to_be_implemented',
-                        'llm_model_param': 'to_be_implemented',
-                    }
-                    resp = requests.post(url=url, files=files, data=data, timeout=30)
-                logger.info(f'Uploading terminate ({resp.status_code})`: {file_path}')
-    except:
-        # 如果上传失败，不会中断程序，因为这是次要功能
-        pass
-
-def check_gptac_cloud(arxiv_id, chatbot):
-    import requests
-    success = False
-    downloaded = []
-    try:
-        for pdf_target in ['translate_zh.pdf', 'comparison.pdf']:
-            url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_exist'
-            data = {
-                'arxiv_id': arxiv_id,
-                'name': pdf_target,
-            }
-            resp = requests.post(url=url, data=data)
-            cache_hit_result = resp.text.strip('"')
-            if cache_hit_result.startswith("http"):
-                url = cache_hit_result
-                logger.info(f'Downloading from GPTAC cloud: {url}')
-                resp = requests.get(url=url, timeout=30)
-                target = os.path.join(get_log_folder(plugin_name='gptac_cloud'), gen_time_str(), pdf_target)
-                os.makedirs(os.path.dirname(target), exist_ok=True)
-                with open(target, 'wb') as f:
-                    f.write(resp.content)
-                new_path = promote_file_to_downloadzone(target, chatbot=chatbot)
-                success = True
-                downloaded.append(new_path)
-    except:
-        pass
-    return success, downloaded
--- a/crazy_functions/latex_fns/latex_pickle_io.py
+++ b/crazy_functions/latex_fns/latex_pickle_io.py
@@ -6,16 +6,12 @@ class SafeUnpickler(pickle.Unpickler):
    def get_safe_classes(self):
        from crazy_functions.latex_fns.latex_actions import LatexPaperFileGroup, LatexPaperSplit
        from crazy_functions.latex_fns.latex_toolbox import LinkedListNode
-        from numpy.core.multiarray import scalar
-        from numpy import dtype
        # 定义允许的安全类
        safe_classes = {
            # 在这里添加其他安全的类
            'LatexPaperFileGroup': LatexPaperFileGroup,
            'LatexPaperSplit': LatexPaperSplit,
            'LinkedListNode': LinkedListNode,
-            'scalar': scalar,
-            'dtype': dtype,
        }
        return safe_classes

@@ -26,6 +22,8 @@ class SafeUnpickler(pickle.Unpickler):
        for class_name in self.safe_classes.keys():
            if (class_name in f'{module}.{name}'):
                match_class_name = class_name
+        if module == 'numpy' or module.startswith('numpy.'):
+            return super().find_class(module, name)
        if match_class_name is not None:
            return self.safe_classes[match_class_name]
        # 如果尝试加载未授权的类，则抛出异常
--- a/crazy_functions/latex_fns/latex_toolbox.py
+++ b/crazy_functions/latex_fns/latex_toolbox.py
@@ -168,7 +168,7 @@ def set_forbidden_text(text, mask, pattern, flags=0):
 def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
    """
    Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch complete text area.
+    count the number of the braces so as to catch compelete text area.
    e.g.
    \begin{abstract} blablablablablabla. \end{abstract}
    """
@@ -188,7 +188,7 @@ def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
 def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
    """
    Add a preserve text area in this paper (text become untouchable for GPT).
-    count the number of the braces so as to catch complete text area.
+    count the number of the braces so as to catch compelete text area.
    e.g.
    \caption{blablablablabla\texbf{blablabla}blablabla.}
    """
@@ -214,7 +214,7 @@ def reverse_forbidden_text_careful_brace(
 ):
    """
    Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch complete text area.
+    count the number of the braces so as to catch compelete text area.
    e.g.
    \caption{blablablablabla\texbf{blablabla}blablabla.}
    """
@@ -287,23 +287,23 @@ def find_main_tex_file(file_manifest, mode):
    在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
    P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
    """
-    candidates = []
+    canidates = []
    for texf in file_manifest:
        if os.path.basename(texf).startswith("merge"):
            continue
        with open(texf, "r", encoding="utf8", errors="ignore") as f:
            file_content = f.read()
        if r"\documentclass" in file_content:
-            candidates.append(texf)
+            canidates.append(texf)
        else:
            continue

-    if len(candidates) == 0:
+    if len(canidates) == 0:
        raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
-    elif len(candidates) == 1:
-        return candidates[0]
-    else:  # if len(candidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
-        candidates_score = []
+    elif len(canidates) == 1:
+        return canidates[0]
+    else:  # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
+        canidates_score = []
        # 给出一些判定模板文档的词作为扣分项
        unexpected_words = [
            "\\LaTeX",
@@ -316,19 +316,19 @@ def find_main_tex_file(file_manifest, mode):
            "reviewers",
        ]
        expected_words = ["\\input", "\\ref", "\\cite"]
-        for texf in candidates:
-            candidates_score.append(0)
+        for texf in canidates:
+            canidates_score.append(0)
            with open(texf, "r", encoding="utf8", errors="ignore") as f:
                file_content = f.read()
                file_content = rm_comments(file_content)
            for uw in unexpected_words:
                if uw in file_content:
-                    candidates_score[-1] -= 1
+                    canidates_score[-1] -= 1
            for uw in expected_words:
                if uw in file_content:
-                    candidates_score[-1] += 1
-        select = np.argmax(candidates_score)  # 取评分最高者返回
-        return candidates[select]
+                    canidates_score[-1] += 1
+        select = np.argmax(canidates_score)  # 取评分最高者返回
+        return canidates[select]


 def rm_comments(main_file):
@@ -374,7 +374,7 @@ def find_tex_file_ignore_case(fp):

 def merge_tex_files_(project_foler, main_file, mode):
    """
-    Merge Tex project recursively
+    Merge Tex project recrusively
    """
    main_file = rm_comments(main_file)
    for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
@@ -429,7 +429,7 @@ def find_title_and_abs(main_file):

 def merge_tex_files(project_foler, main_file, mode):
    """
-    Merge Tex project recursively
+    Merge Tex project recrusively
    P.S. 顺便把CTEX塞进去以支持中文
    P.S. 顺便把Latex的注释去除
    """
@@ -644,15 +644,6 @@ def run_in_subprocess(func):


 def _merge_pdfs(pdf1_path, pdf2_path, output_path):
-    try:
-        logger.info("Merging PDFs using _merge_pdfs_ng")
-        _merge_pdfs_ng(pdf1_path, pdf2_path, output_path)
-    except:
-        logger.info("Merging PDFs using _merge_pdfs_legacy")
-        _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path)
-
-
-def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
    import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
    from PyPDF2.generic import NameObject, TextStringObject,ArrayObject,FloatObject,NumberObject

@@ -697,206 +688,65 @@ def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
                    ),
                    0,
                )
-                if "/Annots" in new_page:
-                    annotations = new_page["/Annots"]
+                if '/Annots' in page1:
+                    page1_annot_id = [annot.idnum for annot in page1['/Annots']]
+                else:
+                    page1_annot_id = []
+
+                if '/Annots' in page2:
+                    page2_annot_id = [annot.idnum for annot in page2['/Annots']]
+                else:
+                    page2_annot_id = []
+                if '/Annots' in new_page:
+                    annotations = new_page['/Annots']
                    for i, annot in enumerate(annotations):
                        annot_obj = annot.get_object()

                        # 检查注释类型是否是链接（/Link）
-                        if annot_obj.get("/Subtype") == "/Link":
+                        if annot_obj.get('/Subtype') == '/Link':
                            # 检查是否为内部链接跳转（/GoTo）或外部URI链接（/URI）
-                            action = annot_obj.get("/A")
+                            action = annot_obj.get('/A')
                            if action:

-                                if "/S" in action and action["/S"] == "/GoTo":
+                                if '/S' in action and action['/S'] == '/GoTo':
                                    # 内部链接：跳转到文档中的某个页面
-                                    dest = action.get("/D")  # 目标页或目标位置
-                                    # if dest and annot.idnum in page2_annot_id:
-                                    # if dest in pdf2_reader.named_destinations:
-                                    if dest and page2.annotations:
-                                        if annot in page2.annotations:
+                                    dest = action.get('/D')  # 目标页或目标位置
+                                    if dest and annot.idnum in page2_annot_id:
                                        # 获取原始文件中跳转信息，包括跳转页面
-                                            destination = pdf2_reader.named_destinations[
-                                                dest
-                                            ]
-                                            page_number = (
-                                                pdf2_reader.get_destination_page_number(
-                                                    destination
-                                                )
-                                            )
+                                        destination = pdf2_reader.named_destinations[dest]
+                                        page_number = pdf2_reader.get_destination_page_number(destination)
                                        #更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
                                        #“/D”:[10,'/XYZ',100,100,0]
-                                            if destination.dest_array[1] == "/XYZ":
-                                                annot_obj["/A"].update(
-                                                    {
-                                                        NameObject("/D"): ArrayObject(
-                                                            [
-                                                                NumberObject(page_number),
-                                                                destination.dest_array[1],
-                                                                FloatObject(
-                                                                    destination.dest_array[
-                                                                        2
-                                                                    ]
-                                                                    + int(
-                                                                        page1.mediaBox.getWidth()
-                                                                    )
-                                                                ),
-                                                                destination.dest_array[3],
-                                                                destination.dest_array[4],
-                                                            ]
-                                                        )  # 确保键和值是 PdfObject
-                                                    }
-                                                )
-                                            else:
-                                                annot_obj["/A"].update(
-                                                    {
-                                                        NameObject("/D"): ArrayObject(
-                                                            [
-                                                                NumberObject(page_number),
-                                                                destination.dest_array[1],
-                                                            ]
-                                                        )  # 确保键和值是 PdfObject
-                                                    }
-                                                )
-
-                                            rect = annot_obj.get("/Rect")
+                                        annot_obj['/A'].update({
+                                            NameObject("/D"): ArrayObject([NumberObject(page_number),destination.dest_array[1], FloatObject(destination.dest_array[2] + int(page1.mediaBox.getWidth())) ,destination.dest_array[3],destination.dest_array[4]])  # 确保键和值是 PdfObject
+                                        })
+                                        rect = annot_obj.get('/Rect')
                                        # 更新点击坐标
-                                            rect = ArrayObject(
-                                                [
-                                                    FloatObject(
-                                                        rect[0]
-                                                        + int(page1.mediaBox.getWidth())
-                                                    ),
-                                                    rect[1],
-                                                    FloatObject(
-                                                        rect[2]
-                                                        + int(page1.mediaBox.getWidth())
-                                                    ),
-                                                    rect[3],
-                                                ]
-                                            )
-                                            annot_obj.update(
-                                                {
-                                                    NameObject(
-                                                        "/Rect"
-                                                    ): rect  # 确保键和值是 PdfObject
-                                                }
-                                            )
-                                    # if dest and annot.idnum in page1_annot_id:
-                                    # if dest in pdf1_reader.named_destinations:
-                                    if dest and page1.annotations:
-                                        if annot in page1.annotations:
+                                        rect = ArrayObject([FloatObject(rect[0]+ int(page1.mediaBox.getWidth())),rect[1],
+                                                            FloatObject(rect[2]+int(page1.mediaBox.getWidth())),rect[3] ])
+                                        annot_obj.update({
+                                            NameObject("/Rect"): rect  # 确保键和值是 PdfObject
+                                        })
+                                    if dest and annot.idnum in page1_annot_id:
                                        # 获取原始文件中跳转信息，包括跳转页面
-                                            destination = pdf1_reader.named_destinations[
-                                                dest
-                                            ]
-                                            page_number = (
-                                                pdf1_reader.get_destination_page_number(
-                                                    destination
-                                                )
-                                            )
+                                        destination = pdf1_reader.named_destinations[dest]
+                                        page_number = pdf1_reader.get_destination_page_number(destination)
                                        #更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
                                        #“/D”:[10,'/XYZ',100,100,0]
-                                            if destination.dest_array[1] == "/XYZ":
-                                                annot_obj["/A"].update(
-                                                    {
-                                                        NameObject("/D"): ArrayObject(
-                                                            [
-                                                                NumberObject(page_number),
-                                                                destination.dest_array[1],
-                                                                FloatObject(
-                                                                    destination.dest_array[
-                                                                        2
-                                                                    ]
-                                                                ),
-                                                                destination.dest_array[3],
-                                                                destination.dest_array[4],
-                                                            ]
-                                                        )  # 确保键和值是 PdfObject
-                                                    }
-                                                )
-                                            else:
-                                                annot_obj["/A"].update(
-                                                    {
-                                                        NameObject("/D"): ArrayObject(
-                                                            [
-                                                                NumberObject(page_number),
-                                                                destination.dest_array[1],
-                                                            ]
-                                                        )  # 确保键和值是 PdfObject
-                                                    }
-                                                )
+                                        annot_obj['/A'].update({
+                                            NameObject("/D"): ArrayObject([NumberObject(page_number),destination.dest_array[1], FloatObject(destination.dest_array[2]) ,destination.dest_array[3],destination.dest_array[4]])  # 确保键和值是 PdfObject
+                                        })
+                                        rect = annot_obj.get('/Rect')
+                                        rect = ArrayObject([FloatObject(rect[0]),rect[1],
+                                                            FloatObject(rect[2]),rect[3] ])
+                                        annot_obj.update({
+                                            NameObject("/Rect"): rect  # 确保键和值是 PdfObject
+                                        })

-                                            rect = annot_obj.get("/Rect")
-                                            rect = ArrayObject(
-                                                [
-                                                    FloatObject(rect[0]),
-                                                    rect[1],
-                                                    FloatObject(rect[2]),
-                                                    rect[3],
-                                                ]
-                                            )
-                                            annot_obj.update(
-                                                {
-                                                    NameObject(
-                                                        "/Rect"
-                                                    ): rect  # 确保键和值是 PdfObject
-                                                }
-                                            )
-
-                                elif "/S" in action and action["/S"] == "/URI":
+                                elif '/S' in action and action['/S'] == '/URI':
                                    # 外部链接：跳转到某个URI
-                                    uri = action.get("/URI")
+                                    uri = action.get('/URI')
                output_writer.addPage(new_page)
-            # Save the merged PDF file
-            with open(output_path, "wb") as output_file:
-                output_writer.write(output_file)
-
-
-def _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path):
-    import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
-
-    Percent = 0.95
-    # raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
-    # Open the first PDF file
-    with open(pdf1_path, "rb") as pdf1_file:
-        pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
-        # Open the second PDF file
-        with open(pdf2_path, "rb") as pdf2_file:
-            pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
-            # Create a new PDF file to store the merged pages
-            output_writer = PyPDF2.PdfFileWriter()
-            # Determine the number of pages in each PDF file
-            num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
-            # Merge the pages from the two PDF files
-            for page_num in range(num_pages):
-                # Add the page from the first PDF file
-                if page_num < pdf1_reader.numPages:
-                    page1 = pdf1_reader.getPage(page_num)
-                else:
-                    page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
-                # Add the page from the second PDF file
-                if page_num < pdf2_reader.numPages:
-                    page2 = pdf2_reader.getPage(page_num)
-                else:
-                    page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
-                # Create a new empty page with double width
-                new_page = PyPDF2.PageObject.createBlankPage(
-                    width=int(
-                        int(page1.mediaBox.getWidth())
-                        + int(page2.mediaBox.getWidth()) * Percent
-                    ),
-                    height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
-                )
-                new_page.mergeTranslatedPage(page1, 0, 0)
-                new_page.mergeTranslatedPage(
-                    page2,
-                    int(
-                        int(page1.mediaBox.getWidth())
-                        - int(page2.mediaBox.getWidth()) * (1 - Percent)
-                    ),
-                    0,
-                )
                output_writer.addPage(new_page)
            # Save the merged PDF file
            with open(output_path, "wb") as output_file:
--- a/crazy_functions/media_fns/get_media.py
+++ b/crazy_functions/media_fns/get_media.py
@@ -1,43 +0,0 @@
-from toolbox import update_ui, get_conf, promote_file_to_downloadzone, update_ui_latest_msg, generate_file_link
-from shared_utils.docker_as_service_api import stream_daas
-from shared_utils.docker_as_service_api import DockerServiceApiComModel
-import random
-
-def download_video(video_id, only_audio, user_name, chatbot, history):
-    from toolbox import get_log_folder
-    chatbot.append([None, "Processing..."])
-    yield from update_ui(chatbot, history)
-    client_command = f'{video_id} --audio-only' if only_audio else video_id
-    server_urls = get_conf('DAAS_SERVER_URLS')
-    server_url = random.choice(server_urls)
-    docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
-    save_file_dir = get_log_folder(user_name, plugin_name='media_downloader')
-    for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
-        status_buf = ""
-        status_buf += "DaaS message: \n\n"
-        status_buf += output_manifest['server_message'].replace('\n', '<br/>')
-        status_buf += "\n\n"
-        status_buf += "DaaS standard error: \n\n"
-        status_buf += output_manifest['server_std_err'].replace('\n', '<br/>')
-        status_buf += "\n\n"
-        status_buf += "DaaS standard output: \n\n"
-        status_buf += output_manifest['server_std_out'].replace('\n', '<br/>')
-        status_buf += "\n\n"
-        status_buf += "DaaS file attach: \n\n"
-        status_buf += str(output_manifest['server_file_attach'])
-        yield from update_ui_latest_msg(status_buf, chatbot, history)
-
-    return output_manifest['server_file_attach']
-
-
-def search_videos(keywords):
-    from toolbox import get_log_folder
-    client_command = keywords
-    server_urls = get_conf('DAAS_SERVER_URLS')
-    server_url = random.choice(server_urls)
-    server_url = server_url.replace('stream', 'search')
-    docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
-    save_file_dir = get_log_folder("default_user", plugin_name='media_downloader')
-    for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
-        return output_manifest['server_message']
-
--- a/crazy_functions/multi_stage/multi_stage_utils.py
+++ b/crazy_functions/multi_stage/multi_stage_utils.py
@@ -1,6 +1,6 @@
 from pydantic import BaseModel, Field
 from typing import List
-from toolbox import update_ui_latest_msg, disable_auto_promotion
+from toolbox import update_ui_lastest_msg, disable_auto_promotion
 from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
 from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
--- a/crazy_functions/paper_fns/init.py
+++ b/crazy_functions/paper_fns/init.py
--- a/crazy_functions/paper_fns/auto_git/handlers/base_handler.py
+++ b/crazy_functions/paper_fns/auto_git/handlers/base_handler.py
@@ -1,386 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List, Dict, Any
-from ..query_analyzer import SearchCriteria
-from ..sources.github_source import GitHubSource
-import asyncio
-import re
-from datetime import datetime
-
-class BaseHandler(ABC):
-    """处理器基类"""
-
-    def __init__(self, github: GitHubSource, llm_kwargs: Dict = None):
-        self.github = github
-        self.llm_kwargs = llm_kwargs or {}
-        self.ranked_repos = []  # 存储排序后的仓库列表
-
-    def _get_search_params(self, plugin_kwargs: Dict) -> Dict:
-        """获取搜索参数"""
-        return {
-            'max_repos': plugin_kwargs.get('max_repos', 150),  # 最大仓库数量，从30改为150
-            'max_details': plugin_kwargs.get('max_details', 80),  # 最多展示详情的仓库数量，新增参数
-            'search_multiplier': plugin_kwargs.get('search_multiplier', 3),  # 检索倍数
-            'min_stars': plugin_kwargs.get('min_stars', 0),  # 最少星标数
-        }
-
-    @abstractmethod
-    async def handle(
-            self,
-            criteria: SearchCriteria,
-            chatbot: List[List[str]],
-            history: List[List[str]],
-            system_prompt: str,
-            llm_kwargs: Dict[str, Any],
-            plugin_kwargs: Dict[str, Any],
-    ) -> str:
-        """处理查询"""
-        pass
-
-    async def _search_repositories(self, query: str, language: str = None, min_stars: int = 0,
-                                sort: str = "stars", per_page: int = 30) -> List[Dict]:
-        """搜索仓库"""
-        try:
-            # 构建查询字符串
-            if min_stars > 0 and "stars:>" not in query:
-                query += f" stars:>{min_stars}"
-                
-            if language and "language:" not in query:
-                query += f" language:{language}"
-                
-            # 执行搜索
-            result = await self.github.search_repositories(
-                query=query,
-                sort=sort,
-                per_page=per_page
-            )
-            
-            if result and "items" in result:
-                return result["items"]
-            return []
-        except Exception as e:
-            print(f"仓库搜索出错: {str(e)}")
-            return []
-
-    async def _search_bilingual_repositories(self, english_query: str, chinese_query: str, language: str = None, min_stars: int = 0,
-                                sort: str = "stars", per_page: int = 30) -> List[Dict]:
-        """同时搜索中英文仓库并合并结果"""
-        try:
-            # 搜索英文仓库
-            english_results = await self._search_repositories(
-                query=english_query,
-                language=language,
-                min_stars=min_stars,
-                sort=sort,
-                per_page=per_page
-            )
-            
-            # 搜索中文仓库
-            chinese_results = await self._search_repositories(
-                query=chinese_query,
-                language=language,
-                min_stars=min_stars,
-                sort=sort,
-                per_page=per_page
-            )
-            
-            # 合并结果，去除重复项
-            merged_results = []
-            seen_repos = set()
-            
-            # 优先添加英文结果
-            for repo in english_results:
-                repo_id = repo.get('id')
-                if repo_id and repo_id not in seen_repos:
-                    seen_repos.add(repo_id)
-                    merged_results.append(repo)
-            
-            # 添加中文结果（排除重复）
-            for repo in chinese_results:
-                repo_id = repo.get('id')
-                if repo_id and repo_id not in seen_repos:
-                    seen_repos.add(repo_id)
-                    merged_results.append(repo)
-            
-            # 按星标数重新排序
-            merged_results.sort(key=lambda x: x.get('stargazers_count', 0), reverse=True)
-            
-            return merged_results[:per_page]  # 返回合并后的前per_page个结果
-        except Exception as e:
-            print(f"双语仓库搜索出错: {str(e)}")
-            return []
-
-    async def _search_code(self, query: str, language: str = None, per_page: int = 30) -> List[Dict]:
-        """搜索代码"""
-        try:
-            # 构建查询字符串
-            if language and "language:" not in query:
-                query += f" language:{language}"
-                
-            # 执行搜索
-            result = await self.github.search_code(
-                query=query,
-                per_page=per_page
-            )
-            
-            if result and "items" in result:
-                return result["items"]
-            return []
-        except Exception as e:
-            print(f"代码搜索出错: {str(e)}")
-            return []
-
-    async def _search_bilingual_code(self, english_query: str, chinese_query: str, language: str = None, per_page: int = 30) -> List[Dict]:
-        """同时搜索中英文代码并合并结果"""
-        try:
-            # 搜索英文代码
-            english_results = await self._search_code(
-                query=english_query,
-                language=language,
-                per_page=per_page
-            )
-            
-            # 搜索中文代码
-            chinese_results = await self._search_code(
-                query=chinese_query,
-                language=language,
-                per_page=per_page
-            )
-            
-            # 合并结果，去除重复项
-            merged_results = []
-            seen_files = set()
-            
-            # 优先添加英文结果
-            for item in english_results:
-                # 使用文件URL作为唯一标识
-                file_url = item.get('html_url', '')
-                if file_url and file_url not in seen_files:
-                    seen_files.add(file_url)
-                    merged_results.append(item)
-            
-            # 添加中文结果（排除重复）
-            for item in chinese_results:
-                file_url = item.get('html_url', '')
-                if file_url and file_url not in seen_files:
-                    seen_files.add(file_url)
-                    merged_results.append(item)
-            
-            # 对结果进行排序，优先显示匹配度高的结果
-            # 由于无法直接获取匹配度，这里使用仓库的星标数作为替代指标
-            merged_results.sort(key=lambda x: x.get('repository', {}).get('stargazers_count', 0), reverse=True)
-            
-            return merged_results[:per_page]  # 返回合并后的前per_page个结果
-        except Exception as e:
-            print(f"双语代码搜索出错: {str(e)}")
-            return []
-
-    async def _search_users(self, query: str, per_page: int = 30) -> List[Dict]:
-        """搜索用户"""
-        try:
-            result = await self.github.search_users(
-                query=query,
-                per_page=per_page
-            )
-            
-            if result and "items" in result:
-                return result["items"]
-            return []
-        except Exception as e:
-            print(f"用户搜索出错: {str(e)}")
-            return []
-
-    async def _search_bilingual_users(self, english_query: str, chinese_query: str, per_page: int = 30) -> List[Dict]:
-        """同时搜索中英文用户并合并结果"""
-        try:
-            # 搜索英文用户
-            english_results = await self._search_users(
-                query=english_query,
-                per_page=per_page
-            )
-            
-            # 搜索中文用户
-            chinese_results = await self._search_users(
-                query=chinese_query,
-                per_page=per_page
-            )
-            
-            # 合并结果，去除重复项
-            merged_results = []
-            seen_users = set()
-            
-            # 优先添加英文结果
-            for user in english_results:
-                user_id = user.get('id')
-                if user_id and user_id not in seen_users:
-                    seen_users.add(user_id)
-                    merged_results.append(user)
-            
-            # 添加中文结果（排除重复）
-            for user in chinese_results:
-                user_id = user.get('id')
-                if user_id and user_id not in seen_users:
-                    seen_users.add(user_id)
-                    merged_results.append(user)
-            
-            # 按关注者数量进行排序
-            merged_results.sort(key=lambda x: x.get('followers', 0), reverse=True)
-            
-            return merged_results[:per_page]  # 返回合并后的前per_page个结果
-        except Exception as e:
-            print(f"双语用户搜索出错: {str(e)}")
-            return []
-
-    async def _search_topics(self, query: str, per_page: int = 30) -> List[Dict]:
-        """搜索主题"""
-        try:
-            result = await self.github.search_topics(
-                query=query,
-                per_page=per_page
-            )
-            
-            if result and "items" in result:
-                return result["items"]
-            return []
-        except Exception as e:
-            print(f"主题搜索出错: {str(e)}")
-            return []
-
-    async def _search_bilingual_topics(self, english_query: str, chinese_query: str, per_page: int = 30) -> List[Dict]:
-        """同时搜索中英文主题并合并结果"""
-        try:
-            # 搜索英文主题
-            english_results = await self._search_topics(
-                query=english_query,
-                per_page=per_page
-            )
-            
-            # 搜索中文主题
-            chinese_results = await self._search_topics(
-                query=chinese_query,
-                per_page=per_page
-            )
-            
-            # 合并结果，去除重复项
-            merged_results = []
-            seen_topics = set()
-            
-            # 优先添加英文结果
-            for topic in english_results:
-                topic_name = topic.get('name')
-                if topic_name and topic_name not in seen_topics:
-                    seen_topics.add(topic_name)
-                    merged_results.append(topic)
-            
-            # 添加中文结果（排除重复）
-            for topic in chinese_results:
-                topic_name = topic.get('name')
-                if topic_name and topic_name not in seen_topics:
-                    seen_topics.add(topic_name)
-                    merged_results.append(topic)
-            
-            # 可以按流行度进行排序（如果有）
-            if merged_results and 'featured' in merged_results[0]:
-                merged_results.sort(key=lambda x: x.get('featured', False), reverse=True)
-            
-            return merged_results[:per_page]  # 返回合并后的前per_page个结果
-        except Exception as e:
-            print(f"双语主题搜索出错: {str(e)}")
-            return []
-
-    async def _get_repo_details(self, repos: List[Dict]) -> List[Dict]:
-        """获取仓库详细信息"""
-        enhanced_repos = []
-        
-        for repo in repos:
-            try:
-                # 获取README信息
-                owner = repo.get('owner', {}).get('login') if repo.get('owner') is not None else None
-                repo_name = repo.get('name')
-                
-                if owner and repo_name:
-                    readme = await self.github.get_repo_readme(owner, repo_name)
-                    if readme and "decoded_content" in readme:
-                        # 提取README的前1000个字符作为摘要
-                        repo['readme_excerpt'] = readme["decoded_content"][:1000] + "..."
-                    
-                    # 获取语言使用情况
-                    languages = await self.github.get_repository_languages(owner, repo_name)
-                    if languages:
-                        repo['languages_detail'] = languages
-                    
-                    # 获取最新发布版本
-                    releases = await self.github.get_repo_releases(owner, repo_name, per_page=1)
-                    if releases and len(releases) > 0:
-                        repo['latest_release'] = releases[0]
-                    
-                    # 获取主题标签
-                    topics = await self.github.get_repo_topics(owner, repo_name)
-                    if topics and "names" in topics:
-                        repo['topics'] = topics["names"]
-                
-                enhanced_repos.append(repo)
-            except Exception as e:
-                print(f"获取仓库 {repo.get('full_name')} 详情时出错: {str(e)}")
-                enhanced_repos.append(repo)  # 添加原始仓库信息
-                
-        return enhanced_repos
-
-    def _format_repos(self, repos: List[Dict]) -> str:
-        """格式化仓库列表"""
-        formatted = []
-        
-        for i, repo in enumerate(repos, 1):
-            # 构建仓库URL
-            repo_url = repo.get('html_url', '')
-            
-            # 构建完整的引用
-            reference = (
-                f"{i}. **{repo.get('full_name', '')}**\n"
-                f"   - 描述: {repo.get('description', 'N/A')}\n"
-                f"   - 语言: {repo.get('language', 'N/A')}\n"
-                f"   - 星标: {repo.get('stargazers_count', 0)}\n"
-                f"   - Fork数: {repo.get('forks_count', 0)}\n"
-                f"   - 更新时间: {repo.get('updated_at', 'N/A')[:10]}\n"
-                f"   - 创建时间: {repo.get('created_at', 'N/A')[:10]}\n"
-                f"   - URL: <a href='{repo_url}' target='_blank'>{repo_url}</a>\n"
-            )
-            
-            # 添加主题标签(如果有)
-            if repo.get('topics'):
-                topics_str = ", ".join(repo.get('topics'))
-                reference += f"   - 主题标签: {topics_str}\n"
-                
-            # 添加最新发布版本(如果有)
-            if repo.get('latest_release'):
-                release = repo.get('latest_release')
-                reference += f"   - 最新版本: {release.get('tag_name', 'N/A')} ({release.get('published_at', 'N/A')[:10]})\n"
-                
-            # 添加README摘要(如果有)
-            if repo.get('readme_excerpt'):
-                # 截断README，只取前300个字符
-                readme_short = repo.get('readme_excerpt')[:300].replace('\n', ' ')
-                reference += f"   - README摘要: {readme_short}...\n"
-                
-            formatted.append(reference)
-            
-        return "\n".join(formatted)
-
-    def _generate_apology_prompt(self, criteria: SearchCriteria) -> str:
-        """生成道歉提示"""
-        return f"""很抱歉，我们未能找到与"{criteria.main_topic}"相关的GitHub项目。
-
-可能的原因：
-1. 搜索词过于具体或冷门
-2. 星标数要求过高
-3. 编程语言限制过于严格
-
-建议解决方案：
-   1. 尝试使用更通用的关键词
-   2. 降低最低星标数要求
-   3. 移除或更改编程语言限制
-请根据以上建议调整后重试。"""
-
-    def _get_current_time(self) -> str:
-        """获取当前时间信息"""
-        now = datetime.now()
-        return now.strftime("%Y年%m月%d日") 
--- a/crazy_functions/paper_fns/auto_git/handlers/code_handler.py
+++ b/crazy_functions/paper_fns/auto_git/handlers/code_handler.py
@@ -1,156 +0,0 @@
-from typing import List, Dict, Any
-from .base_handler import BaseHandler
-from ..query_analyzer import SearchCriteria
-import asyncio
-
-class CodeSearchHandler(BaseHandler):
-    """代码搜索处理器"""
-    
-    def __init__(self, github, llm_kwargs=None):
-        super().__init__(github, llm_kwargs)
-    
-    async def handle(
-        self,
-        criteria: SearchCriteria,
-        chatbot: List[List[str]],
-        history: List[List[str]],
-        system_prompt: str,
-        llm_kwargs: Dict[str, Any],
-        plugin_kwargs: Dict[str, Any],
-    ) -> str:
-        """处理代码搜索请求，返回最终的prompt"""
-        
-        search_params = self._get_search_params(plugin_kwargs)
-        
-        # 搜索代码
-        code_results = await self._search_bilingual_code(
-            english_query=criteria.github_params["query"],
-            chinese_query=criteria.github_params["chinese_query"],
-            language=criteria.language,
-            per_page=search_params['max_repos']
-        )
-        
-        if not code_results:
-            return self._generate_apology_prompt(criteria)
-            
-        # 获取代码文件内容
-        enhanced_code_results = await self._get_code_details(code_results[:search_params['max_details']])
-        self.ranked_repos = [item["repository"] for item in enhanced_code_results if "repository" in item]
-        
-        if not enhanced_code_results:
-            return self._generate_apology_prompt(criteria)
-        
-        # 构建最终的prompt
-        current_time = self._get_current_time()
-        final_prompt = f"""当前时间: {current_time}
-
-基于用户对{criteria.main_topic}的查询，我找到了以下代码示例。
-
-代码搜索结果:
-{self._format_code_results(enhanced_code_results)}
-
-请提供:
-
-1. 对于搜索的"{criteria.main_topic}"主题的综合解释:
-   - 概念和原理介绍
-   - 常见实现方法和技术
-   - 最佳实践和注意事项
-
-2. 对每个代码示例:
-   - 解释代码的主要功能和实现方式
-   - 分析代码质量、可读性和效率
-   - 指出代码中的亮点和潜在改进空间
-   - 说明代码的适用场景
-
-3. 代码实现比较:
-   - 不同实现方法的优缺点
-   - 性能和可维护性分析
-   - 适用不同场景的实现建议
-
-4. 学习建议:
-   - 理解和使用这些代码需要的背景知识
-   - 如何扩展或改进所展示的代码
-   - 进一步学习相关技术的资源
-
-重要提示:
- 深入解释代码的核心逻辑和实现思路
- 提供专业、技术性的分析
- 优先关注代码的实现质量和技术价值
- 当代码实现有问题时，指出并提供改进建议
- 对于复杂代码，分解解释其组成部分
- 根据用户查询的具体问题提供针对性答案
- 所有链接请使用<a href='链接地址' target='_blank'>链接文本</a>格式，确保链接在新窗口打开
-
-使用markdown格式提供清晰的分节回复。
-"""
-        
-        return final_prompt
-
-    async def _get_code_details(self, code_results: List[Dict]) -> List[Dict]:
-        """获取代码详情"""
-        enhanced_results = []
-        
-        for item in code_results:
-            try:
-                repo = item.get('repository', {})
-                file_path = item.get('path', '')
-                repo_name = repo.get('full_name', '')
-                
-                if repo_name and file_path:
-                    owner, repo_name = repo_name.split('/')
-                    
-                    # 获取文件内容
-                    file_content = await self.github.get_file_content(owner, repo_name, file_path)
-                    if file_content and "decoded_content" in file_content:
-                        item['code_content'] = file_content["decoded_content"]
-                        
-                        # 获取仓库基本信息
-                        repo_details = await self.github.get_repo(owner, repo_name)
-                        if repo_details:
-                            item['repository'] = repo_details
-                
-                enhanced_results.append(item)
-            except Exception as e:
-                print(f"获取代码详情时出错: {str(e)}")
-                enhanced_results.append(item)  # 添加原始信息
-                
-        return enhanced_results
-
-    def _format_code_results(self, code_results: List[Dict]) -> str:
-        """格式化代码搜索结果"""
-        formatted = []
-        
-        for i, item in enumerate(code_results, 1):
-            # 构建仓库信息
-            repo = item.get('repository', {})
-            repo_name = repo.get('full_name', 'N/A')
-            repo_url = repo.get('html_url', '')
-            stars = repo.get('stargazers_count', 0)
-            language = repo.get('language', 'N/A')
-            
-            # 构建文件信息
-            file_path = item.get('path', 'N/A')
-            file_url = item.get('html_url', '')
-            
-            # 构建代码内容
-            code_content = item.get('code_content', '')
-            if code_content:
-                # 只显示前30行代码
-                code_lines = code_content.split("\n")
-                if len(code_lines) > 30:
-                    displayed_code = "\n".join(code_lines[:30]) + "\n... (代码太长已截断) ..."
-                else:
-                    displayed_code = code_content
-            else:
-                displayed_code = "(代码内容获取失败)"
-            
-            reference = (
-                f"### {i}. {file_path} (在 {repo_name} 中)\n\n"
-                f"- **仓库**: <a href='{repo_url}' target='_blank'>{repo_name}</a> (⭐ {stars}, 语言: {language})\n"
-                f"- **文件路径**: <a href='{file_url}' target='_blank'>{file_path}</a>\n\n"
-                f"```{language.lower()}\n{displayed_code}\n```\n\n"
-            )
-            
-            formatted.append(reference)
-            
-        return "\n".join(formatted) 
--- a/crazy_functions/paper_fns/auto_git/handlers/repo_handler.py
+++ b/crazy_functions/paper_fns/auto_git/handlers/repo_handler.py
@@ -1,192 +0,0 @@
-from typing import List, Dict, Any
-from .base_handler import BaseHandler
-from ..query_analyzer import SearchCriteria
-import asyncio
-
-class RepositoryHandler(BaseHandler):
-    """仓库搜索处理器"""
-    
-    def __init__(self, github, llm_kwargs=None):
-        super().__init__(github, llm_kwargs)
-    
-    async def handle(
-        self,
-        criteria: SearchCriteria,
-        chatbot: List[List[str]],
-        history: List[List[str]],
-        system_prompt: str,
-        llm_kwargs: Dict[str, Any],
-        plugin_kwargs: Dict[str, Any],
-    ) -> str:
-        """处理仓库搜索请求，返回最终的prompt"""
-        
-        search_params = self._get_search_params(plugin_kwargs)
-        
-        # 如果是特定仓库查询
-        if criteria.repo_id:
-            try:
-                owner, repo = criteria.repo_id.split('/')
-                repo_details = await self.github.get_repo(owner, repo)
-                if repo_details:
-                    # 获取推荐的相似仓库
-                    similar_repos = await self.github.get_repo_recommendations(criteria.repo_id, limit=5)
-                    
-                    # 添加详细信息
-                    all_repos = [repo_details] + similar_repos
-                    enhanced_repos = await self._get_repo_details(all_repos)
-                    
-                    self.ranked_repos = enhanced_repos
-                    
-                    # 构建最终的prompt
-                    current_time = self._get_current_time()
-                    final_prompt = self._build_repo_detail_prompt(enhanced_repos[0], enhanced_repos[1:], current_time)
-                    return final_prompt
-                else:
-                    return self._generate_apology_prompt(criteria)
-            except Exception as e:
-                print(f"处理特定仓库时出错: {str(e)}")
-                return self._generate_apology_prompt(criteria)
-        
-        # 一般仓库搜索
-        repos = await self._search_bilingual_repositories(
-            english_query=criteria.github_params["query"],
-            chinese_query=criteria.github_params["chinese_query"],
-            language=criteria.language,
-            min_stars=criteria.min_stars,
-            per_page=search_params['max_repos']
-        )
-        
-        if not repos:
-            return self._generate_apology_prompt(criteria)
-        
-        # 获取仓库详情
-        enhanced_repos = await self._get_repo_details(repos[:search_params['max_details']])  # 使用max_details参数
-        self.ranked_repos = enhanced_repos
-        
-        if not enhanced_repos:
-            return self._generate_apology_prompt(criteria)
-        
-        # 构建最终的prompt
-        current_time = self._get_current_time()
-        final_prompt = f"""当前时间: {current_time}
-
-基于用户对{criteria.main_topic}的兴趣，以下是相关的GitHub仓库。
-
-可供推荐的GitHub仓库:
-{self._format_repos(enhanced_repos)}
-
-请提供:
-1. 按功能、用途或成熟度对仓库进行分组
-
-2. 对每个仓库：
-   - 简要描述其主要功能和用途
-   - 分析其技术特点和优势
-   - 说明其适用场景和使用难度
-   - 指出其与同类产品相比的独特优势
-   - 解释其星标数量和活跃度代表的意义
-
-3. 使用建议：
-   - 新手最适合入门的仓库
-   - 生产环境中最稳定可靠的选择
-   - 最新技术栈或创新方案的代表
-   - 学习特定技术的最佳资源
-
-4. 相关资源：
-   - 学习这些项目需要的前置知识
-   - 项目间的关联和技术栈兼容性
-   - 可能的使用组合方案
-
-重要提示:
- 重点解释为什么每个仓库值得关注
- 突出项目间的关联性和差异性
- 考虑用户不同水平的需求(初学者vs专业人士)
- 在介绍项目时，使用<a href='链接' target='_blank'>文本</a>格式，确保链接在新窗口打开
- 根据仓库的活跃度、更新频率、维护状态提供使用建议
- 仅基于提供的信息，不要做无根据的猜测
- 在信息缺失或不明确时，坦诚说明
-
-使用markdown格式提供清晰的分节回复。
-"""
-        
-        return final_prompt
-
-    def _build_repo_detail_prompt(self, main_repo: Dict, similar_repos: List[Dict], current_time: str) -> str:
-        """构建仓库详情prompt"""
-        
-        # 提取README摘要
-        readme_content = "未提供"
-        if main_repo.get('readme_excerpt'):
-            readme_content = main_repo.get('readme_excerpt')
-            
-        # 构建语言分布
-        languages = main_repo.get('languages_detail', {})
-        lang_distribution = []
-        if languages:
-            total = sum(languages.values())
-            for lang, bytes_val in languages.items():
-                percentage = (bytes_val / total) * 100
-                lang_distribution.append(f"{lang}: {percentage:.1f}%")
-        
-        lang_str = "未知"
-        if lang_distribution:
-            lang_str = ", ".join(lang_distribution)
-            
-        # 构建最终prompt
-        prompt = f"""当前时间: {current_time}
-
-## 主要仓库信息
-
-### {main_repo.get('full_name')}
-
- **描述**: {main_repo.get('description', '未提供')}
- **星标数**: {main_repo.get('stargazers_count', 0)}
- **Fork数**: {main_repo.get('forks_count', 0)}
- **Watch数**: {main_repo.get('watchers_count', 0)}
- **Issues数**: {main_repo.get('open_issues_count', 0)}
- **语言分布**: {lang_str}
- **许可证**: {main_repo.get('license', {}).get('name', '未指定') if main_repo.get('license') is not None else '未指定'}
- **创建时间**: {main_repo.get('created_at', '')[:10]}
- **最近更新**: {main_repo.get('updated_at', '')[:10]}
- **主题标签**: {', '.join(main_repo.get('topics', ['无']))}
- **GitHub链接**: <a href='{main_repo.get('html_url')}' target='_blank'>链接</a>
-
-### README摘要:
-{readme_content}
-
-## 类似仓库:
-{self._format_repos(similar_repos)}
-
-请提供以下内容:
-
-1. **项目概述**
-   - 详细解释{main_repo.get('name', '')}项目的主要功能和用途
-   - 分析其技术特点、架构和实现原理
-   - 讨论其在所属领域的地位和影响力
-   - 评估项目成熟度和稳定性
-
-2. **优势与特点**
-   - 与同类项目相比的独特优势
-   - 显著的技术创新或设计模式
-   - 值得学习或借鉴的代码实践
-
-3. **使用场景**
-   - 最适合的应用场景
-   - 潜在的使用限制和注意事项
-   - 入门门槛和学习曲线评估
-   - 产品级应用的可行性分析
-
-4. **资源与生态**
-   - 相关学习资源推荐
-   - 配套工具和库的建议
-   - 社区支持和活跃度评估
-
-5. **类似项目对比**
-   - 与列出的类似项目的详细对比
-   - 不同场景下的最佳选择建议
-   - 潜在的互补使用方案
-
-提示：所有链接请使用<a href='链接地址' target='_blank'>链接文本</a>格式，确保链接在新窗口打开。
-
-请以专业、客观的技术分析角度回答，使用markdown格式提供结构化信息。
-"""
-        return prompt 
--- a/crazy_functions/paper_fns/auto_git/handlers/topic_handler.py
+++ b/crazy_functions/paper_fns/auto_git/handlers/topic_handler.py
@@ -1,217 +0,0 @@
-from typing import List, Dict, Any
-from .base_handler import BaseHandler
-from ..query_analyzer import SearchCriteria
-import asyncio
-
-class TopicHandler(BaseHandler):
-    """主题搜索处理器"""
-    
-    def __init__(self, github, llm_kwargs=None):
-        super().__init__(github, llm_kwargs)
-    
-    async def handle(
-        self,
-        criteria: SearchCriteria,
-        chatbot: List[List[str]],
-        history: List[List[str]],
-        system_prompt: str,
-        llm_kwargs: Dict[str, Any],
-        plugin_kwargs: Dict[str, Any],
-    ) -> str:
-        """处理主题搜索请求，返回最终的prompt"""
-        
-        search_params = self._get_search_params(plugin_kwargs)
-        
-        # 搜索主题
-        topics = await self._search_bilingual_topics(
-            english_query=criteria.github_params["query"],
-            chinese_query=criteria.github_params["chinese_query"],
-            per_page=search_params['max_repos']
-        )
-        
-        if not topics:
-            # 尝试用主题搜索仓库
-            search_query = criteria.github_params["query"]
-            chinese_search_query = criteria.github_params["chinese_query"]
-            if "topic:" not in search_query:
-                search_query += " topic:" + criteria.main_topic.replace(" ", "-")
-            if "topic:" not in chinese_search_query:
-                chinese_search_query += " topic:" + criteria.main_topic.replace(" ", "-")
-            
-            repos = await self._search_bilingual_repositories(
-                english_query=search_query,
-                chinese_query=chinese_search_query,
-                language=criteria.language,
-                min_stars=criteria.min_stars,
-                per_page=search_params['max_repos']
-            )
-            
-            if not repos:
-                return self._generate_apology_prompt(criteria)
-                
-            # 获取仓库详情
-            enhanced_repos = await self._get_repo_details(repos[:10])
-            self.ranked_repos = enhanced_repos
-            
-            if not enhanced_repos:
-                return self._generate_apology_prompt(criteria)
-                
-            # 构建基于主题的仓库列表prompt
-            current_time = self._get_current_time()
-            final_prompt = f"""当前时间: {current_time}
-
-基于用户对主题"{criteria.main_topic}"的查询，我找到了以下相关GitHub仓库。
-
-主题相关仓库:
-{self._format_repos(enhanced_repos)}
-
-请提供:
-
-1. 主题综述:
-   - "{criteria.main_topic}"主题的概述和重要性
-   - 该主题在技术领域中的应用和发展趋势
-   - 主题相关的主要技术栈和知识体系
-
-2. 仓库分析:
-   - 按功能、技术栈或应用场景对仓库进行分类
-   - 每个仓库在该主题领域的定位和贡献
-   - 不同仓库间的技术路线对比
-
-3. 学习路径建议:
-   - 初学者入门该主题的推荐仓库和学习顺序
-   - 进阶学习的关键仓库和技术要点
-   - 实际应用中的最佳实践选择
-
-4. 技术生态分析:
-   - 该主题下的主流工具和库
-   - 社区活跃度和维护状况
-   - 与其他相关技术的集成方案
-
-重要提示:
- 主题"{criteria.main_topic}"是用户查询的核心，请围绕此主题展开分析
- 注重仓库质量评估和使用建议
- 提供基于事实的客观技术分析
- 在介绍仓库时使用<a href='链接地址' target='_blank'>链接文本</a>格式，确保链接在新窗口打开
- 考虑不同技术水平用户的需求
-
-使用markdown格式提供清晰的分节回复。
-"""
-            return final_prompt
-            
-        # 如果找到了主题，则获取主题下的热门仓库
-        topic_repos = []
-        for topic in topics[:5]:  # 增加到5个主题
-            topic_name = topic.get('name', '')
-            if topic_name:
-                # 搜索该主题下的仓库
-                repos = await self._search_repositories(
-                    query=f"topic:{topic_name}",
-                    language=criteria.language,
-                    min_stars=criteria.min_stars,
-                    per_page=20  # 每个主题最多20个仓库
-                )
-                
-                if repos:
-                    for repo in repos:
-                        repo['topic_source'] = topic_name
-                        topic_repos.append(repo)
-        
-        if not topic_repos:
-            return self._generate_apology_prompt(criteria)
-            
-        # 获取前N个仓库的详情
-        enhanced_repos = await self._get_repo_details(topic_repos[:search_params['max_details']])
-        self.ranked_repos = enhanced_repos
-        
-        if not enhanced_repos:
-            return self._generate_apology_prompt(criteria)
-            
-        # 构建最终的prompt
-        current_time = self._get_current_time()
-        final_prompt = f"""当前时间: {current_time}
-
-基于用户对"{criteria.main_topic}"主题的查询，我找到了以下相关GitHub主题和仓库。
-
-主题相关仓库:
-{self._format_topic_repos(enhanced_repos)}
-
-请提供:
-
-1. 主题概述:
-   - 对"{criteria.main_topic}"相关主题的介绍和技术背景
-   - 这些主题在软件开发中的重要性和应用范围
-   - 主题间的关联性和技术演进路径
-
-2. 精选仓库分析:
-   - 每个主题下最具代表性的仓库详解
-   - 仓库的技术亮点和创新点
-   - 使用场景和技术成熟度评估
-
-3. 技术趋势分析:
-   - 基于主题和仓库活跃度的技术发展趋势
-   - 新兴解决方案和传统方案的对比
-   - 未来可能的技术方向预测
-
-4. 实践建议:
-   - 不同应用场景下的最佳仓库选择
-   - 学习路径和资源推荐
-   - 实际项目中的应用策略
-
-重要提示:
- 将分析重点放在主题的技术内涵和价值上
- 突出主题间的关联性和技术演进脉络
- 提供基于数据(星标数、更新频率等)的客观分析
- 考虑不同技术背景用户的需求
- 所有链接请使用<a href='链接地址' target='_blank'>链接文本</a>格式，确保链接在新窗口打开
-
-使用markdown格式提供清晰的分节回复。
-"""
-        
-        return final_prompt
-        
-    def _format_topic_repos(self, repos: List[Dict]) -> str:
-        """按主题格式化仓库列表"""
-        # 按主题分组
-        topics_dict = {}
-        for repo in repos:
-            topic = repo.get('topic_source', '其他')
-            if topic not in topics_dict:
-                topics_dict[topic] = []
-            topics_dict[topic].append(repo)
-        
-        # 格式化输出
-        formatted = []
-        for topic, topic_repos in topics_dict.items():
-            formatted.append(f"## 主题: {topic}\n")
-            
-            for i, repo in enumerate(topic_repos, 1):
-                # 构建仓库URL
-                repo_url = repo.get('html_url', '')
-                
-                # 构建引用
-                reference = (
-                    f"{i}. **{repo.get('full_name', '')}**\n"
-                    f"   - 描述: {repo.get('description', 'N/A')}\n"
-                    f"   - 语言: {repo.get('language', 'N/A')}\n"
-                    f"   - 星标: {repo.get('stargazers_count', 0)}\n"
-                    f"   - Fork数: {repo.get('forks_count', 0)}\n"
-                    f"   - 更新时间: {repo.get('updated_at', 'N/A')[:10]}\n"
-                    f"   - URL: <a href='{repo_url}' target='_blank'>{repo_url}</a>\n"
-                )
-                
-                # 添加主题标签(如果有)
-                if repo.get('topics'):
-                    topics_str = ", ".join(repo.get('topics'))
-                    reference += f"   - 主题标签: {topics_str}\n"
-                
-                # 添加README摘要(如果有)
-                if repo.get('readme_excerpt'):
-                    # 截断README，只取前200个字符
-                    readme_short = repo.get('readme_excerpt')[:200].replace('\n', ' ')
-                    reference += f"   - README摘要: {readme_short}...\n"
-                
-                formatted.append(reference)
-            
-            formatted.append("\n")  # 主题之间添加空行
-            
-        return "\n".join(formatted) 
--- a/crazy_functions/paper_fns/auto_git/handlers/user_handler.py
+++ b/crazy_functions/paper_fns/auto_git/handlers/user_handler.py
@@ -1,164 +0,0 @@
-from typing import List, Dict, Any
-from .base_handler import BaseHandler
-from ..query_analyzer import SearchCriteria
-import asyncio
-
-class UserSearchHandler(BaseHandler):
-    """用户搜索处理器"""
-    
-    def __init__(self, github, llm_kwargs=None):
-        super().__init__(github, llm_kwargs)
-    
-    async def handle(
-        self,
-        criteria: SearchCriteria,
-        chatbot: List[List[str]],
-        history: List[List[str]],
-        system_prompt: str,
-        llm_kwargs: Dict[str, Any],
-        plugin_kwargs: Dict[str, Any],
-    ) -> str:
-        """处理用户搜索请求，返回最终的prompt"""
-        
-        search_params = self._get_search_params(plugin_kwargs)
-        
-        # 搜索用户
-        users = await self._search_bilingual_users(
-            english_query=criteria.github_params["query"],
-            chinese_query=criteria.github_params["chinese_query"],
-            per_page=search_params['max_repos']
-        )
-        
-        if not users:
-            return self._generate_apology_prompt(criteria)
-            
-        # 获取用户详情和仓库
-        enhanced_users = await self._get_user_details(users[:search_params['max_details']])
-        self.ranked_repos = []  # 添加用户top仓库进行展示
-        
-        for user in enhanced_users:
-            if user.get('top_repos'):
-                self.ranked_repos.extend(user.get('top_repos'))
-        
-        if not enhanced_users:
-            return self._generate_apology_prompt(criteria)
-        
-        # 构建最终的prompt
-        current_time = self._get_current_time()
-        final_prompt = f"""当前时间: {current_time}
-
-基于用户对{criteria.main_topic}的查询，我找到了以下GitHub用户。
-
-GitHub用户搜索结果:
-{self._format_users(enhanced_users)}
-
-请提供:
-
-1. 用户综合分析:
-   - 各开发者的专业领域和技术专长
-   - 他们在GitHub开源社区的影响力
-   - 技术实力和项目质量评估
-
-2. 对每位开发者:
-   - 其主要贡献领域和技术栈
-   - 代表性项目及其价值
-   - 编程风格和技术特点
-   - 在相关领域的影响力
-
-3. 项目推荐:
-   - 针对用户查询的最有价值项目
-   - 值得学习和借鉴的代码实践
-   - 不同用户项目的相互补充关系
-
-4. 如何学习和使用:
-   - 如何从这些开发者项目中学习
-   - 最适合入门学习的项目
-   - 进阶学习的路径建议
-
-重要提示:
- 关注开发者的技术专长和核心贡献
- 分析其开源项目的技术价值
- 根据用户的原始查询提供相关建议
- 避免过度赞美或主观评价
- 基于事实数据(项目数、星标数等)进行客观分析
- 所有链接请使用<a href='链接地址' target='_blank'>链接文本</a>格式，确保链接在新窗口打开
-
-使用markdown格式提供清晰的分节回复。
-"""
-        
-        return final_prompt
-
-    async def _get_user_details(self, users: List[Dict]) -> List[Dict]:
-        """获取用户详情和仓库"""
-        enhanced_users = []
-        
-        for user in users:
-            try:
-                username = user.get('login')
-                
-                if username:
-                    # 获取用户详情
-                    user_details = await self.github.get_user(username)
-                    if user_details:
-                        user.update(user_details)
-                    
-                    # 获取用户仓库
-                    repos = await self.github.get_user_repos(
-                        username,
-                        sort="stars",
-                        per_page=10  # 增加到10个仓库
-                    )
-                    if repos:
-                        user['top_repos'] = repos
-                
-                enhanced_users.append(user)
-            except Exception as e:
-                print(f"获取用户 {user.get('login')} 详情时出错: {str(e)}")
-                enhanced_users.append(user)  # 添加原始信息
-                
-        return enhanced_users
-
-    def _format_users(self, users: List[Dict]) -> str:
-        """格式化用户列表"""
-        formatted = []
-        
-        for i, user in enumerate(users, 1):
-            # 构建用户信息
-            username = user.get('login', 'N/A')
-            name = user.get('name', username)
-            profile_url = user.get('html_url', '')
-            bio = user.get('bio', '无简介')
-            followers = user.get('followers', 0)
-            public_repos = user.get('public_repos', 0)
-            company = user.get('company', '未指定')
-            location = user.get('location', '未指定')
-            blog = user.get('blog', '')
-            
-            user_info = (
-                f"### {i}. {name} (@{username})\n\n"
-                f"- **简介**: {bio}\n"
-                f"- **关注者**: {followers} | **公开仓库**: {public_repos}\n"
-                f"- **公司**: {company} | **地点**: {location}\n"
-                f"- **个人网站**: {blog}\n"
-                f"- **GitHub**: <a href='{profile_url}' target='_blank'>{username}</a>\n\n"
-            )
-            
-            # 添加用户的热门仓库
-            top_repos = user.get('top_repos', [])
-            if top_repos:
-                user_info += "**热门仓库**:\n\n"
-                for repo in top_repos:
-                    repo_name = repo.get('name', '')
-                    repo_url = repo.get('html_url', '')
-                    repo_desc = repo.get('description', '无描述')
-                    repo_stars = repo.get('stargazers_count', 0)
-                    repo_language = repo.get('language', '未指定')
-                    
-                    user_info += (
-                        f"- <a href='{repo_url}' target='_blank'>{repo_name}</a> - ⭐ {repo_stars}, {repo_language}\n"
-                        f"  {repo_desc}\n\n"
-                    )
-            
-            formatted.append(user_info)
-            
-        return "\n".join(formatted) 
--- a/crazy_functions/paper_fns/auto_git/query_analyzer.py
+++ b/crazy_functions/paper_fns/auto_git/query_analyzer.py
@@ -1,356 +0,0 @@
-from typing import Dict, List
-from dataclasses import dataclass
-import re
-
-@dataclass
-class SearchCriteria:
-    """搜索条件"""
-    query_type: str  # 查询类型: repo/code/user/topic
-    main_topic: str  # 主题
-    sub_topics: List[str]  # 子主题列表
-    language: str  # 编程语言
-    min_stars: int  # 最少星标数
-    github_params: Dict  # GitHub搜索参数
-    original_query: str = ""  # 原始查询字符串
-    repo_id: str = ""  # 特定仓库ID或名称
-
-class QueryAnalyzer:
-    """查询分析器"""
-
-    # 响应索引常量
-    BASIC_QUERY_INDEX = 0
-    GITHUB_QUERY_INDEX = 1
-    
-    def __init__(self):
-        self.valid_types = {
-            "repo": ["repository", "project", "library", "framework", "tool"],
-            "code": ["code", "snippet", "implementation", "function", "class", "algorithm"],
-            "user": ["user", "developer", "organization", "contributor", "maintainer"],
-            "topic": ["topic", "category", "tag", "field", "area", "domain"]
-        }
-
-    def analyze_query(self, query: str, chatbot: List, llm_kwargs: Dict):
-        """分析查询意图"""
-        from crazy_functions.crazy_utils import \
-            request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency as request_gpt
-        
-        # 1. 基本查询分析
-        type_prompt = f"""请分析这个与GitHub相关的查询，并严格按照以下XML格式回答：
-
-查询: {query}
-
-说明:
-1. 你的回答必须使用下面显示的XML标签，不要有任何标签外的文本
-2. 从以下选项中选择查询类型: repo/code/user/topic
-   - repo: 用于查找仓库、项目、框架或库
-   - code: 用于查找代码片段、函数实现或算法
-   - user: 用于查找用户、开发者或组织
-   - topic: 用于查找主题、类别或领域相关项目
-3. 识别主题和子主题
-4. 识别首选编程语言(如果有)
-5. 确定最低星标数(如果适用)
-
-必需格式:
-<query_type>此处回答</query_type>
-<main_topic>此处回答</main_topic>
-<sub_topics>子主题1, 子主题2, ...</sub_topics>
-<language>此处回答</language>
-<min_stars>此处回答</min_stars>
-
-示例回答:
-
-1. 仓库查询:
-查询: "查找有至少1000颗星的Python web框架"
-<query_type>repo</query_type>
-<main_topic>web框架</main_topic>
-<sub_topics>后端开发, HTTP服务器, ORM</sub_topics>
-<language>Python</language>
-<min_stars>1000</min_stars>
-
-2. 代码查询:
-查询: "如何用JavaScript实现防抖函数"
-<query_type>code</query_type>
-<main_topic>防抖函数</main_topic>
-<sub_topics>事件处理, 性能优化, 函数节流</sub_topics>
-<language>JavaScript</language>
-<min_stars>0</min_stars>"""
-
-        # 2. 生成英文搜索条件
-        github_prompt = f"""Optimize the following GitHub search query:
-
-Query: {query}
-
-Task: Convert the natural language query into an optimized GitHub search query.
-Please use English, regardless of the language of the input query.
-
-Available search fields and filters:
-1. Basic fields:
-   - in:name - Search in repository names
-   - in:description - Search in repository descriptions
-   - in:readme - Search in README files
-   - in:topic - Search in topics
-   - language:X - Filter by programming language
-   - user:X - Repositories from a specific user
-   - org:X - Repositories from a specific organization
-
-2. Code search fields:
-   - extension:X - Filter by file extension
-   - path:X - Filter by path
-   - filename:X - Filter by filename
-
-3. Metric filters:
-   - stars:>X - Has more than X stars
-   - forks:>X - Has more than X forks
-   - size:>X - Size greater than X KB
-   - created:>YYYY-MM-DD - Created after a specific date
-   - pushed:>YYYY-MM-DD - Updated after a specific date
-
-4. Other filters:
-   - is:public/private - Public or private repositories
-   - archived:true/false - Archived or not archived
-   - license:X - Specific license
-   - topic:X - Contains specific topic tag
-
-Examples:
-
-1. Query: "Find Python machine learning libraries with at least 1000 stars"
-<query>machine learning in:description language:python stars:>1000</query>
-
-2. Query: "Recently updated React UI component libraries"
-<query>UI components library in:readme in:description language:javascript topic:react pushed:>2023-01-01</query>
-
-3. Query: "Open source projects developed by Facebook"
-<query>org:facebook is:public</query>
-
-4. Query: "Depth-first search implementation in JavaScript"
-<query>depth first search in:file language:javascript</query>
-
-Please analyze the query and answer using only the XML tag:
-<query>Provide the optimized GitHub search query, using appropriate fields and operators</query>"""
-
-        # 3. 生成中文搜索条件
-        chinese_github_prompt = f"""优化以下GitHub搜索查询:
-
-查询: {query}
-
-任务: 将自然语言查询转换为优化的GitHub搜索查询语句。
-为了搜索中文内容，请提取原始查询的关键词并使用中文形式，同时保留GitHub特定的搜索语法为英文。
-
-可用的搜索字段和过滤器:
-1. 基本字段:
-   - in:name - 在仓库名称中搜索
-   - in:description - 在仓库描述中搜索
-   - in:readme - 在README文件中搜索
-   - in:topic - 在主题中搜索
-   - language:X - 按编程语言筛选
-   - user:X - 特定用户的仓库
-   - org:X - 特定组织的仓库
-
-2. 代码搜索字段:
-   - extension:X - 按文件扩展名筛选
-   - path:X - 按路径筛选
-   - filename:X - 按文件名筛选
-
-3. 指标过滤器:
-   - stars:>X - 有超过X颗星
-   - forks:>X - 有超过X个分支
-   - size:>X - 大小超过X KB
-   - created:>YYYY-MM-DD - 在特定日期后创建
-   - pushed:>YYYY-MM-DD - 在特定日期后更新
-
-4. 其他过滤器:
-   - is:public/private - 公开或私有仓库
-   - archived:true/false - 已归档或未归档
-   - license:X - 特定许可证
-   - topic:X - 含特定主题标签
-
-示例:
-
-1. 查询: "找有关机器学习的Python库，至少1000颗星"
-<query>机器学习 in:description language:python stars:>1000</query>
-
-2. 查询: "最近更新的React UI组件库"
-<query>UI 组件库 in:readme in:description language:javascript topic:react pushed:>2023-01-01</query>
-
-3. 查询: "微信小程序开发框架"
-<query>微信小程序 开发框架 in:name in:description in:readme</query>
-
-请分析查询并仅使用XML标签回答:
-<query>提供优化的GitHub搜索查询，使用适当的字段和运算符，保留中文关键词</query>"""
-
-        try:
-            # 构建提示数组
-            prompts = [
-                type_prompt,
-                github_prompt,
-                chinese_github_prompt,
-            ]
-
-            show_messages = [
-                "分析查询类型...",
-                "优化英文GitHub搜索参数...",
-                "优化中文GitHub搜索参数...",
-            ]
-
-            sys_prompts = [
-                "你是一个精通GitHub生态系统的专家，擅长分析与GitHub相关的查询。",
-                "You are a GitHub search expert, specialized in converting natural language queries into optimized GitHub search queries in English.",
-                "你是一个GitHub搜索专家，擅长处理查询并保留中文关键词进行搜索。",
-            ]
-            
-            # 使用同步方式调用LLM
-            responses = yield from request_gpt(
-                inputs_array=prompts,
-                inputs_show_user_array=show_messages,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history_array=[[] for _ in prompts],
-                sys_prompt_array=sys_prompts,
-                max_workers=3
-            )
-
-            # 从收集的响应中提取我们需要的内容
-            extracted_responses = []
-            for i in range(len(prompts)):
-                if (i * 2 + 1) < len(responses):
-                    response = responses[i * 2 + 1]
-                    if response is None:
-                        raise Exception(f"Response {i} is None")
-                    if not isinstance(response, str):
-                        try:
-                            response = str(response)
-                        except:
-                            raise Exception(f"Cannot convert response {i} to string")
-                    extracted_responses.append(response)
-                else:
-                    raise Exception(f"未收到第 {i + 1} 个响应")
-
-            # 解析基本信息
-            query_type = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "query_type")
-            if not query_type:
-                print(
-                    f"Debug - Failed to extract query_type. Response was: {extracted_responses[self.BASIC_QUERY_INDEX]}")
-                raise Exception("无法提取query_type标签内容")
-            query_type = query_type.lower()
-
-            main_topic = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "main_topic")
-            if not main_topic:
-                print(f"Debug - Failed to extract main_topic. Using query as fallback.")
-                main_topic = query
-
-            query_type = self._normalize_query_type(query_type, query)
-
-            # 提取子主题
-            sub_topics = []
-            sub_topics_text = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "sub_topics")
-            if sub_topics_text:
-                sub_topics = [topic.strip() for topic in sub_topics_text.split(",")]
-
-            # 提取语言
-            language = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "language")
-            
-            # 提取最低星标数
-            min_stars = 0
-            min_stars_text = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "min_stars")
-            if min_stars_text and min_stars_text.isdigit():
-                min_stars = int(min_stars_text)
-
-            # 解析GitHub搜索参数 - 英文
-            english_github_query = self._extract_tag(extracted_responses[self.GITHUB_QUERY_INDEX], "query")
-            
-            # 解析GitHub搜索参数 - 中文
-            chinese_github_query = self._extract_tag(extracted_responses[2], "query")
-            
-            # 构建GitHub参数
-            github_params = {
-                "query": english_github_query,
-                "chinese_query": chinese_github_query,
-                "sort": "stars",  # 默认按星标排序
-                "order": "desc",  # 默认降序
-                "per_page": 30,   # 默认每页30条
-                "page": 1         # 默认第1页
-            }
-            
-            # 检查是否为特定仓库查询
-            repo_id = ""
-            if "repo:" in english_github_query or "repository:" in english_github_query:
-                repo_match = re.search(r'(repo|repository):([a-zA-Z0-9_.-]+/[a-zA-Z0-9_.-]+)', english_github_query)
-                if repo_match:
-                    repo_id = repo_match.group(2)
-            
-            print(f"Debug - 提取的信息:")
-            print(f"查询类型: {query_type}")
-            print(f"主题: {main_topic}")
-            print(f"子主题: {sub_topics}")
-            print(f"语言: {language}")
-            print(f"最低星标数: {min_stars}")
-            print(f"英文GitHub参数: {english_github_query}")
-            print(f"中文GitHub参数: {chinese_github_query}")
-            print(f"特定仓库: {repo_id}")
-
-            # 更新返回的 SearchCriteria，包含中英文查询
-            return SearchCriteria(
-                query_type=query_type,
-                main_topic=main_topic,
-                sub_topics=sub_topics,
-                language=language,
-                min_stars=min_stars,
-                github_params=github_params,
-                original_query=query,
-                repo_id=repo_id
-            )
-
-        except Exception as e:
-            raise Exception(f"分析查询失败: {str(e)}")
-
-    def _normalize_query_type(self, query_type: str, query: str) -> str:
-        """规范化查询类型"""
-        if query_type in ["repo", "code", "user", "topic"]:
-            return query_type
-
-        query_lower = query.lower()
-        for type_name, keywords in self.valid_types.items():
-            for keyword in keywords:
-                if keyword in query_lower:
-                    return type_name
-
-        query_type_lower = query_type.lower()
-        for type_name, keywords in self.valid_types.items():
-            for keyword in keywords:
-                if keyword in query_type_lower:
-                    return type_name
-
-        return "repo"  # 默认返回repo类型
-
-    def _extract_tag(self, text: str, tag: str) -> str:
-        """提取标记内容"""
-        if not text:
-            return ""
-
-        # 标准XML格式（处理多行和特殊字符）
-        pattern = f"<{tag}>(.*?)</{tag}>"
-        match = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
-        if match:
-            content = match.group(1).strip()
-            if content:
-                return content
-
-        # 备用模式
-        patterns = [
-            rf"<{tag}>\s*([\s\S]*?)\s*</{tag}>",  # 标准XML格式
-            rf"<{tag}>([\s\S]*?)(?:</{tag}>|$)",  # 未闭合的标签
-            rf"[{tag}]([\s\S]*?)[/{tag}]",  # 方括号格式
-            rf"{tag}:\s*(.*?)(?=\n\w|$)",  # 冒号格式
-            rf"<{tag}>\s*(.*?)(?=<|$)"  # 部分闭合
-        ]
-
-        # 尝试所有模式
-        for pattern in patterns:
-            match = re.search(pattern, text, re.IGNORECASE | re.DOTALL)
-            if match:
-                content = match.group(1).strip()
-                if content:  # 确保提取的内容不为空
-                    return content
-
-        # 如果所有模式都失败，返回空字符串
-        return "" 
--- a/crazy_functions/paper_fns/auto_git/sources/github_source.py
+++ b/crazy_functions/paper_fns/auto_git/sources/github_source.py
@@ -1,701 +0,0 @@
-import aiohttp
-import asyncio
-import base64
-import json
-import random
-from datetime import datetime
-from typing import List, Dict, Optional, Union, Any
-
-class GitHubSource:
-    """GitHub API实现"""
-
-    # 默认API密钥列表 - 可以放置多个GitHub令牌
-    API_KEYS = [
-        "github_pat_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
-        "github_pat_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
-        # "your_github_token_1",
-        # "your_github_token_2",
-        # "your_github_token_3"
-    ]
-
-    def __init__(self, api_key: Optional[Union[str, List[str]]] = None):
-        """初始化GitHub API客户端
-
-        Args:
-            api_key: GitHub个人访问令牌或令牌列表
-        """
-        if api_key is None:
-            self.api_keys = self.API_KEYS
-        elif isinstance(api_key, str):
-            self.api_keys = [api_key]
-        else:
-            self.api_keys = api_key
-
-        self._initialize()
-
-    def _initialize(self) -> None:
-        """初始化客户端，设置默认参数"""
-        self.base_url = "https://api.github.com"
-        self.headers = {
-            "Accept": "application/vnd.github+json",
-            "X-GitHub-Api-Version": "2022-11-28",
-            "User-Agent": "GitHub-API-Python-Client"
-        }
-
-        # 如果有可用的API密钥，随机选择一个
-        if self.api_keys:
-            selected_key = random.choice(self.api_keys)
-            self.headers["Authorization"] = f"Bearer {selected_key}"
-            print(f"已随机选择API密钥进行认证")
-        else:
-            print("警告: 未提供API密钥，将受到GitHub API请求限制")
-
-    async def _request(self, method: str, endpoint: str, params: Dict = None, data: Dict = None) -> Any:
-        """发送API请求
-
-        Args:
-            method: HTTP方法 (GET, POST, PUT, DELETE等)
-            endpoint: API端点
-            params: URL参数
-            data: 请求体数据
-
-        Returns:
-            解析后的响应JSON
-        """
-        async with aiohttp.ClientSession(headers=self.headers) as session:
-            url = f"{self.base_url}{endpoint}"
-
-            # 为调试目的打印请求信息
-            print(f"请求: {method} {url}")
-            if params:
-                print(f"参数: {params}")
-
-            # 发送请求
-            request_kwargs = {}
-            if params:
-                request_kwargs["params"] = params
-            if data:
-                request_kwargs["json"] = data
-
-            async with session.request(method, url, **request_kwargs) as response:
-                response_text = await response.text()
-
-                # 检查HTTP状态码
-                if response.status >= 400:
-                    print(f"API请求失败: HTTP {response.status}")
-                    print(f"响应内容: {response_text}")
-                    return None
-
-                # 解析JSON响应
-                try:
-                    return json.loads(response_text)
-                except json.JSONDecodeError:
-                    print(f"JSON解析错误: {response_text}")
-                    return None
-
-    # ===== 用户相关方法 =====
-
-    async def get_user(self, username: Optional[str] = None) -> Dict:
-        """获取用户信息
-
-        Args:
-            username: 指定用户名，不指定则获取当前授权用户
-
-        Returns:
-            用户信息字典
-        """
-        endpoint = "/user" if username is None else f"/users/{username}"
-        return await self._request("GET", endpoint)
-
-    async def get_user_repos(self, username: Optional[str] = None, sort: str = "updated",
-                            direction: str = "desc", per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取用户的仓库列表
-
-        Args:
-            username: 指定用户名，不指定则获取当前授权用户
-            sort: 排序方式 (created, updated, pushed, full_name)
-            direction: 排序方向 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            仓库列表
-        """
-        endpoint = "/user/repos" if username is None else f"/users/{username}/repos"
-        params = {
-            "sort": sort,
-            "direction": direction,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def get_user_starred(self, username: Optional[str] = None,
-                              per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取用户星标的仓库
-
-        Args:
-            username: 指定用户名，不指定则获取当前授权用户
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            星标仓库列表
-        """
-        endpoint = "/user/starred" if username is None else f"/users/{username}/starred"
-        params = {
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    # ===== 仓库相关方法 =====
-
-    async def get_repo(self, owner: str, repo: str) -> Dict:
-        """获取仓库信息
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-
-        Returns:
-            仓库信息
-        """
-        endpoint = f"/repos/{owner}/{repo}"
-        return await self._request("GET", endpoint)
-
-    async def get_repo_branches(self, owner: str, repo: str, per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取仓库的分支列表
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            分支列表
-        """
-        endpoint = f"/repos/{owner}/{repo}/branches"
-        params = {
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def get_repo_commits(self, owner: str, repo: str, sha: Optional[str] = None,
-                              path: Optional[str] = None, per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取仓库的提交历史
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            sha: 特定提交SHA或分支名
-            path: 文件路径筛选
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            提交列表
-        """
-        endpoint = f"/repos/{owner}/{repo}/commits"
-        params = {
-            "per_page": per_page,
-            "page": page
-        }
-        if sha:
-            params["sha"] = sha
-        if path:
-            params["path"] = path
-
-        return await self._request("GET", endpoint, params=params)
-
-    async def get_commit_details(self, owner: str, repo: str, commit_sha: str) -> Dict:
-        """获取特定提交的详情
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            commit_sha: 提交SHA
-
-        Returns:
-            提交详情
-        """
-        endpoint = f"/repos/{owner}/{repo}/commits/{commit_sha}"
-        return await self._request("GET", endpoint)
-
-    # ===== 内容相关方法 =====
-
-    async def get_file_content(self, owner: str, repo: str, path: str, ref: Optional[str] = None) -> Dict:
-        """获取文件内容
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            path: 文件路径
-            ref: 分支名、标签名或提交SHA
-
-        Returns:
-            文件内容信息
-        """
-        endpoint = f"/repos/{owner}/{repo}/contents/{path}"
-        params = {}
-        if ref:
-            params["ref"] = ref
-
-        response = await self._request("GET", endpoint, params=params)
-        if response and isinstance(response, dict) and "content" in response:
-            try:
-                # 解码Base64编码的文件内容
-                content = base64.b64decode(response["content"].encode()).decode()
-                response["decoded_content"] = content
-            except Exception as e:
-                print(f"解码文件内容时出错: {str(e)}")
-
-        return response
-
-    async def get_directory_content(self, owner: str, repo: str, path: str, ref: Optional[str] = None) -> List[Dict]:
-        """获取目录内容
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            path: 目录路径
-            ref: 分支名、标签名或提交SHA
-
-        Returns:
-            目录内容列表
-        """
-        # 注意：此方法与get_file_content使用相同的端点，但对于目录会返回列表
-        endpoint = f"/repos/{owner}/{repo}/contents/{path}"
-        params = {}
-        if ref:
-            params["ref"] = ref
-
-        return await self._request("GET", endpoint, params=params)
-
-    # ===== Issues相关方法 =====
-
-    async def get_issues(self, owner: str, repo: str, state: str = "open",
-                        sort: str = "created", direction: str = "desc",
-                        per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取仓库的Issues列表
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            state: Issue状态 (open, closed, all)
-            sort: 排序方式 (created, updated, comments)
-            direction: 排序方向 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            Issues列表
-        """
-        endpoint = f"/repos/{owner}/{repo}/issues"
-        params = {
-            "state": state,
-            "sort": sort,
-            "direction": direction,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def get_issue(self, owner: str, repo: str, issue_number: int) -> Dict:
-        """获取特定Issue的详情
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            issue_number: Issue编号
-
-        Returns:
-            Issue详情
-        """
-        endpoint = f"/repos/{owner}/{repo}/issues/{issue_number}"
-        return await self._request("GET", endpoint)
-
-    async def get_issue_comments(self, owner: str, repo: str, issue_number: int) -> List[Dict]:
-        """获取Issue的评论
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            issue_number: Issue编号
-
-        Returns:
-            评论列表
-        """
-        endpoint = f"/repos/{owner}/{repo}/issues/{issue_number}/comments"
-        return await self._request("GET", endpoint)
-
-    # ===== Pull Requests相关方法 =====
-
-    async def get_pull_requests(self, owner: str, repo: str, state: str = "open",
-                               sort: str = "created", direction: str = "desc",
-                               per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取仓库的Pull Request列表
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            state: PR状态 (open, closed, all)
-            sort: 排序方式 (created, updated, popularity, long-running)
-            direction: 排序方向 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            Pull Request列表
-        """
-        endpoint = f"/repos/{owner}/{repo}/pulls"
-        params = {
-            "state": state,
-            "sort": sort,
-            "direction": direction,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def get_pull_request(self, owner: str, repo: str, pr_number: int) -> Dict:
-        """获取特定Pull Request的详情
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            pr_number: Pull Request编号
-
-        Returns:
-            Pull Request详情
-        """
-        endpoint = f"/repos/{owner}/{repo}/pulls/{pr_number}"
-        return await self._request("GET", endpoint)
-
-    async def get_pull_request_files(self, owner: str, repo: str, pr_number: int) -> List[Dict]:
-        """获取Pull Request中修改的文件
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-            pr_number: Pull Request编号
-
-        Returns:
-            修改文件列表
-        """
-        endpoint = f"/repos/{owner}/{repo}/pulls/{pr_number}/files"
-        return await self._request("GET", endpoint)
-
-    # ===== 搜索相关方法 =====
-
-    async def search_repositories(self, query: str, sort: str = "stars",
-                                 order: str = "desc", per_page: int = 30, page: int = 1) -> Dict:
-        """搜索仓库
-
-        Args:
-            query: 搜索关键词
-            sort: 排序方式 (stars, forks, updated)
-            order: 排序顺序 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            搜索结果
-        """
-        endpoint = "/search/repositories"
-        params = {
-            "q": query,
-            "sort": sort,
-            "order": order,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def search_code(self, query: str, sort: str = "indexed",
-                         order: str = "desc", per_page: int = 30, page: int = 1) -> Dict:
-        """搜索代码
-
-        Args:
-            query: 搜索关键词
-            sort: 排序方式 (indexed)
-            order: 排序顺序 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            搜索结果
-        """
-        endpoint = "/search/code"
-        params = {
-            "q": query,
-            "sort": sort,
-            "order": order,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def search_issues(self, query: str, sort: str = "created",
-                           order: str = "desc", per_page: int = 30, page: int = 1) -> Dict:
-        """搜索Issues和Pull Requests
-
-        Args:
-            query: 搜索关键词
-            sort: 排序方式 (created, updated, comments)
-            order: 排序顺序 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            搜索结果
-        """
-        endpoint = "/search/issues"
-        params = {
-            "q": query,
-            "sort": sort,
-            "order": order,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def search_users(self, query: str, sort: str = "followers",
-                          order: str = "desc", per_page: int = 30, page: int = 1) -> Dict:
-        """搜索用户
-
-        Args:
-            query: 搜索关键词
-            sort: 排序方式 (followers, repositories, joined)
-            order: 排序顺序 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            搜索结果
-        """
-        endpoint = "/search/users"
-        params = {
-            "q": query,
-            "sort": sort,
-            "order": order,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    # ===== 组织相关方法 =====
-
-    async def get_organization(self, org: str) -> Dict:
-        """获取组织信息
-
-        Args:
-            org: 组织名称
-
-        Returns:
-            组织信息
-        """
-        endpoint = f"/orgs/{org}"
-        return await self._request("GET", endpoint)
-
-    async def get_organization_repos(self, org: str, type: str = "all",
-                                    sort: str = "created", direction: str = "desc",
-                                    per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取组织的仓库列表
-
-        Args:
-            org: 组织名称
-            type: 仓库类型 (all, public, private, forks, sources, member, internal)
-            sort: 排序方式 (created, updated, pushed, full_name)
-            direction: 排序方向 (asc, desc)
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            仓库列表
-        """
-        endpoint = f"/orgs/{org}/repos"
-        params = {
-            "type": type,
-            "sort": sort,
-            "direction": direction,
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    async def get_organization_members(self, org: str, per_page: int = 30, page: int = 1) -> List[Dict]:
-        """获取组织成员列表
-
-        Args:
-            org: 组织名称
-            per_page: 每页结果数量
-            page: 页码
-
-        Returns:
-            成员列表
-        """
-        endpoint = f"/orgs/{org}/members"
-        params = {
-            "per_page": per_page,
-            "page": page
-        }
-        return await self._request("GET", endpoint, params=params)
-
-    # ===== 更复杂的操作 =====
-
-    async def get_repository_languages(self, owner: str, repo: str) -> Dict:
-        """获取仓库使用的编程语言及其比例
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-
-        Returns:
-            语言使用情况
-        """
-        endpoint = f"/repos/{owner}/{repo}/languages"
-        return await self._request("GET", endpoint)
-
-    async def get_repository_stats_contributors(self, owner: str, repo: str) -> List[Dict]:
-        """获取仓库的贡献者统计
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-
-        Returns:
-            贡献者统计信息
-        """
-        endpoint = f"/repos/{owner}/{repo}/stats/contributors"
-        return await self._request("GET", endpoint)
-
-    async def get_repository_stats_commit_activity(self, owner: str, repo: str) -> List[Dict]:
-        """获取仓库的提交活动
-
-        Args:
-            owner: 仓库所有者
-            repo: 仓库名
-
-        Returns:
-            提交活动统计
-        """
-        endpoint = f"/repos/{owner}/{repo}/stats/commit_activity"
-        return await self._request("GET", endpoint)
-
-async def example_usage():
-    """GitHubSource使用示例"""
-    # 创建客户端实例（可选传入API令牌）
-    # github = GitHubSource(api_key="your_github_token")
-    github = GitHubSource()
-
-    try:
-        # 示例1：搜索热门Python仓库
-        print("\n=== 示例1：搜索热门Python仓库 ===")
-        repos = await github.search_repositories(
-            query="language:python stars:>1000",
-            sort="stars",
-            order="desc",
-            per_page=5
-        )
-
-        if repos and "items" in repos:
-            for i, repo in enumerate(repos["items"], 1):
-                print(f"\n--- 仓库 {i} ---")
-                print(f"名称: {repo['full_name']}")
-                print(f"描述: {repo['description']}")
-                print(f"星标数: {repo['stargazers_count']}")
-                print(f"Fork数: {repo['forks_count']}")
-                print(f"最近更新: {repo['updated_at']}")
-                print(f"URL: {repo['html_url']}")
-
-        # 示例2：获取特定仓库的详情
-        print("\n=== 示例2：获取特定仓库的详情 ===")
-        repo_details = await github.get_repo("microsoft", "vscode")
-        if repo_details:
-            print(f"名称: {repo_details['full_name']}")
-            print(f"描述: {repo_details['description']}")
-            print(f"星标数: {repo_details['stargazers_count']}")
-            print(f"Fork数: {repo_details['forks_count']}")
-            print(f"默认分支: {repo_details['default_branch']}")
-            print(f"开源许可: {repo_details.get('license', {}).get('name', '无')}")
-            print(f"语言: {repo_details['language']}")
-            print(f"Open Issues数: {repo_details['open_issues_count']}")
-
-        # 示例3：获取仓库的提交历史
-        print("\n=== 示例3：获取仓库的最近提交 ===")
-        commits = await github.get_repo_commits("tensorflow", "tensorflow", per_page=5)
-        if commits:
-            for i, commit in enumerate(commits, 1):
-                print(f"\n--- 提交 {i} ---")
-                print(f"SHA: {commit['sha'][:7]}")
-                print(f"作者: {commit['commit']['author']['name']}")
-                print(f"日期: {commit['commit']['author']['date']}")
-                print(f"消息: {commit['commit']['message'].splitlines()[0]}")
-
-        # 示例4：搜索代码
-        print("\n=== 示例4：搜索代码 ===")
-        code_results = await github.search_code(
-            query="filename:README.md language:markdown pytorch in:file",
-            per_page=3
-        )
-        if code_results and "items" in code_results:
-            print(f"共找到: {code_results['total_count']} 个结果")
-            for i, item in enumerate(code_results["items"], 1):
-                print(f"\n--- 代码 {i} ---")
-                print(f"仓库: {item['repository']['full_name']}")
-                print(f"文件: {item['path']}")
-                print(f"URL: {item['html_url']}")
-
-        # 示例5：获取文件内容
-        print("\n=== 示例5：获取文件内容 ===")
-        file_content = await github.get_file_content("python", "cpython", "README.rst")
-        if file_content and "decoded_content" in file_content:
-            content = file_content["decoded_content"]
-            print(f"文件名: {file_content['name']}")
-            print(f"大小: {file_content['size']} 字节")
-            print(f"内容预览: {content[:200]}...")
-
-        # 示例6：获取仓库使用的编程语言
-        print("\n=== 示例6：获取仓库使用的编程语言 ===")
-        languages = await github.get_repository_languages("facebook", "react")
-        if languages:
-            print(f"React仓库使用的编程语言:")
-            for lang, bytes_of_code in languages.items():
-                print(f"- {lang}: {bytes_of_code} 字节")
-
-        # 示例7：获取组织信息
-        print("\n=== 示例7：获取组织信息 ===")
-        org_info = await github.get_organization("google")
-        if org_info:
-            print(f"名称: {org_info['name']}")
-            print(f"描述: {org_info.get('description', '无')}")
-            print(f"位置: {org_info.get('location', '未指定')}")
-            print(f"公共仓库数: {org_info['public_repos']}")
-            print(f"成员数: {org_info.get('public_members', 0)}")
-            print(f"URL: {org_info['html_url']}")
-
-        # 示例8：获取用户信息
-        print("\n=== 示例8：获取用户信息 ===")
-        user_info = await github.get_user("torvalds")
-        if user_info:
-            print(f"名称: {user_info['name']}")
-            print(f"公司: {user_info.get('company', '无')}")
-            print(f"博客: {user_info.get('blog', '无')}")
-            print(f"位置: {user_info.get('location', '未指定')}")
-            print(f"公共仓库数: {user_info['public_repos']}")
-            print(f"关注者数: {user_info['followers']}")
-            print(f"URL: {user_info['html_url']}")
-
-    except Exception as e:
-        print(f"发生错误: {str(e)}")
-        import traceback
-        print(traceback.format_exc())
-
-if __name__ == "__main__":
-    import asyncio
-
-    # 运行示例
-    asyncio.run(example_usage())
--- a/crazy_functions/paper_fns/document_structure_extractor.py
+++ b/crazy_functions/paper_fns/document_structure_extractor.py
@@ -1,593 +0,0 @@
-from typing import List, Dict, Optional, Tuple, Union, Any
-from dataclasses import dataclass, field
-import os
-import re
-import logging
-
-from crazy_functions.doc_fns.read_fns.unstructured_all.paper_structure_extractor import (
-    PaperStructureExtractor, PaperSection, StructuredPaper
-)
-from unstructured.partition.auto import partition
-from unstructured.documents.elements import (
-    Text, Title, NarrativeText, ListItem, Table,
-    Footer, Header, PageBreak, Image, Address
-)
-
-@dataclass
-class DocumentSection:
-    """通用文档章节数据类"""
-    title: str  # 章节标题，如果没有标题则为空字符串
-    content: str  # 章节内容
-    level: int = 0  # 标题级别，0为主标题，1为一级标题，以此类推
-    section_type: str = "content"  # 章节类型
-    is_heading_only: bool = False  # 是否仅包含标题
-    subsections: List['DocumentSection'] = field(default_factory=list)  # 子章节列表
-
-
-@dataclass
-class StructuredDocument:
-    """结构化文档数据类"""
-    title: str = ""  # 文档标题
-    metadata: Dict[str, Any] = field(default_factory=dict)  # 元数据
-    sections: List[DocumentSection] = field(default_factory=list)  # 章节列表
-    full_text: str = ""  # 完整文本
-    is_paper: bool = False  # 是否为学术论文
-
-
-class GenericDocumentStructureExtractor:
-    """通用文档结构提取器
-    
-    可以从各种文档格式中提取结构信息，包括标题和内容。
-    支持论文、报告、文章和一般文本文档。
-    """
-    
-    # 支持的文件扩展名
-    SUPPORTED_EXTENSIONS = [
-        '.pdf', '.docx', '.doc', '.pptx', '.ppt', 
-        '.txt', '.md', '.html', '.htm', '.xml',
-        '.rtf', '.odt', '.epub', '.msg', '.eml'
-    ]
-    
-    # 常见的标题前缀模式
-    HEADING_PATTERNS = [
-        # 数字标题 (1., 1.1., etc.)
-        r'^\s*(\d+\.)+\s+',
-        # 中文数字标题 (一、, 二、, etc.)
-        r'^\s*[一二三四五六七八九十]+[、：:]\s+',
-        # 带括号的数字标题 ((1), (2), etc.)
-        r'^\s*\(\s*\d+\s*\)\s+',
-        # 特定标记的标题 (Chapter 1, Section 1, etc.)
-        r'^\s*(chapter|section|part|附录|章|节)\s+\d+[\.:：]\s+',
-    ]
-    
-    # 常见的文档分段标记词
-    SECTION_MARKERS = {
-        'introduction': ['简介', '导言', '引言', 'introduction', '概述', 'overview'],
-        'background': ['背景', '现状', 'background', '理论基础', '相关工作'],
-        'main_content': ['主要内容', '正文', 'main content', '分析', '讨论'],
-        'conclusion': ['结论', '总结', 'conclusion', '结语', '小结', 'summary'],
-        'reference': ['参考', '参考文献', 'references', '文献', 'bibliography'],
-        'appendix': ['附录', 'appendix', '补充资料', 'supplementary']
-    }
-    
-    def __init__(self):
-        """初始化提取器"""
-        self.paper_extractor = PaperStructureExtractor()  # 论文专用提取器
-        self._setup_logging()
-        
-    def _setup_logging(self):
-        """配置日志"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-        )
-        self.logger = logging.getLogger(__name__)
-        
-    def extract_document_structure(self, file_path: str, strategy: str = "fast") -> StructuredDocument:
-        """提取文档结构
-        
-        Args:
-            file_path: 文件路径
-            strategy: 提取策略 ("fast" 或 "accurate")
-            
-        Returns:
-            StructuredDocument: 结构化文档对象
-        """
-        try:
-            self.logger.info(f"正在处理文档结构: {file_path}")
-            
-            # 1. 首先尝试使用论文提取器
-            try:
-                paper_result = self.paper_extractor.extract_paper_structure(file_path)
-                if paper_result and len(paper_result.sections) > 2:  # 如果成功识别为论文结构
-                    self.logger.info(f"成功识别为学术论文: {file_path}")
-                    # 将论文结构转换为通用文档结构
-                    return self._convert_paper_to_document(paper_result)
-            except Exception as e:
-                self.logger.debug(f"论文结构提取失败，将尝试通用提取: {str(e)}")
-            
-            # 2. 使用通用方法提取文档结构
-            elements = partition(
-                str(file_path),
-                strategy=strategy,
-                include_metadata=True,
-                nlp=False
-            )
-            
-            # 3. 使用通用提取器处理
-            doc = self._extract_generic_structure(elements)
-            return doc
-            
-        except Exception as e:
-            self.logger.error(f"文档结构提取失败: {str(e)}")
-            # 返回一个空的结构化文档
-            return StructuredDocument(
-                title="未能提取文档标题",
-                sections=[DocumentSection(
-                    title="", 
-                    content="", 
-                    level=0, 
-                    section_type="content"
-                )]
-            )
-    
-    def _convert_paper_to_document(self, paper: StructuredPaper) -> StructuredDocument:
-        """将论文结构转换为通用文档结构
-        
-        Args:
-            paper: 结构化论文对象
-            
-        Returns:
-            StructuredDocument: 转换后的通用文档结构
-        """
-        doc = StructuredDocument(
-            title=paper.metadata.title,
-            is_paper=True,
-            full_text=paper.full_text
-        )
-        
-        # 转换元数据
-        doc.metadata = {
-            'title': paper.metadata.title,
-            'authors': paper.metadata.authors,
-            'keywords': paper.keywords,
-            'abstract': paper.metadata.abstract if hasattr(paper.metadata, 'abstract') else "",
-            'is_paper': True
-        }
-        
-        # 转换章节结构
-        doc.sections = self._convert_paper_sections(paper.sections)
-        
-        return doc
-    
-    def _convert_paper_sections(self, paper_sections: List[PaperSection], level: int = 0) -> List[DocumentSection]:
-        """递归转换论文章节为通用文档章节
-        
-        Args:
-            paper_sections: 论文章节列表
-            level: 当前章节级别
-            
-        Returns:
-            List[DocumentSection]: 通用文档章节列表
-        """
-        doc_sections = []
-        
-        for section in paper_sections:
-            doc_section = DocumentSection(
-                title=section.title,
-                content=section.content,
-                level=section.level,
-                section_type=section.section_type,
-                is_heading_only=False if section.content else True
-            )
-            
-            # 递归处理子章节
-            if section.subsections:
-                doc_section.subsections = self._convert_paper_sections(
-                    section.subsections, level + 1
-                )
-                
-            doc_sections.append(doc_section)
-            
-        return doc_sections
-    
-    def _extract_generic_structure(self, elements) -> StructuredDocument:
-        """从元素列表中提取通用文档结构
-        
-        Args:
-            elements: 文档元素列表
-            
-        Returns:
-            StructuredDocument: 结构化文档对象
-        """
-        # 创建结构化文档对象
-        doc = StructuredDocument(full_text="")
-        
-        # 1. 提取文档标题
-        title_candidates = []
-        for i, element in enumerate(elements[:5]):  # 只检查前5个元素
-            if isinstance(element, Title):
-                title_text = str(element).strip()
-                title_candidates.append((i, title_text))
-                
-        if title_candidates:
-            # 使用第一个标题作为文档标题
-            doc.title = title_candidates[0][1]
-        
-        # 2. 识别所有标题元素和内容
-        title_elements = []
-        
-        # 2.1 首先识别所有标题
-        for i, element in enumerate(elements):
-            is_heading = False
-            title_text = ""
-            level = 0
-            
-            # 检查元素类型
-            if isinstance(element, Title):
-                is_heading = True
-                title_text = str(element).strip()
-                
-                # 进一步检查是否为真正的标题
-                if self._is_likely_heading(title_text, element, i, elements):
-                    level = self._estimate_heading_level(title_text, element)
-                else:
-                    is_heading = False
-            
-            # 也检查格式像标题的普通文本
-            elif isinstance(element, (Text, NarrativeText)) and i > 0:
-                text = str(element).strip()
-                # 检查是否匹配标题模式
-                if any(re.match(pattern, text) for pattern in self.HEADING_PATTERNS):
-                    # 检查长度和后续内容以确认是否为标题
-                    if len(text) < 100 and self._has_sufficient_following_content(i, elements):
-                        is_heading = True
-                        title_text = text
-                        level = self._estimate_heading_level(title_text, element)
-            
-            if is_heading:
-                section_type = self._identify_section_type(title_text)
-                title_elements.append((i, title_text, level, section_type))
-        
-        # 2.2 为每个标题提取内容
-        sections = []
-        
-        for i, (index, title_text, level, section_type) in enumerate(title_elements):
-            # 确定内容范围
-            content_start = index + 1
-            content_end = elements[-1]  # 默认到文档结束
-            
-            # 如果有下一个标题，内容到下一个标题开始
-            if i < len(title_elements) - 1:
-                content_end = title_elements[i+1][0]
-            else:
-                content_end = len(elements)
-            
-            # 提取内容
-            content = self._extract_content_between(elements, content_start, content_end)
-            
-            # 创建章节
-            section = DocumentSection(
-                title=title_text,
-                content=content,
-                level=level,
-                section_type=section_type,
-                is_heading_only=False if content.strip() else True
-            )
-            
-            sections.append(section)
-        
-        # 3. 如果没有识别到任何章节，创建一个默认章节
-        if not sections:
-            all_content = self._extract_content_between(elements, 0, len(elements))
-            
-            # 尝试从内容中提取标题
-            first_line = all_content.split('\n')[0] if all_content else ""
-            if first_line and len(first_line) < 100:
-                doc.title = first_line
-                all_content = '\n'.join(all_content.split('\n')[1:])
-            
-            default_section = DocumentSection(
-                title="",
-                content=all_content,
-                level=0,
-                section_type="content"
-            )
-            sections.append(default_section)
-        
-        # 4. 构建层次结构
-        doc.sections = self._build_section_hierarchy(sections)
-        
-        # 5. 提取完整文本
-        doc.full_text = "\n\n".join([str(element) for element in elements if isinstance(element, (Text, NarrativeText, Title, ListItem))])
-        
-        return doc
-    
-    def _build_section_hierarchy(self, sections: List[DocumentSection]) -> List[DocumentSection]:
-        """构建章节层次结构
-        
-        Args:
-            sections: 章节列表
-            
-        Returns:
-            List[DocumentSection]: 具有层次结构的章节列表
-        """
-        if not sections:
-            return []
-        
-        # 按层级排序
-        top_level_sections = []
-        current_parents = {0: None}  # 每个层级的当前父节点
-        
-        for section in sections:
-            # 找到当前节点的父节点
-            parent_level = None
-            for level in sorted([k for k in current_parents.keys() if k < section.level], reverse=True):
-                parent_level = level
-                break
-            
-            if parent_level is None:
-                # 顶级章节
-                top_level_sections.append(section)
-            else:
-                # 子章节
-                parent = current_parents[parent_level]
-                if parent:
-                    parent.subsections.append(section)
-                else:
-                    top_level_sections.append(section)
-            
-            # 更新当前层级的父节点
-            current_parents[section.level] = section
-            
-            # 清除所有更深层级的父节点缓存
-            deeper_levels = [k for k in current_parents.keys() if k > section.level]
-            for level in deeper_levels:
-                current_parents.pop(level, None)
-        
-        return top_level_sections
-    
-    def _is_likely_heading(self, text: str, element, index: int, elements) -> bool:
-        """判断文本是否可能是标题
-        
-        Args:
-            text: 文本内容
-            element: 元素对象
-            index: 元素索引
-            elements: 所有元素列表
-            
-        Returns:
-            bool: 是否可能是标题
-        """
-        # 1. 检查文本长度 - 标题通常不会太长
-        if len(text) > 150:  # 标题通常不超过150个字符
-            return False
-            
-        # 2. 检查是否匹配标题的数字编号模式
-        if any(re.match(pattern, text) for pattern in self.HEADING_PATTERNS):
-            return True
-            
-        # 3. 检查是否包含常见章节标记词
-        lower_text = text.lower()
-        for markers in self.SECTION_MARKERS.values():
-            if any(marker.lower() in lower_text for marker in markers):
-                return True
-        
-        # 4. 检查后续内容数量 - 标题后通常有足够多的内容
-        if not self._has_sufficient_following_content(index, elements, min_chars=100):
-            # 但如果文本很短且以特定格式开头，仍可能是标题
-            if len(text) < 50 and (text.endswith(':') or text.endswith('：')):
-                return True
-            return False
-                
-        # 5. 检查格式特征
-        # 标题通常是元素的开头，不在段落中间
-        if len(text.split('\n')) > 1:
-            # 多行文本不太可能是标题
-            return False
-            
-        # 如果有元数据，检查字体特征（字体大小等）
-        if hasattr(element, 'metadata') and element.metadata:
-            try:
-                font_size = getattr(element.metadata, 'font_size', None)
-                is_bold = getattr(element.metadata, 'is_bold', False)
-                
-                # 字体较大或加粗的文本更可能是标题
-                if font_size and font_size > 12:
-                    return True
-                if is_bold:
-                    return True
-            except (AttributeError, TypeError):
-                pass
-        
-        # 默认返回True，因为元素已被识别为Title类型
-        return True
-    
-    def _estimate_heading_level(self, text: str, element) -> int:
-        """估计标题的层级
-        
-        Args:
-            text: 标题文本
-            element: 元素对象
-            
-        Returns:
-            int: 标题层级 (0为主标题，1为一级标题, 等等)
-        """
-        # 1. 通过编号模式判断层级
-        for pattern, level in [
-            (r'^\s*\d+\.\s+', 1),  # 1. 开头 (一级标题)
-            (r'^\s*\d+\.\d+\.\s+', 2),  # 1.1. 开头 (二级标题)
-            (r'^\s*\d+\.\d+\.\d+\.\s+', 3),  # 1.1.1. 开头 (三级标题)
-            (r'^\s*\d+\.\d+\.\d+\.\d+\.\s+', 4),  # 1.1.1.1. 开头 (四级标题)
-        ]:
-            if re.match(pattern, text):
-                return level
-                
-        # 2. 检查是否是常见的主要章节标题
-        lower_text = text.lower()
-        main_sections = [
-            'abstract', 'introduction', 'background', 'methodology', 
-            'results', 'discussion', 'conclusion', 'references'
-        ]
-        for section in main_sections:
-            if section in lower_text:
-                return 1  # 主要章节为一级标题
-        
-        # 3. 根据文本特征判断
-        if text.isupper():  # 全大写文本可能是章标题
-            return 1
-        
-        # 4. 通过元数据判断层级
-        if hasattr(element, 'metadata') and element.metadata:
-            try:
-                # 根据字体大小判断层级
-                font_size = getattr(element.metadata, 'font_size', None)
-                if font_size is not None:
-                    if font_size > 18:  # 假设主标题字体最大
-                        return 0
-                    elif font_size > 16:
-                        return 1
-                    elif font_size > 14:
-                        return 2
-                    else:
-                        return 3
-            except (AttributeError, TypeError):
-                pass
-        
-        # 默认为二级标题
-        return 2
-    
-    def _identify_section_type(self, title_text: str) -> str:
-        """识别章节类型，包括参考文献部分"""
-        lower_text = title_text.lower()
-        
-        # 特别检查是否为参考文献部分
-        references_patterns = [
-            r'references', r'参考文献', r'bibliography', r'引用文献', 
-            r'literature cited', r'^cited\s+literature', r'^文献$', r'^引用$'
-        ]
-        
-        for pattern in references_patterns:
-            if re.search(pattern, lower_text, re.IGNORECASE):
-                return "references"
-        
-        # 检查是否匹配其他常见章节类型
-        for section_type, markers in self.SECTION_MARKERS.items():
-            if any(marker.lower() in lower_text for marker in markers):
-                return section_type
-        
-        # 检查带编号的章节
-        if re.match(r'^\d+\.', lower_text):
-            return "content"
-            
-        # 默认为内容章节
-        return "content"
-    
-    def _has_sufficient_following_content(self, index: int, elements, min_chars: int = 150) -> bool:
-        """检查元素后是否有足够的内容
-        
-        Args:
-            index: 当前元素索引
-            elements: 所有元素列表
-            min_chars: 最小字符数要求
-            
-        Returns:
-            bool: 是否有足够的内容
-        """
-        total_chars = 0
-        for i in range(index + 1, min(index + 5, len(elements))):
-            if isinstance(elements[i], Title):
-                # 如果紧接着是标题，就停止检查
-                break
-            if isinstance(elements[i], (Text, NarrativeText, ListItem, Table)):
-                total_chars += len(str(elements[i]))
-                if total_chars >= min_chars:
-                    return True
-        
-        return total_chars >= min_chars
-    
-    def _extract_content_between(self, elements, start_index: int, end_index: int) -> str:
-        """提取指定范围内的内容文本
-        
-        Args:
-            elements: 元素列表
-            start_index: 开始索引
-            end_index: 结束索引
-            
-        Returns:
-            str: 提取的内容文本
-        """
-        content_parts = []
-        
-        for i in range(start_index, end_index):
-            if isinstance(elements[i], (Text, NarrativeText, ListItem, Table)):
-                content_parts.append(str(elements[i]).strip())
-        
-        return "\n\n".join([part for part in content_parts if part])
-    
-    def generate_markdown(self, doc: StructuredDocument) -> str:
-        """将结构化文档转换为Markdown格式
-        
-        Args:
-            doc: 结构化文档对象
-            
-        Returns:
-            str: Markdown格式文本
-        """
-        md_parts = []
-        
-        # 添加标题
-        if doc.title:
-            md_parts.append(f"# {doc.title}\n")
-        
-        # 添加元数据
-        if doc.is_paper:
-            # 作者信息
-            if 'authors' in doc.metadata and doc.metadata['authors']:
-                authors_str = ", ".join(doc.metadata['authors'])
-                md_parts.append(f"**作者:** {authors_str}\n")
-            
-            # 关键词
-            if 'keywords' in doc.metadata and doc.metadata['keywords']:
-                keywords_str = ", ".join(doc.metadata['keywords'])
-                md_parts.append(f"**关键词:** {keywords_str}\n")
-            
-            # 摘要
-            if 'abstract' in doc.metadata and doc.metadata['abstract']:
-                md_parts.append(f"## 摘要\n\n{doc.metadata['abstract']}\n")
-        
-        # 添加章节内容
-        md_parts.append(self._format_sections_markdown(doc.sections))
-        
-        return "\n".join(md_parts)
-    
-    def _format_sections_markdown(self, sections: List[DocumentSection], base_level: int = 0) -> str:
-        """递归格式化章节为Markdown
-        
-        Args:
-            sections: 章节列表
-            base_level: 基础层级
-            
-        Returns:
-            str: Markdown格式文本
-        """
-        md_parts = []
-        
-        for section in sections:
-            # 计算标题级别 (确保不超过6级)
-            header_level = min(section.level + base_level + 1, 6)
-            
-            # 添加标题和内容
-            if section.title:
-                md_parts.append(f"{'#' * header_level} {section.title}\n")
-            
-            if section.content:
-                md_parts.append(f"{section.content}\n")
-            
-            # 递归处理子章节
-            if section.subsections:
-                md_parts.append(self._format_sections_markdown(
-                    section.subsections, base_level
-                ))
-        
-        return "\n".join(md_parts) 
--- a/crazy_functions/paper_fns/file2file_doc/init.py
+++ b/crazy_functions/paper_fns/file2file_doc/init.py
@@ -1,4 +0,0 @@
-from .txt_doc import TxtFormatter
-from .markdown_doc import MarkdownFormatter
-from .html_doc import HtmlFormatter
-from .word_doc import WordFormatter
--- a/crazy_functions/paper_fns/file2file_doc/html_doc.py
+++ b/crazy_functions/paper_fns/file2file_doc/html_doc.py
@@ -1,300 +0,0 @@
-class HtmlFormatter:
-    """HTML格式文档生成器 - 保留原始文档结构"""
-    
-    def __init__(self, processing_type="文本处理"):
-        self.processing_type = processing_type
-        self.css_styles = """
-        :root {
-            --primary-color: #2563eb;
-            --primary-light: #eff6ff;
-            --secondary-color: #1e293b;
-            --background-color: #f8fafc;
-            --text-color: #334155;
-            --border-color: #e2e8f0;
-            --card-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
-        }
-
-        body {
-            font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
-            line-height: 1.8;
-            margin: 0;
-            padding: 2rem;
-            color: var(--text-color);
-            background-color: var(--background-color);
-        }
-
-        .container {
-            max-width: 1200px;
-            margin: 0 auto;
-            background: white;
-            padding: 2rem;
-            border-radius: 16px;
-            box-shadow: var(--card-shadow);
-        }
-        ::selection {
-            background: var(--primary-light);
-            color: var(--primary-color);
-        }
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-        
-        .container {
-            animation: fadeIn 0.6s ease-out;
-        }
-        
-        .document-title {
-            color: var(--primary-color);
-            font-size: 2em;
-            text-align: center;
-            margin: 1rem 0 2rem;
-            padding-bottom: 1rem;
-            border-bottom: 2px solid var(--primary-color);
-        }
-
-        .document-body {
-            display: flex;
-            flex-direction: column;
-            gap: 1.5rem;
-            margin: 2rem 0;
-        }
-        
-        .document-header {
-            display: flex;
-            flex-direction: column;
-            align-items: center;
-            margin-bottom: 2rem;
-        }
-        
-        .processing-type {
-            color: var(--secondary-color);
-            font-size: 1.2em;
-            margin: 0.5rem 0;
-        }
-        
-        .processing-date {
-            color: var(--text-color);
-            font-size: 0.9em;
-            opacity: 0.8;
-        }
-        
-        .document-content {
-            background: white;
-            padding: 1.5rem;
-            border-radius: 8px;
-            border-left: 4px solid var(--primary-color);
-            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-        }
-
-        /* 保留文档结构的样式 */
-        h1, h2, h3, h4, h5, h6 {
-            color: var(--secondary-color);
-            margin-top: 1.5em;
-            margin-bottom: 0.5em;
-        }
-        
-        h1 { font-size: 1.8em; }
-        h2 { font-size: 1.5em; }
-        h3 { font-size: 1.3em; }
-        h4 { font-size: 1.1em; }
-        
-        p {
-            margin: 0.8em 0;
-        }
-        
-        ul, ol {
-            margin: 1em 0;
-            padding-left: 2em;
-        }
-        
-        li {
-            margin: 0.5em 0;
-        }
-        
-        blockquote {
-            margin: 1em 0;
-            padding: 0.5em 1em;
-            border-left: 4px solid var(--primary-light);
-            background: rgba(0,0,0,0.02);
-        }
-        
-        code {
-            font-family: monospace;
-            background: rgba(0,0,0,0.05);
-            padding: 0.2em 0.4em;
-            border-radius: 3px;
-        }
-        
-        pre {
-            background: rgba(0,0,0,0.05);
-            padding: 1em;
-            border-radius: 5px;
-            overflow-x: auto;
-        }
-        
-        pre code {
-            background: transparent;
-            padding: 0;
-        }
-
-        @media (prefers-color-scheme: dark) {
-            :root {
-                --background-color: #0f172a;
-                --text-color: #e2e8f0;
-                --border-color: #1e293b;
-            }
-            
-            .container, .document-content {
-                background: #1e293b;
-            }
-            
-            blockquote {
-                background: rgba(255,255,255,0.05);
-            }
-            
-            code, pre {
-                background: rgba(255,255,255,0.05);
-            }
-        }
-        """
-
-    def _escape_html(self, text):
-        """转义HTML特殊字符"""
-        import html
-        return html.escape(text)
-    
-    def _markdown_to_html(self, text):
-        """将Markdown格式转换为HTML格式，保留文档结构"""
-        try:
-            import markdown
-            # 使用Python-Markdown库将markdown转换为HTML，启用更多扩展以支持嵌套列表
-            return markdown.markdown(text, extensions=['tables', 'fenced_code', 'codehilite', 'nl2br', 'sane_lists', 'smarty', 'extra'])
-        except ImportError:
-            # 如果没有markdown库，使用更复杂的替换来处理嵌套列表
-            import re
-            
-            # 替换标题
-            text = re.sub(r'^# (.+)$', r'<h1>\1</h1>', text, flags=re.MULTILINE)
-            text = re.sub(r'^## (.+)$', r'<h2>\1</h2>', text, flags=re.MULTILINE)
-            text = re.sub(r'^### (.+)$', r'<h3>\1</h3>', text, flags=re.MULTILINE)
-            
-            # 预处理列表 - 在列表项之间添加空行以正确分隔
-            # 处理编号列表
-            text = re.sub(r'(\n\d+\.\s.+)(\n\d+\.\s)', r'\1\n\2', text)
-            # 处理项目符号列表
-            text = re.sub(r'(\n•\s.+)(\n•\s)', r'\1\n\2', text)
-            text = re.sub(r'(\n\*\s.+)(\n\*\s)', r'\1\n\2', text)
-            text = re.sub(r'(\n-\s.+)(\n-\s)', r'\1\n\2', text)
-            
-            # 处理嵌套列表 - 确保正确的缩进和结构
-            lines = text.split('\n')
-            in_list = False
-            list_type = None  # 'ol' 或 'ul'
-            list_html = []
-            normal_lines = []
-            
-            i = 0
-            while i < len(lines):
-                line = lines[i]
-                
-                # 匹配编号列表项
-                numbered_match = re.match(r'^(\d+)\.\s+(.+)$', line)
-                # 匹配项目符号列表项
-                bullet_match = re.match(r'^[•\*-]\s+(.+)$', line)
-                
-                if numbered_match:
-                    if not in_list or list_type != 'ol':
-                        # 开始新的编号列表
-                        if in_list:
-                            # 关闭前一个列表
-                            list_html.append(f'</{list_type}>')
-                        list_html.append('<ol>')
-                        in_list = True
-                        list_type = 'ol'
-                    
-                    num, content = numbered_match.groups()
-                    list_html.append(f'<li>{content}</li>')
-                    
-                elif bullet_match:
-                    if not in_list or list_type != 'ul':
-                        # 开始新的项目符号列表
-                        if in_list:
-                            # 关闭前一个列表
-                            list_html.append(f'</{list_type}>')
-                        list_html.append('<ul>')
-                        in_list = True
-                        list_type = 'ul'
-                    
-                    content = bullet_match.group(1)
-                    list_html.append(f'<li>{content}</li>')
-                    
-                else:
-                    if in_list:
-                        # 结束当前列表
-                        list_html.append(f'</{list_type}>')
-                        in_list = False
-                        # 将完成的列表添加到正常行中
-                        normal_lines.append(''.join(list_html))
-                        list_html = []
-                    
-                    normal_lines.append(line)
-                
-                i += 1
-            
-            # 如果最后还在列表中，确保关闭列表
-            if in_list:
-                list_html.append(f'</{list_type}>')
-                normal_lines.append(''.join(list_html))
-            
-            # 重建文本
-            text = '\n'.join(normal_lines)
-            
-            # 替换段落，但避免处理已经是HTML标签的部分
-            paragraphs = text.split('\n\n')
-            for i, p in enumerate(paragraphs):
-                # 如果不是以HTML标签开始且不为空
-                if not (p.strip().startswith('<') and p.strip().endswith('>')) and p.strip() != '':
-                    paragraphs[i] = f'<p>{p}</p>'
-            
-            return '\n'.join(paragraphs)
-
-    def create_document(self, content: str) -> str:
-        """生成完整的HTML文档，保留原始文档结构
-        
-        Args:
-            content: 处理后的文档内容
-            
-        Returns:
-            str: 完整的HTML文档字符串
-        """
-        from datetime import datetime
-        
-        # 将markdown内容转换为HTML
-        html_content = self._markdown_to_html(content)
-        
-        return f"""
-        <!DOCTYPE html>
-        <html lang="zh-CN">
-        <head>
-            <meta charset="utf-8">
-            <meta name="viewport" content="width=device-width, initial-scale=1">
-            <title>文档处理结果</title>
-            <style>{self.css_styles}</style>
-        </head>
-        <body>
-            <div class="container">
-                <h1 class="document-title">文档处理结果</h1>
-                
-                <div class="document-header">
-                    <div class="processing-type">处理方式: {self._escape_html(self.processing_type)}</div>
-                    <div class="processing-date">处理时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</div>
-                </div>
-                
-                <div class="document-content">
-                    {html_content}
-                </div>
-            </div>
-        </body>
-        </html>
-        """
--- a/crazy_functions/paper_fns/file2file_doc/markdown_doc.py
+++ b/crazy_functions/paper_fns/file2file_doc/markdown_doc.py
@@ -1,40 +0,0 @@
-class MarkdownFormatter:
-    """Markdown格式文档生成器 - 保留原始文档结构"""
-
-    def __init__(self):
-        self.content = []
-
-    def _add_content(self, text: str):
-        """添加正文内容"""
-        if text:
-            self.content.append(f"\n{text}\n")
-
-    def create_document(self, content: str, processing_type: str = "文本处理") -> str:
-        """
-        创建完整的Markdown文档，保留原始文档结构
-        Args:
-            content: 处理后的文档内容
-            processing_type: 处理类型（润色、翻译等）
-        Returns:
-            str: 生成的Markdown文本
-        """
-        self.content = []
-        
-        # 添加标题和说明
-        self.content.append(f"# 文档处理结果\n")
-        self.content.append(f"## 处理方式: {processing_type}\n")
-        
-        # 添加处理时间
-        from datetime import datetime
-        self.content.append(f"*处理时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*\n")
-        
-        # 添加分隔线
-        self.content.append("---\n")
-        
-        # 添加原始内容，保留结构
-        self.content.append(content)
-            
-        # 添加结尾分隔线
-        self.content.append("\n---\n")
-
-        return "\n".join(self.content)
--- a/crazy_functions/paper_fns/file2file_doc/txt_doc.py
+++ b/crazy_functions/paper_fns/file2file_doc/txt_doc.py
@@ -1,69 +0,0 @@
-import re
-
-def convert_markdown_to_txt(markdown_text):
-    """Convert markdown text to plain text while preserving formatting"""
-    # Standardize line endings
-    markdown_text = markdown_text.replace('\r\n', '\n').replace('\r', '\n')
-
-    # 1. Handle headers but keep their formatting instead of removing them
-    markdown_text = re.sub(r'^#\s+(.+)$', r'# \1', markdown_text, flags=re.MULTILINE)
-    markdown_text = re.sub(r'^##\s+(.+)$', r'## \1', markdown_text, flags=re.MULTILINE)
-    markdown_text = re.sub(r'^###\s+(.+)$', r'### \1', markdown_text, flags=re.MULTILINE)
-
-    # 2. Handle bold and italic - simply remove markers
-    markdown_text = re.sub(r'\*\*(.+?)\*\*', r'\1', markdown_text)
-    markdown_text = re.sub(r'\*(.+?)\*', r'\1', markdown_text)
-
-    # 3. Handle lists but preserve formatting
-    markdown_text = re.sub(r'^\s*[-*+]\s+(.+?)(?=\n|$)', r'• \1', markdown_text, flags=re.MULTILINE)
-
-    # 4. Handle links - keep only the text
-    markdown_text = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', r'\1 (\2)', markdown_text)
-    
-    # 5. Handle HTML links - convert to user-friendly format
-    markdown_text = re.sub(r'<a href=[\'"]([^\'"]+)[\'"](?:\s+target=[\'"][^\'"]+[\'"])?>([^<]+)</a>', r'\2 (\1)', markdown_text)
-
-    # 6. Preserve paragraph breaks
-    markdown_text = re.sub(r'\n{3,}', '\n\n', markdown_text)  # normalize multiple newlines to double newlines
-
-    # 7. Clean up extra spaces but maintain indentation
-    markdown_text = re.sub(r' +', ' ', markdown_text)
-
-    return markdown_text.strip()
-
-
-class TxtFormatter:
-    """文本格式化器 - 保留原始文档结构"""
-
-    def __init__(self):
-        self.content = []
-        self._setup_document()
-
-    def _setup_document(self):
-        """初始化文档标题"""
-        self.content.append("=" * 50)
-        self.content.append("处理后文档".center(48))
-        self.content.append("=" * 50)
-
-    def _format_header(self):
-        """创建文档头部信息"""
-        from datetime import datetime
-        date_str = datetime.now().strftime('%Y年%m月%d日')
-        return [
-            date_str.center(48),
-            "\n"  # 添加空行
-        ]
-
-    def create_document(self, content):
-        """生成保留原始结构的文档"""
-        # 添加头部信息
-        self.content.extend(self._format_header())
-        
-        # 处理内容，保留原始结构
-        processed_content = convert_markdown_to_txt(content)
-        
-        # 添加处理后的内容
-        self.content.append(processed_content)
-        
-        # 合并所有内容
-        return "\n".join(self.content)
--- a/crazy_functions/paper_fns/file2file_doc/word2pdf.py
+++ b/crazy_functions/paper_fns/file2file_doc/word2pdf.py
@@ -1,125 +0,0 @@
-from docx2pdf import convert
-import os
-import platform
-from typing import Union
-from pathlib import Path
-from datetime import datetime
-
-class WordToPdfConverter:
-    """Word文档转PDF转换器"""
-    
-    @staticmethod
-    def convert_to_pdf(word_path: Union[str, Path], pdf_path: Union[str, Path] = None) -> str:
-        """
-        将Word文档转换为PDF
-        
-        参数:
-            word_path: Word文档的路径
-            pdf_path: 可选，PDF文件的输出路径。如果未指定，将使用与Word文档相同的名称和位置
-            
-        返回:
-            生成的PDF文件路径
-        
-        异常:
-            如果转换失败，将抛出相应异常
-        """
-        try:
-            # 确保输入路径是Path对象
-            word_path = Path(word_path)
-            
-            # 如果未指定pdf_path，则使用与word文档相同的名称
-            if pdf_path is None:
-                pdf_path = word_path.with_suffix('.pdf')
-            else:
-                pdf_path = Path(pdf_path)
-            
-            # 检查操作系统
-            if platform.system() == 'Linux':
-                # Linux系统需要安装libreoffice
-                if not os.system('which libreoffice') == 0:
-                    raise RuntimeError("请先安装LibreOffice: sudo apt-get install libreoffice")
-                
-                # 使用libreoffice进行转换
-                os.system(f'libreoffice --headless --convert-to pdf "{word_path}" --outdir "{pdf_path.parent}"')
-                
-                # 如果输出路径与默认生成的不同，则重命名
-                default_pdf = word_path.with_suffix('.pdf')
-                if default_pdf != pdf_path:
-                    os.rename(default_pdf, pdf_path)
-            else:
-                # Windows和MacOS使用docx2pdf
-                convert(word_path, pdf_path)
-            
-            return str(pdf_path)
-            
-        except Exception as e:
-            raise Exception(f"转换PDF失败: {str(e)}")
-
-    @staticmethod
-    def batch_convert(word_dir: Union[str, Path], pdf_dir: Union[str, Path] = None) -> list:
-        """
-        批量转换目录下的所有Word文档
-        
-        参数:
-            word_dir: 包含Word文档的目录路径
-            pdf_dir: 可选，PDF文件的输出目录。如果未指定，将使用与Word文档相同的目录
-            
-        返回:
-            生成的PDF文件路径列表
-        """
-        word_dir = Path(word_dir)
-        if pdf_dir:
-            pdf_dir = Path(pdf_dir)
-            pdf_dir.mkdir(parents=True, exist_ok=True)
-            
-        converted_files = []
-        
-        for word_file in word_dir.glob("*.docx"):
-            try:
-                if pdf_dir:
-                    pdf_path = pdf_dir / word_file.with_suffix('.pdf').name
-                else:
-                    pdf_path = word_file.with_suffix('.pdf')
-                    
-                pdf_file = WordToPdfConverter.convert_to_pdf(word_file, pdf_path)
-                converted_files.append(pdf_file)
-                
-            except Exception as e:
-                print(f"转换 {word_file} 失败: {str(e)}")
-                
-        return converted_files
-
-    @staticmethod
-    def convert_doc_to_pdf(doc, output_dir: Union[str, Path] = None) -> str:
-        """
-        将docx对象直接转换为PDF
-        
-        参数:
-            doc: python-docx的Document对象
-            output_dir: 可选，输出目录。如果未指定，将使用当前目录
-            
-        返回:
-            生成的PDF文件路径
-        """
-        try:
-            # 设置临时文件路径和输出路径
-            output_dir = Path(output_dir) if output_dir else Path.cwd()
-            output_dir.mkdir(parents=True, exist_ok=True)
-            
-            # 生成临时word文件
-            temp_docx = output_dir / f"temp_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx"
-            doc.save(temp_docx)
-            
-            # 转换为PDF
-            pdf_path = temp_docx.with_suffix('.pdf')
-            WordToPdfConverter.convert_to_pdf(temp_docx, pdf_path)
-            
-            # 删除临时word文件
-            temp_docx.unlink()
-            
-            return str(pdf_path)
-            
-        except Exception as e:
-            if temp_docx.exists():
-                temp_docx.unlink()
-            raise Exception(f"转换PDF失败: {str(e)}") 
--- a/crazy_functions/paper_fns/file2file_doc/word_doc.py
+++ b/crazy_functions/paper_fns/file2file_doc/word_doc.py
@@ -1,236 +0,0 @@
-import re
-from docx import Document
-from docx.shared import Cm, Pt
-from docx.enum.text import WD_PARAGRAPH_ALIGNMENT, WD_LINE_SPACING
-from docx.enum.style import WD_STYLE_TYPE
-from docx.oxml.ns import qn
-from datetime import datetime
-
-def convert_markdown_to_word(markdown_text):
-    # 0. 首先标准化所有换行符为\n
-    markdown_text = markdown_text.replace('\r\n', '\n').replace('\r', '\n')
-    
-    # 1. 处理标题 - 支持更多级别的标题，使用更精确的正则
-    # 保留标题标记，以便后续处理时还能识别出标题级别
-    markdown_text = re.sub(r'^(#{1,6})\s+(.+?)(?:\s+#+)?$', r'\1 \2', markdown_text, flags=re.MULTILINE)
-    
-    # 2. 处理粗体、斜体和加粗斜体
-    markdown_text = re.sub(r'\*\*\*(.+?)\*\*\*', r'\1', markdown_text)  # 加粗斜体
-    markdown_text = re.sub(r'\*\*(.+?)\*\*', r'\1', markdown_text)      # 加粗
-    markdown_text = re.sub(r'\*(.+?)\*', r'\1', markdown_text)          # 斜体
-    markdown_text = re.sub(r'_(.+?)_', r'\1', markdown_text)            # 下划线斜体
-    markdown_text = re.sub(r'__(.+?)__', r'\1', markdown_text)          # 下划线加粗
-    
-    # 3. 处理代码块 - 不移除，而是简化格式
-    # 多行代码块
-    markdown_text = re.sub(r'```(?:\w+)?\n([\s\S]*?)```', r'[代码块]\n\1[/代码块]', markdown_text)
-    # 单行代码
-    markdown_text = re.sub(r'`([^`]+)`', r'[代码]\1[/代码]', markdown_text)
-    
-    # 4. 处理列表 - 保留列表结构
-    # 匹配无序列表
-    markdown_text = re.sub(r'^(\s*)[-*+]\s+(.+?)$', r'\1• \2', markdown_text, flags=re.MULTILINE)
-    
-    # 5. 处理Markdown链接
-    markdown_text = re.sub(r'\[([^\]]+)\]\(([^)]+?)\s*(?:"[^"]*")?\)', r'\1 (\2)', markdown_text)
-    
-    # 6. 处理HTML链接
-    markdown_text = re.sub(r'<a href=[\'"]([^\'"]+)[\'"](?:\s+target=[\'"][^\'"]+[\'"])?>([^<]+)</a>', r'\2 (\1)', markdown_text)
-    
-    # 7. 处理图片
-    markdown_text = re.sub(r'!\[([^\]]*)\]\([^)]+\)', r'[图片：\1]', markdown_text)
-    
-    return markdown_text
-
-
-class WordFormatter:
-    """文档Word格式化器 - 保留原始文档结构"""
-
-    def __init__(self):
-        self.doc = Document()
-        self._setup_document()
-        self._create_styles()
-
-    def _setup_document(self):
-        """设置文档基本格式，包括页面设置和页眉"""
-        sections = self.doc.sections
-        for section in sections:
-            # 设置页面大小为A4
-            section.page_width = Cm(21)
-            section.page_height = Cm(29.7)
-            # 设置页边距
-            section.top_margin = Cm(3.7)  # 上边距37mm
-            section.bottom_margin = Cm(3.5)  # 下边距35mm
-            section.left_margin = Cm(2.8)  # 左边距28mm
-            section.right_margin = Cm(2.6)  # 右边距26mm
-            # 设置页眉页脚距离
-            section.header_distance = Cm(2.0)
-            section.footer_distance = Cm(2.0)
-
-            # 添加页眉
-            header = section.header
-            header_para = header.paragraphs[0]
-            header_para.alignment = WD_PARAGRAPH_ALIGNMENT.RIGHT
-            header_run = header_para.add_run("文档处理结果")
-            header_run.font.name = '仿宋'
-            header_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-            header_run.font.size = Pt(9)
-
-    def _create_styles(self):
-        """创建文档样式"""
-        # 创建正文样式
-        style = self.doc.styles.add_style('Normal_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        style.font.name = '仿宋'
-        style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        style.font.size = Pt(12)  # 调整为12磅
-        style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        style.paragraph_format.space_after = Pt(0)
-
-        # 创建标题样式
-        title_style = self.doc.styles.add_style('Title_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        title_style.font.name = '黑体'
-        title_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        title_style.font.size = Pt(22)  # 调整为22磅
-        title_style.font.bold = True
-        title_style.paragraph_format.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        title_style.paragraph_format.space_before = Pt(0)
-        title_style.paragraph_format.space_after = Pt(24)
-        title_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-
-        # 创建标题1样式
-        h1_style = self.doc.styles.add_style('Heading1_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        h1_style.font.name = '黑体'
-        h1_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        h1_style.font.size = Pt(18)
-        h1_style.font.bold = True
-        h1_style.paragraph_format.space_before = Pt(12)
-        h1_style.paragraph_format.space_after = Pt(6)
-
-        # 创建标题2样式
-        h2_style = self.doc.styles.add_style('Heading2_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        h2_style.font.name = '黑体'
-        h2_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        h2_style.font.size = Pt(16)
-        h2_style.font.bold = True
-        h2_style.paragraph_format.space_before = Pt(10)
-        h2_style.paragraph_format.space_after = Pt(6)
-
-        # 创建标题3样式
-        h3_style = self.doc.styles.add_style('Heading3_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        h3_style.font.name = '黑体'
-        h3_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体')
-        h3_style.font.size = Pt(14)
-        h3_style.font.bold = True
-        h3_style.paragraph_format.space_before = Pt(8)
-        h3_style.paragraph_format.space_after = Pt(4)
-
-        # 创建代码块样式
-        code_style = self.doc.styles.add_style('Code_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        code_style.font.name = 'Courier New'
-        code_style.font.size = Pt(11)
-        code_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.SINGLE
-        code_style.paragraph_format.space_before = Pt(6)
-        code_style.paragraph_format.space_after = Pt(6)
-        code_style.paragraph_format.left_indent = Pt(36)
-        code_style.paragraph_format.right_indent = Pt(36)
-
-        # 创建列表样式
-        list_style = self.doc.styles.add_style('List_Custom', WD_STYLE_TYPE.PARAGRAPH)
-        list_style.font.name = '仿宋'
-        list_style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        list_style.font.size = Pt(12)
-        list_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE
-        list_style.paragraph_format.left_indent = Pt(21)
-        list_style.paragraph_format.first_line_indent = Pt(-21)
-
-    def create_document(self, content: str, processing_type: str = "文本处理"):
-        """创建文档，保留原始结构"""
-        # 添加标题
-        title_para = self.doc.add_paragraph(style='Title_Custom')
-        title_run = title_para.add_run('文档处理结果')
-
-        # 添加处理类型
-        processing_para = self.doc.add_paragraph()
-        processing_para.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        processing_run = processing_para.add_run(f"处理方式: {processing_type}")
-        processing_run.font.name = '仿宋'
-        processing_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        processing_run.font.size = Pt(14)
-
-        # 添加日期
-        date_para = self.doc.add_paragraph()
-        date_para.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        date_run = date_para.add_run(f"处理时间: {datetime.now().strftime('%Y年%m月%d日')}")
-        date_run.font.name = '仿宋'
-        date_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋')
-        date_run.font.size = Pt(14)
-
-        self.doc.add_paragraph()  # 添加空行
-
-        # 预处理内容，将Markdown格式转换为适合Word的格式
-        processed_content = convert_markdown_to_word(content)
-        
-        # 按行处理文本，保留结构
-        lines = processed_content.split('\n')
-        in_code_block = False
-        current_paragraph = None
-        
-        for line in lines:
-            # 检查是否为标题
-            header_match = re.match(r'^(#{1,6})\s+(.+)$', line)
-            
-            if header_match:
-                # 根据#的数量确定标题级别
-                level = len(header_match.group(1))
-                title_text = header_match.group(2)
-                
-                if level == 1:
-                    style = 'Heading1_Custom'
-                elif level == 2:
-                    style = 'Heading2_Custom'
-                else:
-                    style = 'Heading3_Custom'
-                
-                self.doc.add_paragraph(title_text, style=style)
-                current_paragraph = None
-            
-            # 检查代码块标记
-            elif '[代码块]' in line:
-                in_code_block = True
-                current_paragraph = self.doc.add_paragraph(style='Code_Custom')
-                code_line = line.replace('[代码块]', '').strip()
-                if code_line:
-                    current_paragraph.add_run(code_line)
-            
-            elif '[/代码块]' in line:
-                in_code_block = False
-                code_line = line.replace('[/代码块]', '').strip()
-                if code_line and current_paragraph:
-                    current_paragraph.add_run(code_line)
-                current_paragraph = None
-            
-            # 检查列表项
-            elif line.strip().startswith('•'):
-                p = self.doc.add_paragraph(style='List_Custom')
-                p.add_run(line.strip())
-                current_paragraph = None
-            
-            # 处理普通文本行
-            elif line.strip():
-                if in_code_block:
-                    if current_paragraph:
-                        current_paragraph.add_run('\n' + line)
-                    else:
-                        current_paragraph = self.doc.add_paragraph(line, style='Code_Custom')
-                else:
-                    if current_paragraph is None or not current_paragraph.text:
-                        current_paragraph = self.doc.add_paragraph(line, style='Normal_Custom')
-                    else:
-                        current_paragraph.add_run('\n' + line)
-            
-            # 处理空行，创建新段落
-            elif not in_code_block:
-                current_paragraph = None
-
-        return self.doc
-
--- a/crazy_functions/paper_fns/github_search.py
+++ b/crazy_functions/paper_fns/github_search.py
@@ -1,278 +0,0 @@
-from typing import List, Dict, Tuple
-import asyncio
-from dataclasses import dataclass
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
-from toolbox import update_ui, CatchException, report_exception, write_history_to_file
-from crazy_functions.paper_fns.auto_git.query_analyzer import QueryAnalyzer, SearchCriteria
-from crazy_functions.paper_fns.auto_git.handlers.repo_handler import RepositoryHandler
-from crazy_functions.paper_fns.auto_git.handlers.code_handler import CodeSearchHandler
-from crazy_functions.paper_fns.auto_git.handlers.user_handler import UserSearchHandler
-from crazy_functions.paper_fns.auto_git.handlers.topic_handler import TopicHandler
-from crazy_functions.paper_fns.auto_git.sources.github_source import GitHubSource
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import re
-from datetime import datetime
-import os
-import json
-from pathlib import Path
-import time
-
-# 导入格式化器
-from crazy_functions.paper_fns.file2file_doc import (
-    TxtFormatter,
-    MarkdownFormatter,
-    HtmlFormatter,
-    WordFormatter
-)
-from crazy_functions.paper_fns.file2file_doc.word2pdf import WordToPdfConverter
-
-@CatchException
-def GitHub项目智能检索(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-           history: List, system_prompt: str, user_request: str):
-    """GitHub项目智能检索主函数"""
-
-    # 初始化GitHub API调用源
-    github_source = GitHubSource(api_key=plugin_kwargs.get("github_api_key"))
-
-    # 初始化处理器
-    handlers = {
-        "repo": RepositoryHandler(github_source, llm_kwargs),
-        "code": CodeSearchHandler(github_source, llm_kwargs),
-        "user": UserSearchHandler(github_source, llm_kwargs),
-        "topic": TopicHandler(github_source, llm_kwargs),
-    }
-
-    # 分析查询意图
-    chatbot.append(["分析查询意图", "正在分析您的查询需求..."])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    query_analyzer = QueryAnalyzer()
-    search_criteria = yield from query_analyzer.analyze_query(
-        txt, chatbot, llm_kwargs
-    )
-
-    # 根据查询类型选择处理器
-    handler = handlers.get(search_criteria.query_type)
-    if not handler:
-        handler = handlers["repo"]  # 默认使用仓库处理器
-
-    # 处理查询
-    chatbot.append(["开始搜索", f"使用{handler.__class__.__name__}处理您的请求，正在搜索GitHub..."])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    final_prompt = asyncio.run(handler.handle(
-        criteria=search_criteria,
-        chatbot=chatbot,
-        history=history,
-        system_prompt=system_prompt,
-        llm_kwargs=llm_kwargs,
-        plugin_kwargs=plugin_kwargs
-    ))
-
-    if final_prompt:
-        # 检查是否是道歉提示
-        if "很抱歉，我们未能找到" in final_prompt:
-            chatbot.append([txt, final_prompt])
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-
-        # 在 final_prompt 末尾添加用户原始查询要求
-        final_prompt += f"""
-
-原始用户查询: "{txt}"
-
-重要提示:
- 你的回答必须直接满足用户的原始查询要求
- 在遵循之前指南的同时，优先回答用户明确提出的问题
- 确保回答格式和内容与用户期望一致
- 对于GitHub仓库需要提供链接地址, 回复中请采用以下格式的HTML链接:
-  * 对于GitHub仓库: <a href='Github_URL' target='_blank'>仓库名</a>
- 不要生成参考列表，引用信息将另行处理
-"""
-
-        # 使用最终的prompt生成回答
-        response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=final_prompt,
-            inputs_show_user=txt,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history=[],
-            sys_prompt=f"你是一个熟悉GitHub生态系统的专业助手，能帮助用户找到合适的项目、代码和开发者。除非用户指定，否则请使用中文回复。"
-        )
-
-        # 1. 获取项目列表
-        repos_list = handler.ranked_repos  # 直接使用原始仓库数据
-
-        # 在新的对话中添加格式化的仓库参考列表
-        if repos_list:
-            references = ""
-            for idx, repo in enumerate(repos_list, 1):
-                # 构建仓库引用
-                stars_str = f"⭐ {repo.get('stargazers_count', 'N/A')}" if repo.get('stargazers_count') else ""
-                forks_str = f"🍴 {repo.get('forks_count', 'N/A')}" if repo.get('forks_count') else ""
-                stats = f"{stars_str} {forks_str}".strip()
-                stats = f" ({stats})" if stats else ""
-
-                language = f" [{repo.get('language', '')}]" if repo.get('language') else ""
-
-                reference = f"[{idx}] **{repo.get('name', '')}**{language}{stats}  \n"
-                reference += f"👤 {repo.get('owner', {}).get('login', 'N/A') if repo.get('owner') is not None else 'N/A'} | "
-                reference += f"📅 {repo.get('updated_at', 'N/A')[:10]} | "
-                reference += f"<a href='{repo.get('html_url', '')}' target='_blank'>GitHub</a>  \n"
-
-                if repo.get('description'):
-                    reference += f"{repo.get('description')}  \n"
-                reference += "  \n"
-
-                references += reference
-
-            # 添加新的对话显示参考仓库
-            chatbot.append(["推荐项目如下：", references])
-            yield from update_ui(chatbot=chatbot, history=history)
-
-        # 2. 保存结果到文件
-        # 创建保存目录
-        save_dir = get_log_folder(get_user(chatbot), plugin_name='github_search')
-        if not os.path.exists(save_dir):
-            os.makedirs(save_dir)
-
-        # 生成文件名
-        def get_safe_filename(txt, max_length=10):
-            # 获取文本前max_length个字符作为文件名
-            filename = txt[:max_length].strip()
-            # 移除不安全的文件名字符
-            filename = re.sub(r'[\\/:*?"<>|]', '', filename)
-            # 如果文件名为空，使用时间戳
-            if not filename:
-                filename = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-            return filename
-
-        base_filename = get_safe_filename(txt)
-
-        # 准备保存的内容 - 优化文档结构
-        md_content = f"# GitHub搜索结果: {txt}\n\n"
-        md_content += f"搜索时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
-
-        # 添加模型回复
-        md_content += "## 搜索分析与总结\n\n"
-        md_content += response + "\n\n"
-
-        # 添加所有搜索到的仓库详细信息
-        md_content += "## 推荐项目详情\n\n"
-
-        if not repos_list:
-            md_content += "未找到匹配的项目\n\n"
-        else:
-            md_content += f"共找到 {len(repos_list)} 个相关项目\n\n"
-
-            # 添加项目简表
-            md_content += "### 项目一览表\n\n"
-            md_content += "| 序号 | 项目名称 | 作者 | 语言 | 星标数 | 更新时间 |\n"
-            md_content += "| ---- | -------- | ---- | ---- | ------ | -------- |\n"
-
-            for idx, repo in enumerate(repos_list, 1):
-                md_content += f"| {idx} | [{repo.get('name', '')}]({repo.get('html_url', '')}) | {repo.get('owner', {}).get('login', 'N/A') if repo.get('owner') is not None else 'N/A'} | {repo.get('language', 'N/A')} | {repo.get('stargazers_count', 'N/A')} | {repo.get('updated_at', 'N/A')[:10]} |\n"
-
-            md_content += "\n"
-
-            # 添加详细项目信息
-            md_content += "### 项目详细信息\n\n"
-            for idx, repo in enumerate(repos_list, 1):
-                md_content += f"#### {idx}. {repo.get('name', '')}\n\n"
-                md_content += f"- **仓库**: [{repo.get('full_name', '')}]({repo.get('html_url', '')})\n"
-                md_content += f"- **作者**: [{repo.get('owner', {}).get('login', '') if repo.get('owner') is not None else 'N/A'}]({repo.get('owner', {}).get('html_url', '') if repo.get('owner') is not None else '#'})\n"
-                md_content += f"- **描述**: {repo.get('description', 'N/A')}\n"
-                md_content += f"- **语言**: {repo.get('language', 'N/A')}\n"
-                md_content += f"- **星标**: {repo.get('stargazers_count', 'N/A')}\n"
-                md_content += f"- **Fork数**: {repo.get('forks_count', 'N/A')}\n"
-                md_content += f"- **最近更新**: {repo.get('updated_at', 'N/A')[:10]}\n"
-                md_content += f"- **创建时间**: {repo.get('created_at', 'N/A')[:10]}\n"
-                md_content += f"- **开源许可**: {repo.get('license', {}).get('name', 'N/A') if repo.get('license') is not None else 'N/A'}\n"
-                if repo.get('topics'):
-                    md_content += f"- **主题标签**: {', '.join(repo.get('topics', []))}\n"
-                if repo.get('homepage'):
-                    md_content += f"- **项目主页**: [{repo.get('homepage')}]({repo.get('homepage')})\n"
-                md_content += "\n"
-
-        # 添加查询信息和元数据
-        md_content += "## 查询元数据\n\n"
-        md_content += f"- **原始查询**: {txt}\n"
-        md_content += f"- **查询类型**: {search_criteria.query_type}\n"
-        md_content += f"- **关键词**: {', '.join(search_criteria.keywords) if hasattr(search_criteria, 'keywords') and search_criteria.keywords else 'N/A'}\n"
-        md_content += f"- **搜索日期**: {datetime.now().strftime('%Y-%m-%d')}\n\n"
-
-        # 保存为多种格式
-        saved_files = []
-        failed_files = []
-
-        # 1. 保存为TXT
-        try:
-            txt_formatter = TxtFormatter()
-            txt_content = txt_formatter.create_document(md_content)
-            txt_file = os.path.join(save_dir, f"github_results_{base_filename}.txt")
-            with open(txt_file, 'w', encoding='utf-8') as f:
-                f.write(txt_content)
-            promote_file_to_downloadzone(txt_file, chatbot=chatbot)
-            saved_files.append("TXT")
-        except Exception as e:
-            failed_files.append(f"TXT (错误: {str(e)})")
-
-        # 2. 保存为Markdown
-        try:
-            md_formatter = MarkdownFormatter()
-            formatted_md_content = md_formatter.create_document(md_content, "GitHub项目搜索")
-            md_file = os.path.join(save_dir, f"github_results_{base_filename}.md")
-            with open(md_file, 'w', encoding='utf-8') as f:
-                f.write(formatted_md_content)
-            promote_file_to_downloadzone(md_file, chatbot=chatbot)
-            saved_files.append("Markdown")
-        except Exception as e:
-            failed_files.append(f"Markdown (错误: {str(e)})")
-
-        # 3. 保存为HTML
-        try:
-            html_formatter = HtmlFormatter(processing_type="GitHub项目搜索")
-            html_content = html_formatter.create_document(md_content)
-            html_file = os.path.join(save_dir, f"github_results_{base_filename}.html")
-            with open(html_file, 'w', encoding='utf-8') as f:
-                f.write(html_content)
-            promote_file_to_downloadzone(html_file, chatbot=chatbot)
-            saved_files.append("HTML")
-        except Exception as e:
-            failed_files.append(f"HTML (错误: {str(e)})")
-
-        # 4. 保存为Word
-        word_file = None
-        try:
-            word_formatter = WordFormatter()
-            doc = word_formatter.create_document(md_content, "GitHub项目搜索")
-            word_file = os.path.join(save_dir, f"github_results_{base_filename}.docx")
-            doc.save(word_file)
-            promote_file_to_downloadzone(word_file, chatbot=chatbot)
-            saved_files.append("Word")
-        except Exception as e:
-            failed_files.append(f"Word (错误: {str(e)})")
-            word_file = None
-
-        # 5. 保存为PDF (仅当Word保存成功时)
-        if word_file and os.path.exists(word_file):
-            try:
-                pdf_file = WordToPdfConverter.convert_to_pdf(word_file)
-                promote_file_to_downloadzone(pdf_file, chatbot=chatbot)
-                saved_files.append("PDF")
-            except Exception as e:
-                failed_files.append(f"PDF (错误: {str(e)})")
-
-        # 报告保存结果
-        if saved_files:
-            success_message = f"成功保存以下格式: {', '.join(saved_files)}"
-            if failed_files:
-                failure_message = f"以下格式保存失败: {', '.join(failed_files)}"
-                chatbot.append(["部分格式保存成功", f"{success_message}。{failure_message}"])
-            else:
-                chatbot.append(["所有格式保存成功", success_message])
-        else:
-            chatbot.append(["保存失败", f"所有格式均保存失败: {', '.join(failed_files)}"])
-    else:
-        report_exception(chatbot, history, a=f"处理失败", b=f"请尝试其他查询")
-        yield from update_ui(chatbot=chatbot, history=history)
--- a/crazy_functions/paper_fns/journal_paper_recom.py
+++ b/crazy_functions/paper_fns/journal_paper_recom.py
@@ -1,635 +0,0 @@
-import os
-import time
-import glob
-from typing import Dict, List, Generator, Tuple
-from dataclasses import dataclass
-
-from crazy_functions.pdf_fns.text_content_loader import TextContentLoader
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import update_ui, promote_file_to_downloadzone, write_history_to_file, CatchException, report_exception
-from shared_utils.fastapi_server import validate_path_safety
-# 导入论文下载相关函数
-from crazy_functions.论文下载 import extract_paper_id, extract_paper_ids, get_arxiv_paper, format_arxiv_id, SciHub
-from pathlib import Path
-from datetime import datetime, timedelta
-import calendar
-
-
-@dataclass
-class RecommendationQuestion:
-    """期刊会议推荐分析问题类"""
-    id: str  # 问题ID
-    question: str  # 问题内容
-    importance: int  # 重要性 (1-5，5最高)
-    description: str  # 问题描述
-
-
-class JournalConferenceRecommender:
-    """论文期刊会议推荐器"""
-    
-    def __init__(self, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List, history: List, system_prompt: str):
-        """初始化推荐器"""
-        self.llm_kwargs = llm_kwargs
-        self.plugin_kwargs = plugin_kwargs
-        self.chatbot = chatbot
-        self.history = history
-        self.system_prompt = system_prompt
-        self.paper_content = ""
-        self.analysis_results = {}
-        
-        # 定义论文分析问题库（针对期刊会议推荐）
-        self.questions = [
-            RecommendationQuestion(
-                id="research_field_and_topic",
-                question="请分析这篇论文的研究领域、主题和关键词。具体包括：1)论文属于哪个主要学科领域（如自然科学、工程技术、医学、社会科学、人文学科等）；2)具体的研究子领域或方向；3)论文的核心主题和关键概念；4)重要的学术关键词和专业术语；5)研究的跨学科特征（如果有）；6)研究的地域性特征（国际性研究还是特定地区研究）。",
-                importance=5,
-                description="研究领域与主题分析"
-            ),
-            RecommendationQuestion(
-                id="methodology_and_approach",
-                question="请分析论文的研究方法和技术路线。包括：1)采用的主要研究方法（定量研究、定性研究、理论分析、实验研究、田野调查、文献综述、案例研究等）；2)使用的技术手段、工具或分析方法；3)研究设计的严谨性和创新性；4)数据收集和分析方法的适当性；5)研究方法在该学科中的先进性或传统性；6)方法学上的贡献或局限性。",
-                importance=4,
-                description="研究方法与技术路线"
-            ),
-            RecommendationQuestion(
-                id="novelty_and_contribution",
-                question="请评估论文的创新性和学术贡献。包括：1)研究的新颖性程度（理论创新、方法创新、应用创新等）；2)对现有知识体系的贡献或突破；3)解决问题的重要性和学术价值；4)研究成果的理论意义和实践价值；5)在该学科领域的地位和影响潜力；6)与国际前沿研究的关系；7)对后续研究的启发意义。",
-                importance=4,
-                description="创新性与学术贡献"
-            ),
-            RecommendationQuestion(
-                id="target_audience_and_scope",
-                question="请分析论文的目标受众和应用范围。包括：1)主要面向的学术群体（研究者、从业者、政策制定者等）；2)研究成果的潜在应用领域和受益群体；3)对学术界和实践界的价值；4)研究的国际化程度和跨文化适用性；5)是否适合国际期刊还是区域性期刊；6)语言发表偏好（英文、中文或其他语言）；7)开放获取的必要性和可行性。",
-                importance=3,
-                description="目标受众与应用范围"
-            ),
-        ]
-        
-        # 按重要性排序
-        self.questions.sort(key=lambda q: q.importance, reverse=True)
-        
-    def _load_paper(self, paper_path: str) -> Generator:
-        """加载论文内容"""
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-        
-        # 使用TextContentLoader读取文件
-        loader = TextContentLoader(self.chatbot, self.history)
-
-        yield from loader.execute_single_file(paper_path)
-        
-        # 获取加载的内容
-        if len(self.history) >= 2 and self.history[-2]:
-            self.paper_content = self.history[-2]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return True
-        else:
-            self.chatbot.append(["错误", "无法读取论文内容，请检查文件是否有效"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return False
-            
-    def _analyze_question(self, question: RecommendationQuestion) -> Generator:
-        """分析单个问题"""
-        try:
-            # 创建分析提示
-            prompt = f"请基于以下论文内容回答问题：\n\n{self.paper_content}\n\n问题：{question.question}"
-            
-            # 使用单线程版本的请求函数
-            response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=prompt,
-                inputs_show_user=question.question,  # 显示问题本身
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history=[],  # 空历史，确保每个问题独立分析
-                sys_prompt="你是一个专业的学术期刊会议推荐专家，需要仔细分析论文内容并提供准确的分析。请保持客观、专业，并基于论文内容提供深入分析。"
-            )
-            
-            if response:
-                self.analysis_results[question.id] = response
-                return True
-            return False
-            
-        except Exception as e:
-            self.chatbot.append(["错误", f"分析问题时出错: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return False
-
-    def _generate_journal_recommendations(self) -> Generator:
-        """生成期刊推荐"""
-        self.chatbot.append(["生成期刊推荐", "正在基于论文分析结果生成期刊推荐..."])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-        
-        # 构建期刊推荐提示
-        journal_prompt = """请基于以下论文分析结果，为这篇论文推荐合适的学术期刊。
-
-推荐要求：
-1. 根据论文的创新性和工作质量，分别推荐不同级别的期刊：
-   - 顶级期刊（影响因子>8或该领域顶级期刊）：2-3个
-   - 高质量期刊（影响因子4-8或该领域知名期刊）：3-4个  
-   - 中等期刊（影响因子1.5-4或该领域认可期刊）：3-4个
-   - 入门期刊（影响因子<1.5但声誉良好的期刊）：2-3个
-
-注意：不同学科的影响因子标准差异很大，请根据论文所属学科的实际情况调整标准。
-特别是医学领域，需要考虑：
- 临床医学期刊通常影响因子较高（顶级期刊IF>20，高质量期刊IF>10）
- 基础医学期刊影响因子相对较低但学术价值很高
- 专科医学期刊在各自领域内具有权威性
- 医学期刊的临床实用性和循证医学价值
-
-2. 对每个期刊提供详细信息：
-   - 期刊全名和缩写
-   - 最新影响因子（如果知道）
-   - 期刊级别分类（Q1/Q2/Q3/Q4或该学科的分类标准）
-   - 主要研究领域和范围
-   - 与论文内容的匹配度评分（1-10分）
-   - 发表难度评估（容易/中等/困难/极难）
-   - 平均审稿周期
-   - 开放获取政策
-   - 期刊的学科分类（如SCI、SSCI、A&HCI等）
-   - 医学期刊特殊信息（如适用）：
-     * PubMed收录情况
-     * 是否为核心临床期刊
-     * 专科领域权威性
-     * 循证医学等级要求
-     * 临床试验注册要求
-     * 伦理委员会批准要求
-
-3. 按推荐优先级排序，并说明推荐理由
-4. 提供针对性的投稿建议，考虑该学科的特点
-
-论文分析结果："""
-        
-        for q in self.questions:
-            if q.id in self.analysis_results:
-                journal_prompt += f"\n\n{q.description}:\n{self.analysis_results[q.id]}"
-        
-        journal_prompt += "\n\n请提供详细的期刊推荐报告，重点关注期刊的层次性和适配性。请根据论文的具体学科领域，采用该领域通用的期刊评价标准和分类体系。"
-        
-        try:
-            response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=journal_prompt,
-                inputs_show_user="生成期刊推荐报告",
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history=[],
-                sys_prompt="你是一个资深的跨学科学术期刊推荐专家，熟悉各个学科领域不同层次的期刊。请根据论文的具体学科和创新性，推荐从顶级到入门级的各层次期刊。不同学科有不同的期刊评价标准：理工科重视影响因子和SCI收录，社会科学重视SSCI和学科声誉，人文学科重视A&HCI和同行评议，医学领域重视PubMed收录、临床实用性、循证医学价值和伦理规范。请根据论文所属学科采用相应的评价标准。"
-            )
-            
-            if response:
-                return response
-            return "期刊推荐生成失败"
-            
-        except Exception as e:
-            self.chatbot.append(["错误", f"生成期刊推荐时出错: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return "期刊推荐生成失败: " + str(e)
-
-    def _generate_conference_recommendations(self) -> Generator:
-        """生成会议推荐"""
-        self.chatbot.append(["生成会议推荐", "正在基于论文分析结果生成会议推荐..."])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-        
-        # 获取当前时间信息
-        current_time = datetime.now()
-        current_date_str = current_time.strftime("%Y年%m月%d日")
-        current_year = current_time.year
-        current_month = current_time.month
-        
-        # 构建会议推荐提示
-        conference_prompt = f"""请基于以下论文分析结果，为这篇论文推荐合适的学术会议。
-
-**重要提示：当前时间是{current_date_str}（{current_year}年{current_month}月），请基于这个时间点推断会议的举办时间和投稿截止时间。**
-
-推荐要求：
-1. 根据论文的创新性和工作质量，分别推荐不同级别的会议：
-   - 顶级会议（该领域最权威的国际会议）：2-3个
-   - 高质量会议（该领域知名的国际或区域会议）：3-4个
-   - 中等会议（该领域认可的专业会议）：3-4个
-   - 专业会议（该领域细分方向的专门会议）：2-3个
-
-注意：不同学科的会议评价标准不同：
- 计算机科学：可参考CCF分类（A/B/C类）
- 工程学：可参考EI收录和影响力
- 医学：可参考会议的临床影响和同行认可度
- 社会科学：可参考会议的学术声誉和参与度
- 人文学科：可参考会议的历史和学术传统
- 自然科学：可参考会议的国际影响力和发表质量
-
-特别是医学会议，需要考虑：
- 临床医学会议重视实用性和临床指导价值
- 基础医学会议重视科学创新和机制研究
- 专科医学会议在各自领域内具有权威性
- 国际医学会议的CME学分认证情况
-
-2. 对每个会议提供详细信息：
-   - 会议全名和缩写
-   - 会议级别分类（根据该学科的评价标准）
-   - 主要研究领域和主题
-   - 与论文内容的匹配度评分（1-10分）
-   - 录用难度评估（容易/中等/困难/极难）
-   - 会议举办周期（年会/双年会/不定期等）
-   - **基于当前时间{current_date_str}，推断{current_year}年和{current_year+1}年的举办时间和地点**（请根据往年的举办时间规律进行推断）
-   - **基于推断的会议时间，估算论文提交截止时间**（通常在会议前3-6个月）
-   - 会议的国际化程度和影响范围
-   - 医学会议特殊信息（如适用）：
-     * 是否提供CME学分
-     * 临床实践指导价值
-     * 专科认证机构认可情况
-     * 会议论文集的PubMed收录情况
-     * 伦理和临床试验相关要求
-
-3. 按推荐优先级排序，并说明推荐理由
-4. **基于当前时间{current_date_str}，提供会议投稿的时间规划建议**
-   - 哪些会议可以赶上{current_year}年的投稿截止时间
-   - 哪些会议需要准备{current_year+1}年的投稿
-   - 具体的时间安排建议
-
-论文分析结果："""
-        
-        for q in self.questions:
-            if q.id in self.analysis_results:
-                conference_prompt += f"\n\n{q.description}:\n{self.analysis_results[q.id]}"
-        
-        conference_prompt += f"\n\n请提供详细的会议推荐报告，重点关注会议的层次性和时效性。请根据论文的具体学科领域，采用该领域通用的会议评价标准。\n\n**特别注意：请根据当前时间{current_date_str}和各会议的历史举办时间规律，准确推断{current_year}年和{current_year+1}年的会议时间安排，不要使用虚构的时间。**"
-        
-        try:
-            response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=conference_prompt,
-                inputs_show_user="生成会议推荐报告",
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history=[],
-                sys_prompt="你是一个资深的跨学科学术会议推荐专家，熟悉各个学科领域不同层次的学术会议。请根据论文的具体学科和创新性，推荐从顶级到专业级的各层次会议。不同学科有不同的会议评价标准和文化：理工科重视技术创新和国际影响力，社会科学重视理论贡献和社会意义，人文学科重视学术深度和文化价值，医学领域重视临床实用性、CME学分认证、专科权威性和伦理规范。请根据论文所属学科采用相应的评价标准和推荐策略。"
-            )
-            
-            if response:
-                return response
-            return "会议推荐生成失败"
-            
-        except Exception as e:
-            self.chatbot.append(["错误", f"生成会议推荐时出错: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return "会议推荐生成失败: " + str(e)
-
-    def _generate_priority_summary(self, journal_recommendations: str, conference_recommendations: str) -> Generator:
-        """生成优先级总结"""
-        self.chatbot.append(["生成优先级总结", "正在生成投稿优先级总结..."])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-        
-        # 获取当前时间信息
-        current_time = datetime.now()
-        current_date_str = current_time.strftime("%Y年%m月%d日")
-        current_month = current_time.strftime("%Y年%m月")
-        
-        # 计算未来时间点
-        def add_months(date, months):
-            """安全地添加月份"""
-            month = date.month - 1 + months
-            year = date.year + month // 12
-            month = month % 12 + 1
-            day = min(date.day, calendar.monthrange(year, month)[1])
-            return date.replace(year=year, month=month, day=day)
-        
-        future_6_months = add_months(current_time, 6).strftime('%Y年%m月')
-        future_12_months = add_months(current_time, 12).strftime('%Y年%m月')
-        future_year = (current_time.year + 1)
-        
-        priority_prompt = f"""请基于以下期刊和会议推荐结果，生成一个综合的投稿优先级总结。
-
-**重要提示：当前时间是{current_date_str}（{current_month}），请基于这个时间点制定投稿计划。**
-
-期刊推荐结果：
-{journal_recommendations}
-
-会议推荐结果：
-{conference_recommendations}
-
-请提供：
-1. 综合投稿策略建议（考虑该学科的发表文化和惯例）
-   - 期刊优先还是会议优先（不同学科有不同偏好）
-   - 国际期刊/会议 vs 国内期刊/会议的选择策略
-   - 英文发表 vs 中文发表的考虑
-
-2. 按时间线排列的投稿计划（**基于当前时间{current_date_str}，考虑截止时间和审稿周期**）
-   - 短期目标（{current_month}起3-6个月内，即到{future_6_months}）
-   - 中期目标（6-12个月内，即到{future_12_months}）
-   - 长期目标（1年以上，即{future_year}年以后）
-
-3. 风险分散策略
-   - 同时投稿多个不同级别的目标
-   - 考虑该学科的一稿多投政策
-   - 备选方案和应急策略
-
-4. 针对论文可能需要的改进建议
-   - 根据目标期刊/会议的要求调整内容
-   - 语言和格式的优化建议
-   - 补充实验或分析的建议
-
-5. 预期的发表时间线和成功概率评估（基于当前时间{current_date_str}）
-
-6. 该学科特有的发表注意事项
-   - 伦理审查要求（如医学、心理学等）
-   - 数据开放要求（如某些自然科学领域）
-   - 利益冲突声明（如医学、工程等）
-   - 医学领域特殊要求：
-     * 临床试验注册要求（ClinicalTrials.gov、中国临床试验注册中心等）
-     * 患者知情同意和隐私保护
-     * 医学伦理委员会批准证明
-     * CONSORT、STROBE、PRISMA等报告规范遵循
-     * 药物/器械安全性数据要求
-     * CME学分认证相关要求
-     * 临床指南和循证医学等级要求
-   - 其他学科特殊要求
-
-请以表格形式总结前10个最推荐的投稿目标（期刊+会议），包括优先级排序、预期时间线和成功概率。
-
-**注意：所有时间规划都应基于当前时间{current_date_str}进行计算，不要使用虚构的时间。**"""
-        
-        try:
-            response = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=priority_prompt,
-                inputs_show_user="生成投稿优先级总结",
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history=[],
-                sys_prompt="你是一个资深的跨学科学术发表策略专家，熟悉各个学科的发表文化、惯例和要求。请综合考虑不同学科的特点：理工科通常重视期刊发表和影响因子，社会科学平衡期刊和专著，人文学科重视同行评议和学术声誉，医学重视临床意义和伦理规范。请为作者制定最适合其学科背景的投稿策略和时间规划。"
-            )
-            
-            if response:
-                return response
-            return "优先级总结生成失败"
-            
-        except Exception as e:
-            self.chatbot.append(["错误", f"生成优先级总结时出错: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return "优先级总结生成失败: " + str(e)
-    
-    def save_recommendations(self, journal_recommendations: str, conference_recommendations: str, priority_summary: str) -> Generator:
-        """保存推荐报告"""
-        timestamp = time.strftime("%Y%m%d_%H%M%S")
-        
-        # 保存为Markdown文件
-        try:
-            md_content = f"""# 论文期刊会议推荐报告
-
-## 投稿优先级总结
-
-{priority_summary}
-
-## 期刊推荐
-
-{journal_recommendations}
-
-## 会议推荐
-
-{conference_recommendations}
-
---
-
-# 详细分析结果
-"""
-            
-            # 添加详细分析结果
-            for q in self.questions:
-                if q.id in self.analysis_results:
-                    md_content += f"\n\n## {q.description}\n\n{self.analysis_results[q.id]}"
-                    
-            result_file = write_history_to_file(
-                history=[md_content],
-                file_basename=f"期刊会议推荐_{timestamp}.md"
-            )
-            
-            if result_file and os.path.exists(result_file):
-                promote_file_to_downloadzone(result_file, chatbot=self.chatbot)
-                self.chatbot.append(["保存成功", f"推荐报告已保存至: {os.path.basename(result_file)}"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-            else:
-                self.chatbot.append(["警告", "保存报告成功但找不到文件"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-        except Exception as e:
-            self.chatbot.append(["警告", f"保存报告失败: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-    
-    def recommend_venues(self, paper_path: str) -> Generator:
-        """推荐期刊会议主流程"""
-        # 加载论文
-        success = yield from self._load_paper(paper_path)
-        if not success:
-            return
-        
-        # 分析关键问题
-        for question in self.questions:
-            yield from self._analyze_question(question)
-        
-        # 分别生成期刊和会议推荐
-        journal_recommendations = yield from self._generate_journal_recommendations()
-        conference_recommendations = yield from self._generate_conference_recommendations()
-        
-        # 生成优先级总结
-        priority_summary = yield from self._generate_priority_summary(journal_recommendations, conference_recommendations)
-        
-        # 显示结果
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-        
-        # 保存报告
-        yield from self.save_recommendations(journal_recommendations, conference_recommendations, priority_summary)
-        
-        # 将完整的分析结果和推荐内容添加到历史记录中，方便用户继续提问
-        self._add_to_history(journal_recommendations, conference_recommendations, priority_summary)
-        
-    def _add_to_history(self, journal_recommendations: str, conference_recommendations: str, priority_summary: str):
-        """将分析结果和推荐内容添加到历史记录中"""
-        try:
-            # 构建完整的内容摘要
-            history_content = f"""# 论文期刊会议推荐分析完成
-
-## 📊 投稿优先级总结
-{priority_summary}
-
-## 📚 期刊推荐
-{journal_recommendations}
-
-## 🏛️ 会议推荐
-{conference_recommendations}
-
-## 📋 详细分析结果
-"""
-            
-            # 添加详细分析结果
-            for q in self.questions:
-                if q.id in self.analysis_results:
-                    history_content += f"\n### {q.description}\n{self.analysis_results[q.id]}\n"
-            
-            history_content += "\n---\n💡 您现在可以基于以上分析结果继续提问，比如询问特定期刊的详细信息、投稿策略建议、或者对推荐结果的进一步解释。"
-            
-            # 添加到历史记录中
-            self.history.append("论文期刊会议推荐分析")
-            self.history.append(history_content)
-            
-            self.chatbot.append(["✅ 分析完成", "所有分析结果和推荐内容已添加到对话历史中，您可以继续基于这些内容提问。"])
-            
-        except Exception as e:
-            self.chatbot.append(["警告", f"添加到历史记录时出错: {str(e)}，但推荐报告已正常生成"])
-            # 即使添加历史失败，也不影响主要功能
-
-
-def _find_paper_file(path: str) -> str:
-    """查找路径中的论文文件（简化版）"""
-    if os.path.isfile(path):
-        return path
-        
-    # 支持的文件扩展名（按优先级排序）
-    extensions = ["pdf", "docx", "doc", "txt", "md", "tex"]
-    
-    # 简单地遍历目录
-    if os.path.isdir(path):
-        try:
-            for ext in extensions:
-                # 手动检查每个可能的文件，而不使用glob
-                potential_file = os.path.join(path, f"paper.{ext}")
-                if os.path.exists(potential_file) and os.path.isfile(potential_file):
-                    return potential_file
-                    
-            # 如果没找到特定命名的文件，检查目录中的所有文件
-            for file in os.listdir(path):
-                file_path = os.path.join(path, file)
-                if os.path.isfile(file_path):
-                    file_ext = file.split('.')[-1].lower() if '.' in file else ""
-                    if file_ext in extensions:
-                        return file_path
-        except Exception:
-            pass  # 忽略任何错误
-    
-    return None
-
-
-def download_paper_by_id(paper_info, chatbot, history) -> str:
-    """下载论文并返回保存路径
-    
-    Args:
-        paper_info: 元组，包含论文ID类型（arxiv或doi）和ID值
-        chatbot: 聊天机器人对象
-        history: 历史记录
-        
-    Returns:
-        str: 下载的论文路径或None
-    """
-    id_type, paper_id = paper_info
-    
-    # 创建保存目录 - 使用时间戳创建唯一文件夹
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    user_name = chatbot.get_user() if hasattr(chatbot, 'get_user') else "default"
-    from toolbox import get_log_folder, get_user
-    base_save_dir = get_log_folder(get_user(chatbot), plugin_name='paper_download')
-    save_dir = os.path.join(base_save_dir, f"papers_{timestamp}")
-    if not os.path.exists(save_dir):
-        os.makedirs(save_dir)
-    save_path = Path(save_dir)
-    
-    chatbot.append([f"下载论文", f"正在下载{'arXiv' if id_type == 'arxiv' else 'DOI'} {paper_id} 的论文..."])
-    update_ui(chatbot=chatbot, history=history)
-    
-    pdf_path = None
-    
-    try:
-        if id_type == 'arxiv':
-            # 使用改进的arxiv查询方法
-            formatted_id = format_arxiv_id(paper_id)
-            paper_result = get_arxiv_paper(formatted_id)
-            
-            if not paper_result:
-                chatbot.append([f"下载失败", f"未找到arXiv论文: {paper_id}"])
-                update_ui(chatbot=chatbot, history=history)
-                return None
-            
-            # 下载PDF
-            filename = f"arxiv_{paper_id.replace('/', '_')}.pdf"
-            pdf_path = str(save_path / filename)
-            paper_result.download_pdf(filename=pdf_path)
-            
-        else:  # doi
-            # 下载DOI
-            sci_hub = SciHub(
-                doi=paper_id,
-                path=save_path
-            )
-            pdf_path = sci_hub.fetch()
-        
-        # 检查下载结果
-        if pdf_path and os.path.exists(pdf_path):
-            promote_file_to_downloadzone(pdf_path, chatbot=chatbot)
-            chatbot.append([f"下载成功", f"已成功下载论文: {os.path.basename(pdf_path)}"])
-            update_ui(chatbot=chatbot, history=history)
-            return pdf_path
-        else:
-            chatbot.append([f"下载失败", f"论文下载失败: {paper_id}"])
-            update_ui(chatbot=chatbot, history=history)
-            return None
-            
-    except Exception as e:
-        chatbot.append([f"下载错误", f"下载论文时出错: {str(e)}"])
-        update_ui(chatbot=chatbot, history=history)
-        return None
-
-
-@CatchException
-def 论文期刊会议推荐(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-                history: List, system_prompt: str, user_request: str):
-    """主函数 - 论文期刊会议推荐"""
-    # 初始化推荐器
-    chatbot.append(["函数插件功能及使用方式", "论文期刊会议推荐：基于论文内容分析，为您推荐合适的学术期刊和会议投稿目标。适用于各个学科专业（自然科学、工程技术、医学、社会科学、人文学科等），根据不同学科的评价标准和发表文化，提供分层次的期刊会议推荐、影响因子分析、发表难度评估、投稿策略建议等。<br><br>📋 使用方式：<br>1、直接上传PDF文件<br>2、输入DOI号或arXiv ID<br>3、点击插件开始分析"])
-    yield from update_ui(chatbot=chatbot, history=history)
-    
-    paper_file = None
-    
-    # 检查输入是否为论文ID（arxiv或DOI）
-    paper_info = extract_paper_id(txt)
-    
-    if paper_info:
-        # 如果是论文ID，下载论文
-        chatbot.append(["检测到论文ID", f"检测到{'arXiv' if paper_info[0] == 'arxiv' else 'DOI'} ID: {paper_info[1]}，准备下载论文..."])
-        yield from update_ui(chatbot=chatbot, history=history)
-        
-        # 下载论文
-        paper_file = download_paper_by_id(paper_info, chatbot, history)
-        
-        if not paper_file:
-            report_exception(chatbot, history, a=f"下载论文失败", b=f"无法下载{'arXiv' if paper_info[0] == 'arxiv' else 'DOI'}论文: {paper_info[1]}")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-    else:
-        # 检查输入路径
-        if not os.path.exists(txt):
-            report_exception(chatbot, history, a=f"解析论文: {txt}", b=f"找不到文件或无权访问: {txt}")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-            
-        # 验证路径安全性
-        user_name = chatbot.get_user()
-        validate_path_safety(txt, user_name)
-        
-        # 查找论文文件
-        paper_file = _find_paper_file(txt)
-        
-        if not paper_file:
-            report_exception(chatbot, history, a=f"解析论文", b=f"在路径 {txt} 中未找到支持的论文文件")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-    
-    yield from update_ui(chatbot=chatbot, history=history)
-    
-    # 确保paper_file是字符串
-    if paper_file is not None and not isinstance(paper_file, str):
-        # 尝试转换为字符串
-        try:
-            paper_file = str(paper_file)
-        except:
-            report_exception(chatbot, history, a=f"类型错误", b=f"论文路径不是有效的字符串: {type(paper_file)}")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
-    
-    # 开始推荐
-    chatbot.append(["开始分析", f"正在分析论文并生成期刊会议推荐: {os.path.basename(paper_file)}"])
-    yield from update_ui(chatbot=chatbot, history=history)
-    
-    recommender = JournalConferenceRecommender(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    yield from recommender.recommend_venues(paper_file) 
--- a/crazy_functions/paper_fns/paper_download.py
+++ b/crazy_functions/paper_fns/paper_download.py
@@ -1,295 +0,0 @@
-import re
-import os
-import zipfile
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
-
-from pathlib import Path
-from datetime import datetime
-
-def extract_paper_id(txt):
-    """从输入文本中提取论文ID"""
-    # 尝试匹配DOI（将DOI匹配提前，因为其格式更加明确）
-    doi_patterns = [
-        r'doi.org/([\w\./-]+)',        # doi.org/10.1234/xxx
-        r'doi:\s*([\w\./-]+)',         # doi: 10.1234/xxx
-        r'(10\.\d{4,}/[\w\.-]+)',      # 直接输入DOI: 10.1234/xxx
-    ]
-
-    for pattern in doi_patterns:
-        match = re.search(pattern, txt, re.IGNORECASE)
-        if match:
-            return ('doi', match.group(1))
-
-    # 尝试匹配arXiv ID
-    arxiv_patterns = [
-        r'arxiv.org/abs/(\d+\.\d+)',           # arxiv.org/abs/2103.14030
-        r'arxiv.org/pdf/(\d+\.\d+)',           # arxiv.org/pdf/2103.14030
-        r'arxiv/(\d+\.\d+)',                   # arxiv/2103.14030
-        r'^(\d{4}\.\d{4,5})$',                 # 直接输入ID: 2103.14030
-        # 添加对早期arXiv ID的支持
-        r'arxiv.org/abs/([\w-]+/\d{7})',       # arxiv.org/abs/math/0211159
-        r'arxiv.org/pdf/([\w-]+/\d{7})',       # arxiv.org/pdf/hep-th/9901001
-        r'^([\w-]+/\d{7})$',                   # 直接输入: math/0211159
-    ]
-
-    for pattern in arxiv_patterns:
-        match = re.search(pattern, txt, re.IGNORECASE)
-        if match:
-            paper_id = match.group(1)
-            # 如果是新格式（YYMM.NNNNN）或旧格式（category/NNNNNNN），都直接返回
-            if re.match(r'^\d{4}\.\d{4,5}$', paper_id) or re.match(r'^[\w-]+/\d{7}$', paper_id):
-                return ('arxiv', paper_id)
-
-    return None
-
-def extract_paper_ids(txt):
-    """从输入文本中提取多个论文ID"""
-    paper_ids = []
-
-    # 首先按换行符分割
-    for line in txt.strip().split('\n'):
-        line = line.strip()
-        if not line:  # 跳过空行
-            continue
-
-        # 对每一行再按空格分割
-        for item in line.split():
-            item = item.strip()
-            if not item:  # 跳过空项
-                continue
-            paper_info = extract_paper_id(item)
-            if paper_info:
-                paper_ids.append(paper_info)
-
-    # 去除重复项，保持顺序
-    unique_paper_ids = []
-    seen = set()
-    for paper_info in paper_ids:
-        if paper_info not in seen:
-            seen.add(paper_info)
-            unique_paper_ids.append(paper_info)
-
-    return unique_paper_ids
-
-def format_arxiv_id(paper_id):
-    """格式化arXiv ID，处理新旧两种格式"""
-    # 如果是旧格式 (e.g. astro-ph/0404140)，需要去掉arxiv:前缀
-    if '/' in paper_id:
-        return paper_id.replace('arxiv:', '')  # 确保移除可能存在的arxiv:前缀
-    return paper_id
-
-def get_arxiv_paper(paper_id):
-    """获取arXiv论文，处理新旧两种格式"""
-    import arxiv
-
-    # 尝试不同的查询方式
-    query_formats = [
-        paper_id,                    # 原始ID
-        paper_id.replace('/', ''),   # 移除斜杠
-        f"id:{paper_id}",           # 添加id:前缀
-    ]
-
-    for query in query_formats:
-        try:
-            # 使用Search查询
-            search = arxiv.Search(
-                query=query,
-                max_results=1
-            )
-            result = next(arxiv.Client().results(search))
-            if result:
-                return result
-        except:
-            continue
-
-        try:
-            # 使用id_list查询
-            search = arxiv.Search(id_list=[query])
-            result = next(arxiv.Client().results(search))
-            if result:
-                return result
-        except:
-            continue
-
-    return None
-
-def create_zip_archive(files, save_path):
-    """将多个PDF文件打包成zip"""
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    zip_filename = f"papers_{timestamp}.zip"
-    zip_path = str(save_path / zip_filename)
-
-    with zipfile.ZipFile(zip_path, 'w') as zipf:
-        for file in files:
-            if os.path.exists(file):
-                # 只添加文件名，不包含路径
-                zipf.write(file, os.path.basename(file))
-
-    return zip_path
-
-@CatchException
-def 论文下载(txt: str, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt: 用户输入，可以是DOI、arxiv ID或相关链接，支持多行输入进行批量下载
-    """
-    from crazy_functions.doc_fns.text_content_loader import TextContentLoader
-    from crazy_functions.review_fns.data_sources.arxiv_source import ArxivSource
-    from crazy_functions.review_fns.data_sources.scihub_source import SciHub
-    # 解析输入
-    paper_infos = extract_paper_ids(txt)
-    if not paper_infos:
-        chatbot.append(["输入解析", "未能识别任何论文ID或DOI，请检查输入格式。支持以下格式：\n- arXiv ID (例如：2103.14030)\n- arXiv链接\n- DOI (例如：10.1234/xxx)\n- DOI链接\n\n多个论文ID请用换行分隔。"])
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 创建保存目录 - 使用时间戳创建唯一文件夹
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    base_save_dir = get_log_folder(get_user(chatbot), plugin_name='paper_download')
-    save_dir = os.path.join(base_save_dir, f"papers_{timestamp}")
-    if not os.path.exists(save_dir):
-        os.makedirs(save_dir)
-    save_path = Path(save_dir)
-
-    # 记录下载结果
-    success_count = 0
-    failed_papers = []
-    downloaded_files = []  # 记录成功下载的文件路径
-
-    chatbot.append([f"开始下载", f"支持多行输入下载多篇论文，共检测到 {len(paper_infos)} 篇论文，开始下载..."])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    for id_type, paper_id in paper_infos:
-        try:
-            if id_type == 'arxiv':
-                chatbot.append([f"正在下载", f"从arXiv下载论文 {paper_id}..."])
-                yield from update_ui(chatbot=chatbot, history=history)
-
-                # 使用改进的arxiv查询方法
-                formatted_id = format_arxiv_id(paper_id)
-                paper_result = get_arxiv_paper(formatted_id)
-
-                if not paper_result:
-                    failed_papers.append((paper_id, "未找到论文"))
-                    continue
-
-                # 下载PDF
-                try:
-                    filename = f"arxiv_{paper_id.replace('/', '_')}.pdf"
-                    pdf_path = str(save_path / filename)
-                    paper_result.download_pdf(filename=pdf_path)
-                    if os.path.exists(pdf_path):
-                        downloaded_files.append(pdf_path)
-                except Exception as e:
-                    failed_papers.append((paper_id, f"PDF下载失败: {str(e)}"))
-                    continue
-
-            else:  # doi
-                chatbot.append([f"正在下载", f"从Sci-Hub下载论文 {paper_id}..."])
-                yield from update_ui(chatbot=chatbot, history=history)
-
-                sci_hub = SciHub(
-                    doi=paper_id,
-                    path=save_path
-                )
-                pdf_path = sci_hub.fetch()
-                if pdf_path and os.path.exists(pdf_path):
-                    downloaded_files.append(pdf_path)
-
-            # 检查下载结果
-            if pdf_path and os.path.exists(pdf_path):
-                promote_file_to_downloadzone(pdf_path, chatbot=chatbot)
-                success_count += 1
-            else:
-                failed_papers.append((paper_id, "下载失败"))
-
-        except Exception as e:
-            failed_papers.append((paper_id, str(e)))
-
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 创建ZIP压缩包
-    if downloaded_files:
-        try:
-            zip_path = create_zip_archive(downloaded_files, Path(base_save_dir))
-            promote_file_to_downloadzone(zip_path, chatbot=chatbot)
-            chatbot.append([
-                f"创建压缩包",
-                f"已将所有下载的论文打包为: {os.path.basename(zip_path)}"
-            ])
-            yield from update_ui(chatbot=chatbot, history=history)
-        except Exception as e:
-            chatbot.append([
-                f"创建压缩包失败",
-                f"打包文件时出现错误: {str(e)}"
-            ])
-            yield from update_ui(chatbot=chatbot, history=history)
-
-    # 生成最终报告
-    summary = f"下载完成！成功下载 {success_count} 篇论文。\n"
-    if failed_papers:
-        summary += "\n以下论文下载失败：\n"
-        for paper_id, reason in failed_papers:
-            summary += f"- {paper_id}: {reason}\n"
-
-    if downloaded_files:
-        summary += f"\n所有论文已存放在文件夹 '{save_dir}' 中，并打包到压缩文件中。您可以在下载区找到单个PDF文件和压缩包。"
-
-    chatbot.append([
-        f"下载完成",
-        summary
-    ])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    # 如果下载成功且用户想要直接阅读内容
-    if downloaded_files:
-        chatbot.append([
-            "提示",
-            "正在读取论文内容进行分析，请稍候..."
-        ])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-        # 使用TextContentLoader加载整个文件夹的PDF文件内容
-        loader = TextContentLoader(chatbot, history)
-
-        # 删除提示信息
-        chatbot.pop()
-
-        # 加载PDF内容 - 传入文件夹路径而不是单个文件路径
-        yield from loader.execute(save_dir)
-
-        # 添加提示信息
-        chatbot.append([
-            "提示",
-            "论文内容已加载完毕，您可以直接向AI提问有关该论文的问题。"
-        ])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-if __name__ == "__main__":
-    # 测试代码
-    import asyncio
-    async def test():
-        # 测试批量输入
-        batch_inputs = [
-            # 换行分隔的测试
-            """https://arxiv.org/abs/2103.14030
-            math/0211159
-            10.1038/s41586-021-03819-2""",
-
-            # 空格分隔的测试
-            "https://arxiv.org/abs/2103.14030 math/0211159 10.1038/s41586-021-03819-2",
-
-            # 混合分隔的测试
-            """https://arxiv.org/abs/2103.14030 math/0211159
-            10.1038/s41586-021-03819-2 https://doi.org/10.1038/s41586-021-03819-2
-            2103.14030""",
-        ]
-
-        for i, test_input in enumerate(batch_inputs, 1):
-            print(f"\n测试用例 {i}:")
-            print(f"输入: {test_input}")
-            results = extract_paper_ids(test_input)
-            print(f"解析结果:")
-            for result in results:
-                print(f"  {result}")
-
-    asyncio.run(test())
--- a/crazy_functions/paper_fns/reduce_aigc.py
+++ b/crazy_functions/paper_fns/reduce_aigc.py
@@ -1,867 +0,0 @@
-import os
-import time
-import glob
-import re
-import threading
-from typing import Dict, List, Generator, Tuple
-from dataclasses import dataclass
-
-from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-from crazy_functions.rag_fns.rag_file_support import extract_text,  convert_to_markdown
-from request_llms.bridge_all import model_info
-from toolbox import update_ui, CatchException, report_exception, promote_file_to_downloadzone, write_history_to_file
-from shared_utils.fastapi_server import validate_path_safety
-
-# 新增：导入结构化论文提取器
-from crazy_functions.doc_fns.read_fns.unstructured_all.paper_structure_extractor import PaperStructureExtractor, ExtractorConfig, StructuredPaper
-
-# 导入格式化器
-from crazy_functions.paper_fns.file2file_doc import (
-    TxtFormatter,
-    MarkdownFormatter,
-    HtmlFormatter,
-    WordFormatter
-)
-
-@dataclass
-class TextFragment:
-    """文本片段数据类，用于组织处理单元"""
-    content: str
-    fragment_index: int
-    total_fragments: int
-
-
-class DocumentProcessor:
-    """文档处理器 - 处理单个文档并输出结果"""
-
-    def __init__(self, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List, history: List, system_prompt: str):
-        """初始化处理器"""
-        self.llm_kwargs = llm_kwargs.copy()  # 创建原始llm_kwargs的副本
-        # 固定使用deepseek-reasoner模型
-        self.llm_kwargs['llm_model'] = "deepseek-reasoner"
-        self.plugin_kwargs = plugin_kwargs
-        self.chatbot = chatbot
-        self.history = history
-        self.system_prompt = system_prompt
-        self.processed_results = []
-        self.failed_fragments = []
-        # 新增：初始化论文结构提取器
-        self.paper_extractor = PaperStructureExtractor()
-        # 修改：设置降重次数，默认为1次（原为3次）
-        self.reduction_times = int(self.plugin_kwargs.get("reduction_times", 1))
-
-    def _get_token_limit(self) -> int:
-        """获取模型token限制，返回更小的值以确保更细粒度的分割"""
-        max_token = model_info[self.llm_kwargs['llm_model']]['max_token']
-        # 降低token限制，使每个片段更小
-        return max_token // 4  # 从3/4降低到1/4
-
-    def _create_batch_inputs(self, fragments: List[TextFragment], current_iteration: int = 1) -> Tuple[List, List, List]:
-        """创建批处理输入"""
-        inputs_array = []
-        inputs_show_user_array = []
-        history_array = []
-
-        # 基础指令 - 增强单次调用的效果
-        user_instruction = self.plugin_kwargs.get("advanced_arg", """你是一个人类科研工作者，请对以下学术文本进行深度改写，以彻底消除AI生成特征。具体要求如下：
-
-1. 保持学术写作的严谨性和专业性，但加入一些人类写作的不完美元素
-2. 维持原文的核心论述和逻辑框架，但打破过于完美的结构
-3. 彻底改变句式结构：
-   - 灵活运用主动句与被动句，偏好使用主动语态
-   - 适当使用不同长度的句子，包括一些较长的复杂句
-   - 引入变化多样的句式，打破规律性
-   - 完全避免AI常用的模板化句式和套路表达
-   - 增加一些学术写作中常见的转折和连接方式
-4. 全面改善用词：
-   - 使用更多学术语境下的专业词汇和同义词替换
-   - 避免过于机械和规律性的连接词，使用更自然的过渡
-   - 重构专业术语的表达方式，但保持准确性
-   - 增加词汇多样性，减少重复用词
-   - 偶尔使用一些不太常见但恰当的词汇
-5. 模拟真实学者的写作风格：
-   - 注重论证的严密性，但允许存在一些微小的不对称性
-   - 保持表达的客观性，同时适度体现个人学术见解
-   - 在适当位置表达观点时更加自信和坚定
-   - 避免过于完美和机械均衡的论述结构
-   - 允许段落长度有所变化，不要过于均匀
-6. 引入人类学者常见的写作特点：
-   - 段落之间的过渡更加自然流畅
-   - 适当使用一些学术界常见的修辞手法，但不过度使用
-   - 偶尔使用一些强调和限定性表达
-   - 适当使用一些学术界认可的个人化表达
-7. 彻底消除AI痕迹：
-   - 避免过于规整和均衡的段落结构
-   - 避免机械性的句式变化和词汇替换模式
-   - 避免过于完美的逻辑推导，适当增加一些转折
-   - 减少公式化的表达方式""")
-
-        # 对于单次调用的场景，不需要迭代前缀，直接使用更强力的改写指令
-        for frag in fragments:
-            # 在单次调用时使用更强力的指令
-            if self.reduction_times == 1:
-                i_say = (f'请对以下学术文本进行彻底改写，完全消除AI特征，使其像真实人类学者撰写的内容。\n\n{user_instruction}\n\n'
-                         f'请记住以下几点：\n'
-                         f'1. 避免过于规整和均衡的结构\n'
-                         f'2. 引入一些人类写作的微小不完美之处\n'
-                         f'3. 使用多样化的句式和词汇\n'
-                         f'4. 打破可能的AI规律性表达模式\n'
-                         f'5. 适当使用一些专业领域内的表达习惯\n\n'
-                         f'请将对文本的处理结果放在<decision>和</decision>标签之间。\n\n'
-                         f'文本内容：\n```\n{frag.content}\n```')
-            else:
-                # 原有的迭代前缀逻辑
-                iteration_prefix = ""
-                if current_iteration > 1:
-                    iteration_prefix = f"这是第{current_iteration}次改写，请在保持学术性的基础上，采用更加人性化、不同的表达方式。"
-                    if current_iteration == 2:
-                        iteration_prefix += "在保持专业性的同时，进一步优化句式结构和用词，显著降低AI痕迹。"
-                    elif current_iteration >= 3:
-                        iteration_prefix += "请在确保不损失任何学术内容的前提下，彻底重构表达方式，并适当引入少量人类学者常用的表达技巧，避免过度使用比喻和类比。"
-
-                i_say = (f'请按照以下要求处理文本内容：{iteration_prefix}{user_instruction}\n\n'
-                         f'请将对文本的处理结果放在<decision>和</decision>标签之间。\n\n'
-                         f'文本内容：\n```\n{frag.content}\n```')
-
-            i_say_show_user = f'正在处理文本片段 {frag.fragment_index + 1}/{frag.total_fragments}'
-
-            inputs_array.append(i_say)
-            inputs_show_user_array.append(i_say_show_user)
-            history_array.append([])
-
-        return inputs_array, inputs_show_user_array, history_array
-
-    def _extract_decision(self, text: str) -> str:
-        """从LLM响应中提取<decision>标签内的内容"""
-        import re
-        pattern = r'<decision>(.*?)</decision>'
-        matches = re.findall(pattern, text, re.DOTALL)
-
-        if matches:
-            return matches[0].strip()
-        else:
-            # 如果没有找到标签，返回原始文本
-            return text.strip()
-
-    def process_file(self, file_path: str) -> Generator:
-        """处理单个文件"""
-        self.chatbot.append(["开始处理文件", f"文件路径: {file_path}"])
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        try:
-            # 首先尝试转换为Markdown
-            file_path = convert_to_markdown(file_path)
-
-            # 1. 检查文件是否为支持的论文格式
-            is_paper_format = any(file_path.lower().endswith(ext) for ext in self.paper_extractor.SUPPORTED_EXTENSIONS)
-
-            if is_paper_format:
-                # 使用结构化提取器处理论文
-                return (yield from self._process_structured_paper(file_path))
-            else:
-                # 使用原有方式处理普通文档
-                return (yield from self._process_regular_file(file_path))
-
-        except Exception as e:
-            self.chatbot.append(["处理错误", f"文件处理失败: {str(e)}"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return None
-
-    def _process_structured_paper(self, file_path: str) -> Generator:
-        """处理结构化论文文件"""
-        # 1. 提取论文结构
-        self.chatbot[-1] = ["正在分析论文结构", f"文件路径: {file_path}"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        try:
-            paper = self.paper_extractor.extract_paper_structure(file_path)
-
-            if not paper or not paper.sections:
-                self.chatbot.append(["无法提取论文结构", "将使用全文内容进行处理"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-                # 使用全文内容进行段落切分
-                if paper and paper.full_text:
-                    # 使用增强的分割函数进行更细致的分割
-                    fragments = self._breakdown_section_content(paper.full_text)
-
-                    # 创建文本片段对象
-                    text_fragments = []
-                    for i, frag in enumerate(fragments):
-                        if frag.strip():
-                            text_fragments.append(TextFragment(
-                                content=frag,
-                                fragment_index=i,
-                                total_fragments=len(fragments)
-                            ))
-
-                    # 多次降重处理
-                    if text_fragments:
-                        current_fragments = text_fragments
-
-                        # 进行多轮降重处理
-                        for iteration in range(1, self.reduction_times + 1):
-                            # 处理当前片段
-                            processed_content = yield from self._process_text_fragments(current_fragments, iteration)
-
-                            # 如果这是最后一次迭代，保存结果
-                            if iteration == self.reduction_times:
-                                final_content = processed_content
-                                break
-
-                            # 否则，准备下一轮迭代的片段
-                            # 从处理结果中提取处理后的内容
-                            next_fragments = []
-                            for idx, item in enumerate(self.processed_results):
-                                next_fragments.append(TextFragment(
-                                    content=item['content'],
-                                    fragment_index=idx,
-                                    total_fragments=len(self.processed_results)
-                                ))
-
-                            current_fragments = next_fragments
-
-                        # 更新UI显示最终结果
-                        self.chatbot[-1] = ["处理完成", f"共完成 {self.reduction_times} 轮降重"]
-                        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-                        return final_content
-                    else:
-                        self.chatbot.append(["处理失败", "未能提取到有效的文本内容"])
-                        yield from update_ui(chatbot=self.chatbot, history=self.history)
-                        return None
-                else:
-                    self.chatbot.append(["处理失败", "未能提取到论文内容"])
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                    return None
-
-            # 2. 准备处理章节内容（不处理标题）
-            self.chatbot[-1] = ["已提取论文结构", f"共 {len(paper.sections)} 个主要章节"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            # 3. 收集所有需要处理的章节内容并分割为合适大小
-            sections_to_process = []
-            section_map = {}  # 用于映射处理前后的内容
-
-            def collect_section_contents(sections, parent_path=""):
-                """递归收集章节内容，跳过参考文献部分"""
-                for i, section in enumerate(sections):
-                    current_path = f"{parent_path}/{i}" if parent_path else f"{i}"
-
-                    # 检查是否为参考文献部分，如果是则跳过
-                    if section.section_type == 'references' or section.title.lower() in ['references', '参考文献', 'bibliography', '文献']:
-                        continue  # 跳过参考文献部分
-
-                    # 只处理内容非空的章节
-                    if section.content and section.content.strip():
-                        # 使用增强的分割函数进行更细致的分割
-                        fragments = self._breakdown_section_content(section.content)
-
-                        for fragment_idx, fragment_content in enumerate(fragments):
-                            if fragment_content.strip():
-                                fragment_index = len(sections_to_process)
-                                sections_to_process.append(TextFragment(
-                                    content=fragment_content,
-                                    fragment_index=fragment_index,
-                                    total_fragments=0  # 临时值，稍后更新
-                                ))
-
-                                # 保存映射关系，用于稍后更新章节内容
-                                # 为每个片段存储原始章节和片段索引信息
-                                section_map[fragment_index] = (current_path, section, fragment_idx, len(fragments))
-
-                    # 递归处理子章节
-                    if section.subsections:
-                        collect_section_contents(section.subsections, current_path)
-
-            # 收集所有章节内容
-            collect_section_contents(paper.sections)
-
-            # 更新总片段数
-            total_fragments = len(sections_to_process)
-            for frag in sections_to_process:
-                frag.total_fragments = total_fragments
-
-            # 4. 如果没有内容需要处理，直接返回
-            if not sections_to_process:
-                self.chatbot.append(["处理完成", "未找到需要处理的内容"])
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                return None
-
-            # 5. 批量处理章节内容
-            self.chatbot[-1] = ["开始处理论文内容", f"共 {len(sections_to_process)} 个内容片段"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            # 一次性准备所有输入
-            inputs_array, inputs_show_user_array, history_array = self._create_batch_inputs(sections_to_process)
-
-            # 使用系统提示
-            instruction = self.plugin_kwargs.get("advanced_arg", """请对以下学术文本进行彻底改写，以显著降低AI生成特征。具体要求如下：
-
-1. 保持学术写作的严谨性和专业性
-2. 维持原文的核心论述和逻辑框架
-3. 优化句式结构：
-   - 灵活运用主动句与被动句
-   - 适当拆分复杂句式，提高可读性
-   - 注意句式的多样性，避免重复模式
-   - 打破AI常用的句式模板
-4. 改善用词：
-   - 使用更多学术语境下的同义词替换
-   - 避免过于机械和规律性的连接词
-   - 适当调整专业术语的表达方式
-   - 增加词汇多样性，减少重复用词
-5. 增强文本的学术特征：
-   - 注重论证的严密性
-   - 保持表达的客观性
-   - 适度体现作者的学术见解
-   - 避免过于完美和均衡的论述结构
-6. 确保语言风格的一致性
-7. 减少AI生成文本常见的套路和模式""")
-            sys_prompt_array = [f"""作为一位专业的学术写作顾问，请按照以下要求改写文本：
-
-1. 严格保持学术写作规范
-2. 维持原文的核心论述和逻辑框架
-3. 通过优化句式结构和用词降低AI生成特征
-4. 确保语言风格的一致性和专业性
-5. 保持内容的客观性和准确性
-6. 避免AI常见的套路化表达和过于完美的结构"""] * len(sections_to_process)
-
-            # 调用LLM一次性处理所有片段
-            response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                inputs_array=inputs_array,
-                inputs_show_user_array=inputs_show_user_array,
-                llm_kwargs=self.llm_kwargs,
-                chatbot=self.chatbot,
-                history_array=history_array,
-                sys_prompt_array=sys_prompt_array,
-            )
-
-            # 处理响应，重组章节内容
-            section_contents = {}  # 用于重组各章节的处理后内容
-
-            for j, frag in enumerate(sections_to_process):
-                try:
-                    llm_response = response_collection[j * 2 + 1]
-                    processed_text = self._extract_decision(llm_response)
-
-                    if processed_text and processed_text.strip():
-                        # 保存处理结果
-                        self.processed_results.append({
-                            'index': frag.fragment_index,
-                            'content': processed_text
-                        })
-
-                        # 存储处理后的文本片段，用于后续重组
-                        fragment_index = frag.fragment_index
-                        if fragment_index in section_map:
-                            path, section, fragment_idx, total_fragments = section_map[fragment_index]
-
-                            # 初始化此章节的内容容器（如果尚未创建）
-                            if path not in section_contents:
-                                section_contents[path] = [""] * total_fragments
-
-                            # 将处理后的片段放入正确位置
-                            section_contents[path][fragment_idx] = processed_text
-                    else:
-                        self.failed_fragments.append(frag)
-                except Exception as e:
-                    self.failed_fragments.append(frag)
-
-            # 重组每个章节的内容
-            for path, fragments in section_contents.items():
-                section = None
-                for idx in section_map:
-                    if section_map[idx][0] == path:
-                        section = section_map[idx][1]
-                        break
-
-                if section:
-                    # 合并该章节的所有处理后片段
-                    section.content = "\n".join(fragments)
-
-            # 6. 更新UI
-            success_count = total_fragments - len(self.failed_fragments)
-            self.chatbot[-1] = ["处理完成", f"成功处理 {success_count}/{total_fragments} 个内容片段"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            # 收集参考文献部分（不进行处理）
-            references_sections = []
-            def collect_references(sections, parent_path=""):
-                """递归收集参考文献部分"""
-                for i, section in enumerate(sections):
-                    current_path = f"{parent_path}/{i}" if parent_path else f"{i}"
-
-                    # 检查是否为参考文献部分
-                    if section.section_type == 'references' or section.title.lower() in ['references', '参考文献', 'bibliography', '文献']:
-                        references_sections.append((current_path, section))
-
-                    # 递归检查子章节
-                    if section.subsections:
-                        collect_references(section.subsections, current_path)
-
-            # 收集参考文献
-            collect_references(paper.sections)
-
-            # 7. 将处理后的结构化论文转换为Markdown
-            markdown_content = self.paper_extractor.generate_markdown(paper)
-
-            # 8. 返回处理后的内容
-            self.chatbot[-1] = ["处理完成", f"成功处理 {success_count}/{total_fragments} 个内容片段，参考文献部分未处理"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            return markdown_content
-
-        except Exception as e:
-            self.chatbot.append(["结构化处理失败", f"错误: {str(e)}，将尝试作为普通文件处理"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return (yield from self._process_regular_file(file_path))
-
-    def _process_regular_file(self, file_path: str) -> Generator:
-        """使用原有方式处理普通文件"""
-        # 原有的文件处理逻辑
-        self.chatbot[-1] = ["正在读取文件", f"文件路径: {file_path}"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        content = extract_text(file_path)
-        if not content or not content.strip():
-            self.chatbot.append(["处理失败", "文件内容为空或无法提取内容"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return None
-
-        # 2. 分割文本
-        self.chatbot[-1] = ["正在分析文件", "将文件内容分割为适当大小的片段"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 使用增强的分割函数
-        fragments = self._breakdown_section_content(content)
-
-        # 3. 创建文本片段对象
-        text_fragments = []
-        for i, frag in enumerate(fragments):
-            if frag.strip():
-                text_fragments.append(TextFragment(
-                    content=frag,
-                    fragment_index=i,
-                    total_fragments=len(fragments)
-                ))
-
-        # 4. 多轮降重处理
-        if not text_fragments:
-            self.chatbot.append(["处理失败", "未能提取到有效的文本内容"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            return None
-
-        # 批处理大小
-        batch_size = 8  # 每批处理的片段数
-
-        # 第一次迭代
-        current_batches = []
-        for i in range(0, len(text_fragments), batch_size):
-            current_batches.append(text_fragments[i:i + batch_size])
-
-        all_processed_fragments = []
-
-        # 进行多轮降重处理
-        for iteration in range(1, self.reduction_times + 1):
-            self.chatbot[-1] = ["开始处理文本", f"第 {iteration}/{self.reduction_times} 次降重"]
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-            next_batches = []
-            all_processed_fragments = []
-
-            # 分批处理当前迭代的片段
-            for batch in current_batches:
-                # 处理当前批次
-                _ = yield from self._process_text_fragments(batch, iteration)
-
-                # 收集处理结果
-                processed_batch = []
-                for item in self.processed_results:
-                    processed_batch.append(TextFragment(
-                        content=item['content'],
-                        fragment_index=len(all_processed_fragments) + len(processed_batch),
-                        total_fragments=0  # 临时值，稍后更新
-                    ))
-
-                all_processed_fragments.extend(processed_batch)
-
-                # 如果不是最后一轮迭代，准备下一批次
-                if iteration < self.reduction_times:
-                    for i in range(0, len(processed_batch), batch_size):
-                        next_batches.append(processed_batch[i:i + batch_size])
-
-            # 更新总片段数
-            for frag in all_processed_fragments:
-                frag.total_fragments = len(all_processed_fragments)
-
-            # 为下一轮迭代准备批次
-            current_batches = next_batches
-
-        # 合并最终结果
-        final_content = "\n\n".join([frag.content for frag in all_processed_fragments])
-
-        # 5. 更新UI显示最终结果
-        self.chatbot[-1] = ["处理完成", f"共完成 {self.reduction_times} 轮降重"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        return final_content
-
-    def save_results(self, content: str, original_file_path: str) -> List[str]:
-        """保存处理结果为TXT格式"""
-        if not content:
-            return []
-
-        timestamp = time.strftime("%Y%m%d_%H%M%S")
-        original_filename = os.path.basename(original_file_path)
-        filename_without_ext = os.path.splitext(original_filename)[0]
-        base_filename = f"{filename_without_ext}_processed_{timestamp}"
-
-        result_files = []
-
-        # 只保存为TXT
-        try:
-            txt_formatter = TxtFormatter()
-            txt_content = txt_formatter.create_document(content)
-            txt_file = write_history_to_file(
-                history=[txt_content],
-                file_basename=f"{base_filename}.txt"
-            )
-            result_files.append(txt_file)
-        except Exception as e:
-            self.chatbot.append(["警告", f"TXT格式保存失败: {str(e)}"])
-
-        # 添加到下载区
-        for file in result_files:
-            promote_file_to_downloadzone(file, chatbot=self.chatbot)
-
-        return result_files
-
-    def _breakdown_section_content(self, content: str) -> List[str]:
-        """对文本内容进行分割与合并
-
-        主要按段落进行组织，只合并较小的段落以减少片段数量
-        保留原始段落结构，不对长段落进行强制分割
-        针对中英文设置不同的阈值，因为字符密度不同
-        """
-        # 先按段落分割文本
-        paragraphs = content.split('\n\n')
-
-        # 检测语言类型
-        chinese_char_count = sum(1 for char in content if '\u4e00' <= char <= '\u9fff')
-        is_chinese_text = chinese_char_count / max(1, len(content)) > 0.3
-
-        # 根据语言类型设置不同的阈值（只用于合并小段落）
-        if is_chinese_text:
-            # 中文文本：一个汉字就是一个字符，信息密度高
-            min_chunk_size = 300  # 段落合并的最小阈值
-            target_size = 800  # 理想的段落大小
-        else:
-            # 英文文本：一个单词由多个字符组成，信息密度低
-            min_chunk_size = 600  # 段落合并的最小阈值
-            target_size = 1600  # 理想的段落大小
-
-        # 1. 只合并小段落，不对长段落进行分割
-        result_fragments = []
-        current_chunk = []
-        current_length = 0
-
-        for para in paragraphs:
-            # 如果段落太小且不会超过目标大小，则合并
-            if len(para) < min_chunk_size and current_length + len(para) <= target_size:
-                current_chunk.append(para)
-                current_length += len(para)
-            # 否则，创建新段落
-            else:
-                # 如果当前块非空且与当前段落无关，先保存它
-                if current_chunk and current_length > 0:
-                    result_fragments.append('\n\n'.join(current_chunk))
-
-                # 当前段落作为新块
-                current_chunk = [para]
-                current_length = len(para)
-
-            # 如果当前块大小已接近目标大小，保存并开始新块
-            if current_length >= target_size:
-                result_fragments.append('\n\n'.join(current_chunk))
-                current_chunk = []
-                current_length = 0
-
-        # 保存最后一个块
-        if current_chunk:
-            result_fragments.append('\n\n'.join(current_chunk))
-
-        # 2. 处理可能过大的片段（确保不超过token限制）
-        final_fragments = []
-        max_token = self._get_token_limit()
-
-        for fragment in result_fragments:
-            # 检查fragment是否可能超出token限制
-            # 根据语言类型调整token估算
-            if is_chinese_text:
-                estimated_tokens = len(fragment) / 1.5  # 中文每个token约1-2个字符
-            else:
-                estimated_tokens = len(fragment) / 4  # 英文每个token约4个字符
-
-            if estimated_tokens > max_token:
-                # 即使可能超出限制，也尽量保持段落的完整性
-                # 使用breakdown_text但设置更大的限制来减少分割
-                larger_limit = max_token * 0.95  # 使用95%的限制
-                sub_fragments = breakdown_text_to_satisfy_token_limit(
-                    txt=fragment,
-                    limit=larger_limit,
-                    llm_model=self.llm_kwargs['llm_model']
-                )
-                final_fragments.extend(sub_fragments)
-            else:
-                final_fragments.append(fragment)
-
-        return final_fragments
-
-    def _process_text_fragments(self, text_fragments: List[TextFragment], current_iteration: int = 1) -> str:
-        """处理文本片段，支持多次降重
-
-        Args:
-            text_fragments: 要处理的文本片段列表
-            current_iteration: 当前是第几次降重迭代
-
-        Returns:
-            处理后的文本内容
-        """
-        self.chatbot[-1] = ["开始处理文本", f"第 {current_iteration}/{self.reduction_times} 次降重，共 {len(text_fragments)} 个片段"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        # 重置处理结果，为当前迭代做准备
-        self.processed_results = []
-        self.failed_fragments = []
-
-        # 一次性准备所有输入
-        inputs_array, inputs_show_user_array, history_array = self._create_batch_inputs(text_fragments, current_iteration)
-
-        # 对于单次调用的特殊处理 - 使用更强力的系统提示词
-        if self.reduction_times == 1:
-            sys_prompt = f"""你是一个伪装成人类科研学者的AI，能够将各种AI生成内容改写成人类学者常用的表达方式，让人类学者无法分辨。
-
-你的任务是对学术文本进行深度改写，以彻底消除AI特征，使其具有真实人类学者的写作特点：
-
-1. 打破AI常见的规整句式结构和词汇选择模式
-2. 引入学术写作中的自然变化和不规则性
-3. 使用多样化的句式和段落结构，避免明显的平衡性和规律性
-4. 加入人类写作中常见的微小不完美之处，如段落长度变化、论述强调点不均等
-5. 在维持核心论点的同时，完全重构表达方式
-6. 适当使用学科领域内专家常用的表达习惯和术语
-7. 确保文本读起来自然流畅，避免机械感
-8. 打破AI生成内容常见的逻辑结构和表达套路
-9. 根据文本内容，在保持专业性的基础上增加写作的特色和个性
-
-对于每个文本片段，尽可能彻底地重构，使其无法被识别为AI生成。"""
-
-            sys_prompt_array = [sys_prompt] * len(text_fragments)
-
-        else:
-            # 原有的多次迭代处理逻辑
-            # 根据迭代次数调整系统提示词强度
-            base_instruction = self.plugin_kwargs.get("advanced_arg", """你是一个人类科研工作者，请对以下学术文本进行彻底改写，以显著降低AI生成特征。具体要求如下：
-
-1. 保持学术写作的严谨性和专业性
-2. 维持原文的核心论述和逻辑框架
-3. 优化句式结构：
-   - 灵活运用主动句与被动句
-   - 适当拆分复杂句式，提高可读性
-   - 注意句式的多样性，避免重复模式
-   - 打破AI常用的句式模板
-4. 改善用词：
-   - 使用更多学术语境下的同义词替换
-   - 避免过于机械和规律性的连接词
-   - 适当调整专业术语的表达方式
-   - 增加词汇多样性，减少重复用词
-5. 增强文本的学术特征：
-   - 注重论证的严密性
-   - 保持表达的客观性
-   - 适度体现作者的学术见解
-   - 避免过于完美和均衡的论述结构
-6. 确保语言风格的一致性
-7. 减少AI生成文本常见的套路和模式""")
-
-            # 根据迭代次数增强强度
-            if current_iteration == 1:
-                # 第一次迭代使用基础强度
-                instruction = base_instruction
-                sys_prompt = f"""你是一个伪装成人类科研学者的AI，能够将各种AI生成内容改写成人类学者常用的表达方式，让人类学者无法分辨。请进行第{current_iteration}轮深度改写：
-
-1. 严格保持学术写作规范
-2. 维持原文的核心论述和逻辑框架
-3. 通过优化句式结构和用词降低AI生成特征
-4. 确保语言风格的一致性和专业性
-5. 保持内容的客观性和准确性
-6. 避免AI常见的套路化表达和过于完美的结构"""
-            elif current_iteration == 2:
-                # 第二次迭代使用中等强度
-                instruction = base_instruction + """这是第二次改写，请进一步优化：
-
-1. 更深入地调整句式结构，增加表达多样性
-2. 在保持准确性的前提下，替换更多专业术语的表达方式
-3. 优化段落结构，增强逻辑连贯性
-4. 适当调整学术论证的表达方式
-5. 打破AI生成内容的常见模式，如过于均衡的结构和规律性的表达方式"""
-                sys_prompt = f"""你是一个伪装成人类科研学者的AI，能够将各种AI生成内容改写成人类学者常用的表达方式，让人类学者无法分辨。请进行第二轮改写：
-
-1. 在保持严谨性的基础上，优化表达方式
-2. 改善文本结构，提升学术论述的清晰度
-3. 增强专业术语使用的灵活性
-4. 确保论证过程的完整性和逻辑性
-5. 进一步减少AI生成内容的特征和模式
-6. 使文本表现出更自然、更人性化的写作风格"""
-            else:
-                # 第三次或更多次迭代使用高强度
-                instruction = base_instruction + f"""这是第{current_iteration}次改写，需要在保持学术性的前提下彻底重构文本：
-
-1. 全面重构句式，采用不同的表达策略
-2. 系统性替换学术用语，保持专业准确性
-3. 重组段落结构，优化论证层次
-4. 深化学术观点的表达方式
-5. 增强文本的逻辑严密性
-6. 提升论述的专业深度
-7. 确保不遗漏任何学术观点和论证要素
-8. 适当使用学术表达技巧（如精准举例、恰当转折等），但不过分依赖比喻和类比
-9. 彻底消除AI生成内容的特征，使文本具有真实学者的写作风格"""
-                sys_prompt = f"""你是一个伪装成人类科研学者的AI，能够将各种AI生成内容改写成人类学者常用的表达方式，让人类学者无法分辨。请进行第{current_iteration}轮深度改写：
-
-1. 在保持专业水准的前提下，彻底重构表达方式，引入长难句
-2. 确保学术论证的严密性和完整性
-3. 优化专业术语的运用
-4. 提升文本的学术价值
-5. 保持论述的逻辑性和连贯性
-6. 适当使用少量学术表达技巧，提高文本说服力，但避免过度使用比喻和类比
-7. 消除所有明显的AI生成痕迹，使文本更接近真实学者的写作风格"""
-
-            sys_prompt_array = [sys_prompt] * len(text_fragments)
-
-        # 调用LLM一次性处理所有片段
-        response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=inputs_array,
-            inputs_show_user_array=inputs_show_user_array,
-            llm_kwargs=self.llm_kwargs,
-            chatbot=self.chatbot,
-            history_array=history_array,
-            sys_prompt_array=sys_prompt_array,
-        )
-
-        # 处理响应
-        for j, frag in enumerate(text_fragments):
-            try:
-                llm_response = response_collection[j * 2 + 1]
-                processed_text = self._extract_decision(llm_response)
-
-                if processed_text and processed_text.strip():
-                    self.processed_results.append({
-                        'index': frag.fragment_index,
-                        'content': processed_text
-                    })
-                else:
-                    self.failed_fragments.append(frag)
-                    self.processed_results.append({
-                        'index': frag.fragment_index,
-                        'content': frag.content
-                    })
-            except Exception as e:
-                self.failed_fragments.append(frag)
-                self.processed_results.append({
-                    'index': frag.fragment_index,
-                    'content': frag.content
-                })
-
-        # 按原始顺序合并结果
-        self.processed_results.sort(key=lambda x: x['index'])
-        final_content = "\n".join([item['content'] for item in self.processed_results])
-
-        # 更新UI
-        success_count = len(text_fragments) - len(self.failed_fragments)
-        self.chatbot[-1] = ["当前阶段处理完成", f"第 {current_iteration}/{self.reduction_times} 次降重，成功处理 {success_count}/{len(text_fragments)} 个片段"]
-        yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-        return final_content
-
-
-@CatchException
-def 学术降重(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List,
-              history: List, system_prompt: str, user_request: str):
-    """主函数 - 文件到文件处理"""
-    # 初始化
-    # 从高级参数中提取降重次数
-    if "advanced_arg" in plugin_kwargs and plugin_kwargs["advanced_arg"]:
-        # 检查是否包含降重次数的设置
-        match = re.search(r'reduction_times\s*=\s*(\d+)', plugin_kwargs["advanced_arg"])
-        if match:
-            reduction_times = int(match.group(1))
-            # 替换掉高级参数中的reduction_times设置，但保留其他内容
-            plugin_kwargs["advanced_arg"] = re.sub(r'reduction_times\s*=\s*\d+', '', plugin_kwargs["advanced_arg"]).strip()
-            # 添加到plugin_kwargs中作为单独的参数
-            plugin_kwargs["reduction_times"] = reduction_times
-
-    processor = DocumentProcessor(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    chatbot.append(["函数插件功能", f"文件内容处理：将文档内容进行{processor.reduction_times}次降重处理"])
-
-    # 更新用户提示，提供关于降重策略的详细说明
-    if processor.reduction_times == 1:
-        chatbot.append(["降重策略", "将使用单次深度降重，这种方式能更有效地降低AI特征，减少查重率。我们采用特殊优化的提示词，通过一次性强力改写来实现降重效果。"])
-    elif processor.reduction_times > 1:
-        chatbot.append(["降重策略", f"将进行{processor.reduction_times}轮迭代降重，每轮降重都会基于上一轮的结果，并逐渐增加降重强度。请注意，多轮迭代可能会引入新的AI特征，单次强力降重通常效果更好。"])
-
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    # 验证输入路径
-    if not os.path.exists(txt):
-        report_exception(chatbot, history, a=f"解析路径: {txt}", b=f"找不到路径或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 验证路径安全性
-    user_name = chatbot.get_user()
-    validate_path_safety(txt, user_name)
-
-    # 获取文件列表
-    if os.path.isfile(txt):
-        # 单个文件处理
-        file_paths = [txt]
-    else:
-        # 目录处理 - 类似批量文件询问插件
-        project_folder = txt
-        extract_folder = next((d for d in glob.glob(f'{project_folder}/*')
-                           if os.path.isdir(d) and d.endswith('.extract')), project_folder)
-
-        # 排除压缩文件
-        exclude_patterns = r'/[^/]+\.(zip|rar|7z|tar|gz)$'
-        file_paths = [f for f in glob.glob(f'{extract_folder}/**', recursive=True)
-                     if os.path.isfile(f) and not re.search(exclude_patterns, f)]
-
-        # 过滤支持的文件格式
-        file_paths = [f for f in file_paths if any(f.lower().endswith(ext) for ext in
-                    list(processor.paper_extractor.SUPPORTED_EXTENSIONS) + ['.json', '.csv', '.xlsx', '.xls'])]
-
-    if not file_paths:
-        report_exception(chatbot, history, a=f"解析路径: {txt}", b="未找到支持的文件类型")
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    # 处理文件
-    if len(file_paths) > 1:
-        chatbot.append(["发现多个文件", f"共找到 {len(file_paths)} 个文件，将处理第一个文件"])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 只处理第一个文件
-    file_to_process = file_paths[0]
-    processed_content = yield from processor.process_file(file_to_process)
-
-    if processed_content:
-        # 保存结果
-        result_files = processor.save_results(processed_content, file_to_process)
-
-        if result_files:
-            chatbot.append(["处理完成", f"已生成 {len(result_files)} 个结果文件"])
-        else:
-            chatbot.append(["处理完成", "但未能保存任何结果文件"])
-    else:
-        chatbot.append(["处理失败", "未能生成有效的处理结果"])
-
-    yield from update_ui(chatbot=chatbot, history=history)
--- a/crazy_functions/paper_fns/wiki/wikipedia_api.py
+++ b/crazy_functions/paper_fns/wiki/wikipedia_api.py
@@ -1,387 +0,0 @@
-import aiohttp
-import asyncio
-from typing import List, Dict, Optional
-import re
-import random
-import time
-
-class WikipediaAPI:
-    """维基百科API调用实现"""
-    
-    def __init__(self, language: str = "zh", user_agent: str = None, 
-                 max_concurrent: int = 5, request_delay: float = 0.5):
-        """
-        初始化维基百科API客户端
-        
-        Args:
-            language: 语言代码 (zh: 中文, en: 英文, ja: 日文等)
-            user_agent: 用户代理信息，如果为None将使用默认值
-            max_concurrent: 最大并发请求数
-            request_delay: 请求间隔时间(秒)
-        """
-        self.language = language
-        self.base_url = f"https://{language}.wikipedia.org/w/api.php"
-        self.user_agent = user_agent or "WikipediaAPIClient/1.0 (chatscholar@163.com)"
-        self.headers = {
-            "User-Agent": self.user_agent,
-            "Accept": "application/json"
-        }
-        # 添加并发控制
-        self.semaphore = asyncio.Semaphore(max_concurrent)
-        self.request_delay = request_delay
-        self.last_request_time = 0
-    
-    async def _make_request(self, url, params=None):
-        """
-        发起API请求，包含并发控制和请求延迟
-        
-        Args:
-            url: 请求URL
-            params: 请求参数
-            
-        Returns:
-            API响应数据
-        """
-        # 使用信号量控制并发
-        async with self.semaphore:
-            # 添加请求间隔
-            current_time = time.time()
-            time_since_last_request = current_time - self.last_request_time
-            if time_since_last_request < self.request_delay:
-                await asyncio.sleep(self.request_delay - time_since_last_request)
-            
-            # 设置随机延迟，避免规律性请求
-            jitter = random.uniform(0, 0.2)
-            await asyncio.sleep(jitter)
-            
-            # 记录本次请求时间
-            self.last_request_time = time.time()
-            
-            # 发起请求
-            try:
-                async with aiohttp.ClientSession(headers=self.headers) as session:
-                    async with session.get(url, params=params) as response:
-                        if response.status == 429:  # Too Many Requests
-                            retry_after = int(response.headers.get('Retry-After', 5))
-                            print(f"达到请求限制，等待 {retry_after} 秒后重试...")
-                            await asyncio.sleep(retry_after)
-                            return await self._make_request(url, params)
-                        
-                        if response.status != 200:
-                            print(f"API请求失败: HTTP {response.status}")
-                            print(f"响应内容: {await response.text()}")
-                            return None
-                        
-                        return await response.json()
-            except aiohttp.ClientError as e:
-                print(f"请求错误: {str(e)}")
-                return None
-    
-    async def search(self, query: str, limit: int = 10, namespace: int = 0) -> List[Dict]:
-        """
-        搜索维基百科文章
-        
-        Args:
-            query: 搜索关键词
-            limit: 返回结果数量
-            namespace: 命名空间 (0表示文章, 14表示分类等)
-            
-        Returns:
-            搜索结果列表
-        """
-        params = {
-            "action": "query",
-            "list": "search",
-            "srsearch": query,
-            "format": "json",
-            "srlimit": limit,
-            "srnamespace": namespace,
-            "srprop": "snippet|titlesnippet|sectiontitle|categorysnippet|size|wordcount|timestamp|redirecttitle"
-        }
-        
-        data = await self._make_request(self.base_url, params)
-        if not data:
-            return []
-        
-        search_results = data.get("query", {}).get("search", [])
-        return search_results
-    
-    async def get_page_content(self, title: str, section: Optional[int] = None) -> Dict:
-        """
-        获取维基百科页面内容
-        
-        Args:
-            title: 页面标题
-            section: 特定章节编号(可选)
-            
-        Returns:
-            页面内容字典
-        """
-        async with aiohttp.ClientSession(headers=self.headers) as session:
-            params = {
-                "action": "parse",
-                "page": title,
-                "format": "json",
-                "prop": "text|langlinks|categories|links|templates|images|externallinks|sections|revid|displaytitle|iwlinks|properties"
-            }
-            
-            # 如果指定了章节，只获取该章节内容
-            if section is not None:
-                params["section"] = section
-            
-            async with session.get(self.base_url, params=params) as response:
-                if response.status != 200:
-                    print(f"API请求失败: HTTP {response.status}")
-                    return {}
-                
-                data = await response.json()
-                if "error" in data:
-                    print(f"API错误: {data['error'].get('info', '未知错误')}")
-                    return {}
-                
-                return data.get("parse", {})
-    
-    async def get_summary(self, title: str, sentences: int = 3) -> str:
-        """
-        获取页面摘要
-        
-        Args:
-            title: 页面标题
-            sentences: 返回的句子数量
-            
-        Returns:
-            页面摘要文本
-        """
-        async with aiohttp.ClientSession(headers=self.headers) as session:
-            params = {
-                "action": "query",
-                "prop": "extracts",
-                "exintro": "1",
-                "exsentences": sentences,
-                "explaintext": "1",
-                "titles": title,
-                "format": "json"
-            }
-            
-            async with session.get(self.base_url, params=params) as response:
-                if response.status != 200:
-                    print(f"API请求失败: HTTP {response.status}")
-                    return ""
-                
-                data = await response.json()
-                pages = data.get("query", {}).get("pages", {})
-                # 获取第一个页面ID的内容
-                for page_id in pages:
-                    return pages[page_id].get("extract", "")
-                return ""
-    
-    async def get_random_articles(self, count: int = 1, namespace: int = 0) -> List[Dict]:
-        """
-        获取随机文章
-        
-        Args:
-            count: 需要的随机文章数量
-            namespace: 命名空间
-            
-        Returns:
-            随机文章列表
-        """
-        async with aiohttp.ClientSession(headers=self.headers) as session:
-            params = {
-                "action": "query",
-                "list": "random",
-                "rnlimit": count,
-                "rnnamespace": namespace,
-                "format": "json"
-            }
-            
-            async with session.get(self.base_url, params=params) as response:
-                if response.status != 200:
-                    print(f"API请求失败: HTTP {response.status}")
-                    return []
-                
-                data = await response.json()
-                return data.get("query", {}).get("random", [])
-
-    async def login(self, username: str, password: str) -> bool:
-        """
-        使用维基百科账户登录
-        
-        Args:
-            username: 维基百科用户名
-            password: 维基百科密码
-            
-        Returns:
-            登录是否成功
-        """
-        async with aiohttp.ClientSession(headers=self.headers) as session:
-            # 获取登录令牌
-            params = {
-                "action": "query",
-                "meta": "tokens",
-                "type": "login",
-                "format": "json"
-            }
-            
-            async with session.get(self.base_url, params=params) as response:
-                if response.status != 200:
-                    print(f"获取登录令牌失败: HTTP {response.status}")
-                    return False
-                
-                data = await response.json()
-                login_token = data.get("query", {}).get("tokens", {}).get("logintoken")
-                
-                if not login_token:
-                    print("获取登录令牌失败")
-                    return False
-                
-                # 使用令牌登录
-                login_params = {
-                    "action": "login",
-                    "lgname": username,
-                    "lgpassword": password,
-                    "lgtoken": login_token,
-                    "format": "json"
-                }
-                
-                async with session.post(self.base_url, data=login_params) as login_response:
-                    login_data = await login_response.json()
-                    
-                    if login_data.get("login", {}).get("result") == "Success":
-                        print(f"登录成功: {username}")
-                        return True
-                    else:
-                        print(f"登录失败: {login_data.get('login', {}).get('reason', '未知原因')}")
-                        return False
-
-    async def setup_oauth(self, consumer_token: str, consumer_secret: str, 
-                       access_token: str = None, access_secret: str = None) -> bool:
-        """
-        设置OAuth认证
-        
-        Args:
-            consumer_token: 消费者令牌
-            consumer_secret: 消费者密钥
-            access_token: 访问令牌（可选）
-            access_secret: 访问密钥（可选）
-            
-        Returns:
-            设置是否成功
-        """
-        try:
-            # 需要安装 mwoauth 库: pip install mwoauth
-            import mwoauth
-            import requests_oauthlib
-            
-            # 设置OAuth
-            self.consumer_token = consumer_token
-            self.consumer_secret = consumer_secret
-            
-            if access_token and access_secret:
-                # 如果已有访问令牌
-                self.auth = requests_oauthlib.OAuth1(
-                    consumer_token,
-                    consumer_secret,
-                    access_token,
-                    access_secret
-                )
-                print("OAuth设置成功")
-                return True
-            else:
-                # 需要获取访问令牌（这通常需要用户在网页上授权）
-                print("请在开发环境中完成以下OAuth授权流程:")
-                
-                # 创建消费者
-                consumer = mwoauth.Consumer(
-                    consumer_token, consumer_secret
-                )
-                
-                # 初始化握手
-                redirect, request_token = mwoauth.initiate(
-                    f"https://{self.language}.wikipedia.org/w/index.php",
-                    consumer
-                )
-                
-                print(f"请访问此URL授权应用: {redirect}")
-                # 这里通常会提示用户访问URL并输入授权码
-                # 实际应用中需要实现适当的授权流程
-                return False
-        except ImportError:
-            print("请安装 mwoauth 库: pip install mwoauth")
-            return False
-        except Exception as e:
-            print(f"设置OAuth时发生错误: {str(e)}")
-            return False
-
-async def example_usage():
-    """演示WikipediaAPI的使用方法"""
-    # 创建默认中文维基百科API客户端
-    wiki_zh = WikipediaAPI(language="zh")
-    
-    try:
-        # 示例1: 基本搜索
-        print("\n=== 示例1: 搜索维基百科 ===")
-        results = await wiki_zh.search("人工智能", limit=3)
-        
-        for i, result in enumerate(results, 1):
-            print(f"\n--- 结果 {i} ---")
-            print(f"标题: {result.get('title')}")
-            snippet = result.get('snippet', '')
-            # 清理HTML标签
-            snippet = re.sub(r'<.*?>', '', snippet)
-            print(f"摘要: {snippet}")
-            print(f"字数: {result.get('wordcount')}")
-            print(f"大小: {result.get('size')} 字节")
-
-        # 示例2: 获取页面摘要
-        print("\n=== 示例2: 获取页面摘要 ===")
-        summary = await wiki_zh.get_summary("深度学习", sentences=2)
-        print(f"深度学习摘要: {summary}")
-
-        # 示例3: 获取页面内容
-        print("\n=== 示例3: 获取页面内容 ===")
-        content = await wiki_zh.get_page_content("机器学习")
-        if content and "text" in content:
-            text = content["text"].get("*", "")
-            # 移除HTML标签以便控制台显示
-            clean_text = re.sub(r'<.*?>', '', text)
-            print(f"机器学习页面内容片段: {clean_text[:200]}...")
-            
-            # 显示页面包含的分类数量
-            categories = content.get("categories", [])
-            print(f"分类数量: {len(categories)}")
-            
-            # 显示页面包含的链接数量
-            links = content.get("links", [])
-            print(f"链接数量: {len(links)}")
-
-        # 示例4: 获取特定章节内容
-        print("\n=== 示例4: 获取特定章节内容 ===")
-        # 获取引言部分(通常是0号章节)
-        intro_content = await wiki_zh.get_page_content("人工智能", section=0)
-        if intro_content and "text" in intro_content:
-            intro_text = intro_content["text"].get("*", "")
-            clean_intro = re.sub(r'<.*?>', '', intro_text)
-            print(f"人工智能引言内容片段: {clean_intro[:200]}...")
-
-        # 示例5: 获取随机文章
-        print("\n=== 示例5: 获取随机文章 ===")
-        random_articles = await wiki_zh.get_random_articles(count=2)
-        print("随机文章:")
-        for i, article in enumerate(random_articles, 1):
-            print(f"{i}. {article.get('title')}")
-            
-            # 显示随机文章的简短摘要
-            article_summary = await wiki_zh.get_summary(article.get('title'), sentences=1)
-            print(f"   摘要: {article_summary[:100]}...")
-
-    except Exception as e:
-        print(f"发生错误: {str(e)}")
-        import traceback
-        print(traceback.format_exc())
-
-if __name__ == "__main__":
-    import asyncio
-    
-    # 运行示例
-    asyncio.run(example_usage()) 
--- a/crazy_functions/pdf_fns/breakdown_pdf_txt.py
+++ b/crazy_functions/pdf_fns/breakdown_pdf_txt.py
@@ -1,275 +0,0 @@
-from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
-from loguru import logger
-import time
-import re
-
-def force_breakdown(txt, limit, get_token_fn):
-    """ 当无法用标点、空行分割时，我们用最暴力的方法切割
-    """
-    for i in reversed(range(len(txt))):
-        if get_token_fn(txt[:i]) < limit:
-            return txt[:i], txt[i:]
-    return "Tiktoken未知错误", "Tiktoken未知错误"
-
-
-def maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
-    """ 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
-    """
-    _min = int(5e4)
-    _max = int(1e5)
-    # print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
-    if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
-        remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
-        remain_txt_to_cut_storage = ""
-    if len(remain_txt_to_cut) > _max:
-        remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
-        remain_txt_to_cut = remain_txt_to_cut[:_max]
-    return remain_txt_to_cut, remain_txt_to_cut_storage
-
-
-def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
-    """ 文本切分
-    """
-    res = []
-    total_len = len(txt_tocut)
-    fin_len = 0
-    remain_txt_to_cut = txt_tocut
-    remain_txt_to_cut_storage = ""
-    # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-
-    while True:
-        if get_token_fn(remain_txt_to_cut) <= limit:
-            # 如果剩余文本的token数小于限制，那么就不用切了
-            res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
-            break
-        else:
-            # 如果剩余文本的token数大于限制，那么就切
-            lines = remain_txt_to_cut.split('\n')
-
-            # 估计一个切分点
-            estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-
-            # 开始查找合适切分点的偏移（cnt）
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    # 首先尝试用双空行（\n\n）作为切分点
-                    if lines[cnt] != "":
-                        continue
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-
-            if cnt == 0:
-                # 如果没有找到合适的切分点
-                if break_anyway:
-                    # 是否允许暴力切分
-                    prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn)
-                else:
-                    # 不允许直接报错
-                    raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}")
-
-            # 追加列表
-            res.append(prev); fin_len+=len(prev)
-            # 准备下一次迭代
-            remain_txt_to_cut = post
-            remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-            process = fin_len/total_len
-            logger.info(f'正在文本切分 {int(process*100)}%')
-            if len(remain_txt_to_cut.strip()) == 0:
-                break
-    return res
-
-
-def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
-    """ 使用多种方式尝试切分文本，以满足 token 限制
-    """
-    from request_llms.bridge_all import model_info
-    enc = model_info[llm_model]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        return cut(limit, get_token_fn, txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            return cut(limit, get_token_fn, txt, must_break_at_empty_line=False)
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res = cut(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res]
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res = cut(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res]
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下吧
-                    return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
-
-breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
-
-def cut_new(limit, get_token_fn, txt_tocut, must_break_at_empty_line, must_break_at_one_empty_line=False, break_anyway=False):
-    """ 文本切分
-    """
-    res = []
-    res_empty_line = []
-    total_len = len(txt_tocut)
-    fin_len = 0
-    remain_txt_to_cut = txt_tocut
-    remain_txt_to_cut_storage = ""
-    # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-    empty=0
-
-    while True:
-        if get_token_fn(remain_txt_to_cut) <= limit:
-            # 如果剩余文本的token数小于限制，那么就不用切了
-            res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
-            res_empty_line.append(empty)
-            break
-        else:
-            # 如果剩余文本的token数大于限制，那么就切
-            lines = remain_txt_to_cut.split('\n')
-
-            # 估计一个切分点
-            estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-
-            # 开始查找合适切分点的偏移（cnt）
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    # 首先尝试用双空行（\n\n）作为切分点
-                    if lines[cnt] != "":
-                        continue
-                if must_break_at_empty_line or must_break_at_one_empty_line:
-                    empty=1
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit :
-                    break
-                    # empty=0
-                if get_token_fn(prev)>limit:
-                    if '.' not in prev or '。' not in prev:
-                        # empty = 0
-                        break
-
-            # if cnt
-            if cnt == 0:
-                # 如果没有找到合适的切分点
-                if break_anyway:
-                    # 是否允许暴力切分
-                    prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn)
-                    empty =0
-                else:
-                    # 不允许直接报错
-                    raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}")
-
-            # 追加列表
-            res.append(prev); fin_len+=len(prev)
-            res_empty_line.append(empty)
-            # 准备下一次迭代
-            remain_txt_to_cut = post
-            remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-            process = fin_len/total_len
-            logger.info(f'正在文本切分 {int(process*100)}%')
-            if len(remain_txt_to_cut.strip()) == 0:
-                break
-    return res,res_empty_line
-
-
-def breakdown_text_to_satisfy_token_limit_new_(txt, limit, llm_model="gpt-3.5-turbo"):
-    """ 使用多种方式尝试切分文本，以满足 token 限制
-    """
-    from request_llms.bridge_all import model_info
-    enc = model_info[llm_model]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        res, empty_line =cut_new(limit, get_token_fn, txt, must_break_at_empty_line=True)
-        return res,empty_line
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            res, _ = cut_new(limit, get_token_fn, txt, must_break_at_empty_line=False,must_break_at_one_empty_line=True)
-            return res, _
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res, _ = cut_new(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res],_
-
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res,_ = cut_new(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res], _
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下吧
-                    res, _ = cut_new(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
-                    return res,_
-breakdown_text_to_satisfy_token_limit_new = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_new_, timeout=60)
-
-def cut_from_end_to_satisfy_token_limit_(txt, limit, reserve_token=500, llm_model="gpt-3.5-turbo"):
-    """从后往前裁剪文本，以论文为单位进行裁剪
-
-    参数：
-        txt: 要处理的文本（格式化后的论文列表字符串）
-        limit: token数量上限
-        reserve_token: 需要预留的token数量，默认500
-        llm_model: 使用的模型名称
-    返回：
-        裁剪后的文本
-    """
-    from request_llms.bridge_all import model_info
-    enc = model_info[llm_model]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-
-    # 计算当前文本的token数
-    current_tokens = get_token_fn(txt)
-    target_limit = limit - reserve_token
-
-    # 如果当前token数已经在限制范围内，直接返回
-    if current_tokens <= target_limit:
-        return txt
-
-    # 按论文编号分割文本
-    papers = re.split(r'\n(?=\d+\. \*\*)', txt)
-    if not papers:
-        return txt
-
-    # 从前往后累加论文，直到达到token限制
-    result = papers[0]  # 保留第一篇
-    current_tokens = get_token_fn(result)
-
-    for paper in papers[1:]:
-        paper_tokens = get_token_fn(paper)
-        if current_tokens + paper_tokens <= target_limit:
-            result += "\n" + paper
-            current_tokens += paper_tokens
-        else:
-            break
-
-    return result
-
-# 添加超时保护
-cut_from_end_to_satisfy_token_limit = run_in_subprocess_with_timeout(cut_from_end_to_satisfy_token_limit_, timeout=20)
-
-if __name__ == '__main__':
-    from crazy_functions.crazy_utils import read_and_clean_pdf_text
-    file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
-
-    from request_llms.bridge_all import model_info
-    for i in range(5):
-        file_content += file_content
-
-    logger.info(len(file_content))
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-    res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
-
--- a/crazy_functions/pdf_fns/parse_pdf.py
+++ b/crazy_functions/pdf_fns/parse_pdf.py
@@ -113,7 +113,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
            return [txt]
        else:
            # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
-            # find a smooth token limit to achieve even separation
+            # find a smooth token limit to achieve even seperation
            count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
            token_limit_smooth = raw_token_num // count + count
            return breakdown_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
--- a/crazy_functions/pdf_fns/parse_pdf_grobid.py
+++ b/crazy_functions/pdf_fns/parse_pdf_grobid.py
@@ -1,6 +1,6 @@
 import os
 from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
-from toolbox import update_ui, promote_file_to_downloadzone, update_ui_latest_msg, disable_auto_promotion
+from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
 from toolbox import write_history_to_file, promote_file_to_downloadzone, get_conf, extract_archive
 from crazy_functions.pdf_fns.parse_pdf import parse_pdf, translate_pdf

--- a/crazy_functions/pdf_fns/parse_pdf_via_doc2x.py
+++ b/crazy_functions/pdf_fns/parse_pdf_via_doc2x.py
@@ -4,225 +4,123 @@ from toolbox import promote_file_to_downloadzone, extract_archive
 from toolbox import generate_file_link, zip_folder
 from crazy_functions.crazy_utils import get_files_from_everything
 from shared_utils.colorful import *
-from loguru import logger
 import os
-import requests
-import time

-
-def retry_request(max_retries=3, delay=3):
-    """
-    Decorator for retrying HTTP requests
-    Args:
-        max_retries: Maximum number of retry attempts
-        delay: Delay between retries in seconds
-    """
-
-    def decorator(func):
-        def wrapper(*args, **kwargs):
-            for attempt in range(max_retries):
-                try:
-                    return func(*args, **kwargs)
-                except Exception as e:
-                    if attempt < max_retries - 1:
-                        logger.error(
-                            f"Request failed, retrying... ({attempt + 1}/{max_retries}) Error: {e}"
+def refresh_key(doc2x_api_key):
+    import requests, json
+    url = "https://api.doc2x.noedgeai.com/api/token/refresh"
+    res = requests.post(
+        url,
+        headers={"Authorization": "Bearer " + doc2x_api_key}
    )
-                        time.sleep(delay)
-                        continue
-                    raise e
-            return None
-
-        return wrapper
-
-    return decorator
-
-
-@retry_request()
-def make_request(method, url, **kwargs):
-    """
-    Make HTTP request with retry mechanism
-    """
-    return requests.request(method, url, **kwargs)
-
-
-def doc2x_api_response_status(response, uid=""):
-    """
-    Check the status of Doc2x API response
-    Args:
-        response_data: Response object from Doc2x API
-    """
-    response_json = response.json()
-    response_data = response_json.get("data", {})
-    code = response_json.get("code", "Unknown")
-    meg = response_data.get("message", response_json)
-    trace_id = response.headers.get("trace-id", "Failed to get trace-id")
-    if response.status_code != 200:
-        raise RuntimeError(
-            f"Doc2x return an error:\nTrace ID: {trace_id} {uid}\n{response.status_code} - {response_json}"
-        )
-    if code in ["parse_page_limit_exceeded", "parse_concurrency_limit"]:
-        raise RuntimeError(
-            f"Reached the limit of Doc2x:\nTrace ID: {trace_id} {uid}\n{code} - {meg}"
-        )
-    if code not in ["ok", "success"]:
-        raise RuntimeError(
-            f"Doc2x return an error:\nTrace ID: {trace_id} {uid}\n{code} - {meg}"
-        )
-    return response_data
-
+    res_json = []
+    if res.status_code == 200:
+        decoded = res.content.decode("utf-8")
+        res_json = json.loads(decoded)
+        doc2x_api_key = res_json['data']['token']
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+    return doc2x_api_key

 def 解析PDF_DOC2X_转Latex(pdf_file_path):
-    zip_file_path, unzipped_folder = 解析PDF_DOC2X(pdf_file_path, format="tex")
-    return unzipped_folder
-
-
-def 解析PDF_DOC2X(pdf_file_path, format="tex"):
-    """
-    format: 'tex', 'md', 'docx'
-    """
-
-    DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
+    import requests, json, os
+    DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
    latex_dir = get_log_folder(plugin_name="pdf_ocr_latex")
+    doc2x_api_key = DOC2X_API_KEY
+    if doc2x_api_key.startswith('sk-'):
+        url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
+    else:
+        doc2x_api_key = refresh_key(doc2x_api_key)
+        url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
+
+    res = requests.post(
+        url,
+        files={"file": open(pdf_file_path, "rb")},
+        data={"ocr": "1"},
+        headers={"Authorization": "Bearer " + doc2x_api_key}
+    )
+    res_json = []
+    if res.status_code == 200:
+        decoded = res.content.decode("utf-8")
+        for z_decoded in decoded.split('\n'):
+            if len(z_decoded) == 0: continue
+            assert z_decoded.startswith("data: ")
+            z_decoded = z_decoded[len("data: "):]
+            decoded_json = json.loads(z_decoded)
+            res_json.append(decoded_json)
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+
+    uuid = res_json[0]['uuid']
+    to = "latex" # latex, md, docx
+    url = "https://api.doc2x.noedgeai.com/api/export"+"?request_id="+uuid+"&to="+to
+
+    res = requests.get(url, headers={"Authorization": "Bearer " + doc2x_api_key})
+    latex_zip_path = os.path.join(latex_dir, gen_time_str() + '.zip')
+    latex_unzip_path = os.path.join(latex_dir, gen_time_str())
+    if res.status_code == 200:
+        with open(latex_zip_path, "wb") as f: f.write(res.content)
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+
+    import zipfile
+    with zipfile.ZipFile(latex_zip_path, 'r') as zip_ref:
+        zip_ref.extractall(latex_unzip_path)
+
+
+    return latex_unzip_path
+
+
+
+
+def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request):
+
+
+    def pdf2markdown(filepath):
+        import requests, json, os
        markdown_dir = get_log_folder(plugin_name="pdf_ocr")
        doc2x_api_key = DOC2X_API_KEY
-
-    # < ------ 第1步：预上传获取URL，然后上传文件 ------ >
-    logger.info("Doc2x 上传文件：预上传获取URL")
-    res = make_request(
-        "POST",
-        "https://v2.doc2x.noedgeai.com/api/v2/parse/preupload",
-        headers={"Authorization": "Bearer " + doc2x_api_key},
-        timeout=15,
-    )
-    res_data = doc2x_api_response_status(res)
-    upload_url = res_data["url"]
-    uuid = res_data["uid"]
-
-    logger.info("Doc2x 上传文件：上传文件")
-    with open(pdf_file_path, "rb") as file:
-        res = make_request("PUT", upload_url, data=file, timeout=60)
-    res.raise_for_status()
-
-    # < ------ 第2步：轮询等待 ------ >
-    logger.info("Doc2x 处理文件中：轮询等待")
-    params = {"uid": uuid}
-    max_attempts = 60
-    attempt = 0
-    while attempt < max_attempts:
-        res = make_request(
-            "GET",
-            "https://v2.doc2x.noedgeai.com/api/v2/parse/status",
-            headers={"Authorization": "Bearer " + doc2x_api_key},
-            params=params,
-            timeout=15,
-        )
-        res_data = doc2x_api_response_status(res)
-        if res_data["status"] == "success":
-            break
-        elif res_data["status"] == "processing":
-            time.sleep(5)
-            logger.info(f"Doc2x is processing at {res_data['progress']}%")
-            attempt += 1
+        if doc2x_api_key.startswith('sk-'):
+            url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
        else:
-            raise RuntimeError(f"Doc2x return an error: {res_data}")
-    if attempt >= max_attempts:
-        raise RuntimeError("Doc2x processing timeout after maximum attempts")
+            doc2x_api_key = refresh_key(doc2x_api_key)
+            url = "https://api.doc2x.noedgeai.com/api/platform/pdf"

-    # < ------ 第3步：提交转化 ------ >
-    logger.info("Doc2x 第3步：提交转化")
-    data = {
-        "uid": uuid,
-        "to": format,
-        "formula_mode": "dollar",
-        "filename": "output"
-    }
-    res = make_request(
-        "POST",
-        "https://v2.doc2x.noedgeai.com/api/v2/convert/parse",
-        headers={"Authorization": "Bearer " + doc2x_api_key},
-        json=data,
-        timeout=15,
-    )
-    doc2x_api_response_status(res, uid=f"uid: {uuid}")
-
-    # < ------ 第4步：等待结果 ------ >
-    logger.info("Doc2x 第4步：等待结果")
-    params = {"uid": uuid}
-    max_attempts = 36
-    attempt = 0
-    while attempt < max_attempts:
-        res = make_request(
-            "GET",
-            "https://v2.doc2x.noedgeai.com/api/v2/convert/parse/result",
-            headers={"Authorization": "Bearer " + doc2x_api_key},
-            params=params,
-            timeout=15,
-        )
-        res_data = doc2x_api_response_status(res, uid=f"uid: {uuid}")
-        if res_data["status"] == "success":
-            break
-        elif res_data["status"] == "processing":
-            time.sleep(3)
-            logger.info("Doc2x still processing to convert file")
-            attempt += 1
-    if attempt >= max_attempts:
-        raise RuntimeError("Doc2x conversion timeout after maximum attempts")
-
-    # < ------ 第5步：最后的处理 ------ >
-    logger.info("Doc2x 第5步：下载转换后的文件")
-
-    if format == "tex":
-        target_path = latex_dir
-    if format == "md":
-        target_path = markdown_dir
-    os.makedirs(target_path, exist_ok=True)
-
-    max_attempt = 3
-    # < ------ 下载 ------ >
-    for attempt in range(max_attempt):
-        try:
-            result_url = res_data["url"]
-            res = make_request("GET", result_url, timeout=60)
-            zip_path = os.path.join(target_path, gen_time_str() + ".zip")
-            unzip_path = os.path.join(target_path, gen_time_str())
-            if res.status_code == 200:
-                with open(zip_path, "wb") as f:
-                    f.write(res.content)
-            else:
-                raise RuntimeError(f"Doc2x return an error: {res.json()}")
-        except Exception as e:
-            if attempt < max_attempt - 1:
-                logger.error(f"Failed to download uid = {uuid} file, retrying... {e}")
-                time.sleep(3)
-                continue
-            else:
-                raise e
-
-    # < ------ 解压 ------ >
-    import zipfile
-    with zipfile.ZipFile(zip_path, "r") as zip_ref:
-        zip_ref.extractall(unzip_path)
-    return zip_path, unzip_path
-
-
-def 解析PDF_DOC2X_单文件(
-    fp,
-    project_folder,
-    llm_kwargs,
-    plugin_kwargs,
-    chatbot,
-    history,
-    system_prompt,
-    DOC2X_API_KEY,
-    user_request,
-):
-    def pdf2markdown(filepath):
-        chatbot.append((None, f"Doc2x 解析中"))
+        chatbot.append((None, "加载PDF文件，发送至DOC2X解析..."))
        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

-        md_zip_path, unzipped_folder = 解析PDF_DOC2X(filepath, format="md")
+        res = requests.post(
+            url,
+            files={"file": open(filepath, "rb")},
+            data={"ocr": "1"},
+            headers={"Authorization": "Bearer " + doc2x_api_key}
+        )
+        res_json = []
+        if res.status_code == 200:
+            decoded = res.content.decode("utf-8")
+            for z_decoded in decoded.split('\n'):
+                if len(z_decoded) == 0: continue
+                assert z_decoded.startswith("data: ")
+                z_decoded = z_decoded[len("data: "):]
+                decoded_json = json.loads(z_decoded)
+                res_json.append(decoded_json)
+            if 'limit exceeded' in decoded_json.get('status', ''):
+                raise RuntimeError("Doc2x API 页数受限，请联系 Doc2x 方面，并更换新的 API 秘钥。")
+        else:
+            raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+        uuid = res_json[0]['uuid']
+        to = "md" # latex, md, docx
+        url = "https://api.doc2x.noedgeai.com/api/export"+"?request_id="+uuid+"&to="+to

+        chatbot.append((None, f"读取解析: {url} ..."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        res = requests.get(url, headers={"Authorization": "Bearer " + doc2x_api_key})
+        md_zip_path = os.path.join(markdown_dir, gen_time_str() + '.zip')
+        if res.status_code == 200:
+            with open(md_zip_path, "wb") as f: f.write(res.content)
+        else:
+            raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
        promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
        chatbot.append((None, f"完成解析 {md_zip_path} ..."))
        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@@ -239,97 +137,77 @@ def 解析PDF_DOC2X_单文件(
        os.makedirs(target_path_base, exist_ok=True)
        shutil.copyfile(md_zip_path, this_file_path)
        ex_folder = this_file_path + ".extract"
-        extract_archive(file_path=this_file_path, dest_dir=ex_folder)
+        extract_archive(
+            file_path=this_file_path, dest_dir=ex_folder
+        )

        # edit markdown files
-        success, file_manifest, project_folder = get_files_from_everything(
-            ex_folder, type=".md"
-        )
+        success, file_manifest, project_folder = get_files_from_everything(ex_folder, type='.md')
        for generated_fp in file_manifest:
            # 修正一些公式问题
-            with open(generated_fp, "r", encoding="utf8") as f:
+            with open(generated_fp, 'r', encoding='utf8') as f:
                content = f.read()
            # 将公式中的\[ \]替换成$$
-            content = content.replace(r"\[", r"$$").replace(r"\]", r"$$")
+            content = content.replace(r'\[', r'$$').replace(r'\]', r'$$')
            # 将公式中的\( \)替换成$
-            content = content.replace(r"\(", r"$").replace(r"\)", r"$")
-            content = content.replace("```markdown", "\n").replace("```", "\n")
-            with open(generated_fp, "w", encoding="utf8") as f:
+            content = content.replace(r'\(', r'$').replace(r'\)', r'$')
+            content = content.replace('```markdown', '\n').replace('```', '\n')
+            with open(generated_fp, 'w', encoding='utf8') as f:
                f.write(content)
            promote_file_to_downloadzone(generated_fp, chatbot=chatbot)
            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

            # 生成在线预览html
-            file_name = "在线预览翻译（原文）" + gen_time_str() + ".html"
+            file_name = '在线预览翻译（原文）' + gen_time_str() + '.html'
            preview_fp = os.path.join(ex_folder, file_name)
-            from shared_utils.advanced_markdown_format import (
-                markdown_convertion_for_file,
-            )
-
+            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
            with open(generated_fp, "r", encoding="utf-8") as f:
                md = f.read()
            #     # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
            #     md = re.sub(r'^<table>', r'.<table>', md, flags=re.MULTILINE)
            html = markdown_convertion_for_file(md)
-            with open(preview_fp, "w", encoding="utf-8") as f:
-                f.write(html)
+            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
            chatbot.append([None, f"生成在线预览：{generate_file_link([preview_fp])}"])
            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)

-        chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
-        plugin_kwargs["markdown_expected_output_dir"] = ex_folder

-        translated_f_name = "translated_markdown.md"
-        generated_fp = plugin_kwargs["markdown_expected_output_path"] = os.path.join(
-            ex_folder, translated_f_name
-        )
+
+        chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
+        plugin_kwargs['markdown_expected_output_dir'] = ex_folder
+
+        translated_f_name = 'translated_markdown.md'
+        generated_fp = plugin_kwargs['markdown_expected_output_path'] = os.path.join(ex_folder, translated_f_name)
        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        yield from Markdown英译中(
-            ex_folder,
-            llm_kwargs,
-            plugin_kwargs,
-            chatbot,
-            history,
-            system_prompt,
-            user_request,
-        )
+        yield from Markdown英译中(ex_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
        if os.path.exists(generated_fp):
            # 修正一些公式问题
-            with open(generated_fp, "r", encoding="utf8") as f:
-                content = f.read()
-            content = content.replace("```markdown", "\n").replace("```", "\n")
+            with open(generated_fp, 'r', encoding='utf8') as f: content = f.read()
+            content = content.replace('```markdown', '\n').replace('```', '\n')
            # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
            # content = re.sub(r'^<table>', r'.<table>', content, flags=re.MULTILINE)
-            with open(generated_fp, "w", encoding="utf8") as f:
-                f.write(content)
+            with open(generated_fp, 'w', encoding='utf8') as f: f.write(content)
            # 生成在线预览html
-            file_name = "在线预览翻译" + gen_time_str() + ".html"
+            file_name = '在线预览翻译' + gen_time_str() + '.html'
            preview_fp = os.path.join(ex_folder, file_name)
-            from shared_utils.advanced_markdown_format import (
-                markdown_convertion_for_file,
-            )
-
+            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
            with open(generated_fp, "r", encoding="utf-8") as f:
                md = f.read()
            html = markdown_convertion_for_file(md)
-            with open(preview_fp, "w", encoding="utf-8") as f:
-                f.write(html)
+            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
            # 生成包含图片的压缩包
            dest_folder = get_log_folder(chatbot.get_user())
-            zip_name = "翻译后的带图文档.zip"
-            zip_folder(
-                source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name
-            )
+            zip_name = '翻译后的带图文档.zip'
+            zip_folder(source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name)
            zip_fp = os.path.join(dest_folder, zip_name)
            promote_file_to_downloadzone(zip_fp, chatbot=chatbot)
            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
    md_zip_path = yield from pdf2markdown(fp)
    yield from deliver_to_markdown_plugin(md_zip_path, user_request)

-
 def 解析PDF_基于DOC2X(file_manifest, *args):
    for index, fp in enumerate(file_manifest):
        yield from 解析PDF_DOC2X_单文件(fp, *args)
    return
+
+
--- a/crazy_functions/pdf_fns/parse_word.py
+++ b/crazy_functions/pdf_fns/parse_word.py
@@ -14,17 +14,17 @@ def extract_text_from_files(txt, chatbot, history):
        final_result(list):文本内容
        page_one(list):第一页内容/摘要
        file_manifest(list):文件路径
-        exception(string):需要用户手动处理的信息,如没出错则保持为空
+        excption(string):需要用户手动处理的信息,如没出错则保持为空
    """

    final_result = []
    page_one = []
    file_manifest = []
-    exception = ""
+    excption = ""

    if txt == "":
        final_result.append(txt)
-        return False, final_result, page_one, file_manifest, exception   #如输入区内容不是文件则直接返回输入区内容
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容

    #查找输入区内容中的文件
    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
@@ -33,20 +33,20 @@ def extract_text_from_files(txt, chatbot, history):
    file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc')

    if file_doc:
-        exception = "word"
-        return False, final_result, page_one, file_manifest, exception
+        excption = "word"
+        return False, final_result, page_one, file_manifest, excption

    file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
    if file_num == 0:
        final_result.append(txt)
-        return False, final_result, page_one, file_manifest, exception   #如输入区内容不是文件则直接返回输入区内容
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容

    if file_pdf:
        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
            import fitz
        except:
-            exception = "pdf"
-            return False, final_result, page_one, file_manifest, exception
+            excption = "pdf"
+            return False, final_result, page_one, file_manifest, excption
        for index, fp in enumerate(pdf_manifest):
            file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
@@ -72,8 +72,8 @@ def extract_text_from_files(txt, chatbot, history):
        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
            from docx import Document
        except:
-            exception = "word_pip"
-            return False, final_result, page_one, file_manifest, exception
+            excption = "word_pip"
+            return False, final_result, page_one, file_manifest, excption
        for index, fp in enumerate(word_manifest):
            doc = Document(fp)
            file_content = '\n'.join([p.text for p in doc.paragraphs])
@@ -82,4 +82,4 @@ def extract_text_from_files(txt, chatbot, history):
            final_result.append(file_content)
            file_manifest.append(os.path.relpath(fp, folder_word))

-    return True, final_result, page_one, file_manifest, exception
+    return True, final_result, page_one, file_manifest, excption
--- a/crazy_functions/rag_fns/llama_index_worker.py
+++ b/crazy_functions/rag_fns/llama_index_worker.py
@@ -1,13 +1,17 @@
+import llama_index
+import os
 import atexit
 from loguru import logger
 from typing import List
-
 from llama_index.core import Document
-from llama_index.core.ingestion import run_transformations
 from llama_index.core.schema import TextNode
-
-from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
 from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
+from shared_utils.connect_void_terminal import get_chat_default_kwargs
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
+from llama_index.core.ingestion import run_transformations
+from llama_index.core import PromptTemplate
+from llama_index.core.response_synthesizers import TreeSummarize

 DEFAULT_QUERY_GENERATION_PROMPT = """\
 Now, you have context information as below:
@@ -59,7 +63,7 @@ class SaveLoad():
    def purge(self):
        import shutil
        shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
-        self.vs_index = self.create_new_vs(self.checkpoint_dir)
+        self.vs_index = self.create_new_vs()


 class LlamaIndexRagWorker(SaveLoad):
@@ -71,7 +75,7 @@ class LlamaIndexRagWorker(SaveLoad):
        if auto_load_checkpoint:
            self.vs_index = self.load_from_checkpoint(checkpoint_dir)
        else:
-            self.vs_index = self.create_new_vs()
+            self.vs_index = self.create_new_vs(checkpoint_dir)
        atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))

    def assign_embedding_model(self):
@@ -87,21 +91,17 @@ class LlamaIndexRagWorker(SaveLoad):
        logger.info('oo --------inspect_vector_store end--------')
        return vector_store_preview

-    def add_documents_to_vector_store(self, document_list: List[Document]):
-        """
-        Adds a list of Document objects to the vector store after processing.
-        """
-        documents = document_list
+    def add_documents_to_vector_store(self, document_list):
+        documents = [Document(text=t) for t in document_list]
        documents_nodes = run_transformations(
                        documents,  # type: ignore
                        self.vs_index._transformations,
                        show_progress=True
                    )
        self.vs_index.insert_nodes(documents_nodes)
-        if self.debug_mode:
-            self.inspect_vector_store()
+        if self.debug_mode: self.inspect_vector_store()

-    def add_text_to_vector_store(self, text: str):
+    def add_text_to_vector_store(self, text):
        node = TextNode(text=text)
        documents_nodes = run_transformations(
                        [node],
@@ -109,16 +109,14 @@ class LlamaIndexRagWorker(SaveLoad):
                        show_progress=True
                    )
        self.vs_index.insert_nodes(documents_nodes)
-        if self.debug_mode:
-            self.inspect_vector_store()
+        if self.debug_mode: self.inspect_vector_store()

    def remember_qa(self, question, answer):
        formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
        self.add_text_to_vector_store(formatted_str)

    def retrieve_from_store_with_query(self, query):
-        if self.debug_mode:
-            self.inspect_vector_store()
+        if self.debug_mode: self.inspect_vector_store()
        retriever = self.vs_index.as_retriever()
        return retriever.retrieve(query)

@@ -130,9 +128,3 @@ class LlamaIndexRagWorker(SaveLoad):
        buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
        if self.debug_mode: logger.info(buf)
        return buf
-
-    def purge_vector_store(self):
-        """
-        Purges the current vector store and creates a new one.
-        """
-        self.purge()
--- a/crazy_functions/rag_fns/rag_file_support.py
+++ b/crazy_functions/rag_fns/rag_file_support.py
@@ -1,48 +0,0 @@
-import subprocess
-import os
-
-supports_format = ['.csv', '.docx', '.epub', '.ipynb',  '.mbox', '.md', '.pdf',  '.txt', '.ppt', '.pptm', '.pptx', '.bat']
-
-def convert_to_markdown(file_path: str) -> str:
-    """
-    将支持的文件格式转换为Markdown格式
-    Args:
-        file_path: 输入文件路径
-    Returns:
-        str: 转换后的Markdown文件路径，如果转换失败则返回原始文件路径
-    """
-    _, ext = os.path.splitext(file_path.lower())
-
-    if ext in ['.docx', '.doc', '.pptx', '.ppt', '.pptm', '.xls', '.xlsx', '.csv', 'pdf']:
-        try:
-            # 创建输出Markdown文件路径
-            md_path = os.path.splitext(file_path)[0] + '.md'
-            # 使用markitdown工具将文件转换为Markdown
-            command = f"markitdown {file_path} > {md_path}"
-            subprocess.run(command, shell=True, check=True)
-            print(f"已将{ext}文件转换为Markdown: {md_path}")
-            return md_path
-        except Exception as e:
-            print(f"{ext}转Markdown失败: {str(e)}，将继续处理原文件")
-            return file_path
-
-    return file_path
-
-# 修改后的 extract_text 函数，结合 SimpleDirectoryReader 和自定义解析逻辑
-def extract_text(file_path):
-    from llama_index.core import SimpleDirectoryReader
-    _, ext = os.path.splitext(file_path.lower())
-
-    # 使用 SimpleDirectoryReader 处理它支持的文件格式
-    if ext in supports_format:
-        try:
-            reader = SimpleDirectoryReader(input_files=[file_path])
-            print(f"Extracting text from {file_path} using SimpleDirectoryReader")
-            documents = reader.load_data()
-            print(f"Complete: Extracting text from {file_path} using SimpleDirectoryReader")
-            buffer = [ doc.text for doc in documents ]
-            return '\n'.join(buffer)
-        except Exception as e:
-            pass
-    else:
-        return '格式不支持'
--- a/crazy_functions/review_fns/init.py
+++ b/crazy_functions/review_fns/init.py
--- a/crazy_functions/review_fns/conversation_doc/endnote_doc.py
+++ b/crazy_functions/review_fns/conversation_doc/endnote_doc.py
@@ -1,68 +0,0 @@
-from typing import List
-from crazy_functions.review_fns.data_sources.base_source import PaperMetadata
-
-class EndNoteFormatter:
-    """EndNote参考文献格式生成器"""
-
-    def __init__(self):
-        pass
-
-    def create_document(self, papers: List[PaperMetadata]) -> str:
-        """生成EndNote格式的参考文献文本
-
-        Args:
-            papers: 论文列表
-
-        Returns:
-            str: EndNote格式的参考文献文本
-        """
-        endnote_text = ""
-
-        for paper in papers:
-            # 开始一个新条目
-            endnote_text += "%0 Journal Article\n"  # 默认类型为期刊文章
-
-            # 根据venue_type调整条目类型
-            if hasattr(paper, 'venue_type') and paper.venue_type:
-                if paper.venue_type.lower() == 'conference':
-                    endnote_text = endnote_text.replace("Journal Article", "Conference Paper")
-                elif paper.venue_type.lower() == 'preprint':
-                    endnote_text = endnote_text.replace("Journal Article", "Electronic Article")
-
-            # 添加标题
-            endnote_text += f"%T {paper.title}\n"
-
-            # 添加作者
-            for author in paper.authors:
-                endnote_text += f"%A {author}\n"
-
-            # 添加年份
-            if paper.year:
-                endnote_text += f"%D {paper.year}\n"
-
-            # 添加期刊/会议名称
-            if hasattr(paper, 'venue_name') and paper.venue_name:
-                endnote_text += f"%J {paper.venue_name}\n"
-            elif paper.venue:
-                endnote_text += f"%J {paper.venue}\n"
-
-            # 添加DOI
-            if paper.doi:
-                endnote_text += f"%R {paper.doi}\n"
-                endnote_text += f"%U https://doi.org/{paper.doi}\n"
-            elif paper.url:
-                endnote_text += f"%U {paper.url}\n"
-
-            # 添加摘要
-            if paper.abstract:
-                endnote_text += f"%X {paper.abstract}\n"
-
-            # 添加机构
-            if hasattr(paper, 'institutions'):
-                for institution in paper.institutions:
-                    endnote_text += f"%I {institution}\n"
-
-            # 条目之间添加空行
-            endnote_text += "\n"
-
-        return endnote_text
--- a/Show More
+++ b/Show More