Merge branch 'master' of https://github.com/binary-husky/gpt_academic

* typo: Fix typos and rename functions across multiple files

This commit addresses several minor issues:
- Corrected spelling of function names (e.g., `update_ui_lastest_msg` to `update_ui_latest_msg`)
- Fixed typos in comments and variable names
- Corrected capitalization in some strings (e.g., "ArXiv" instead of "Arixv")
- Renamed some variables for consistency
- Corrected some console-related parameter names (e.g., `console_slience` to `console_silence`)

The changes span multiple files across the project, including request LLM bridges, crazy functions, and utility modules.

* fix: f-string expression part cannot include a backslash (#2139)

* raise error when the uploaded tar contain hard/soft link (#2136)

* minor bug fix

* fine tune reasoning css

* upgrade internet gpt plugin

* Update README.md

* fix GHSA-gqp5-wm97-qxcv

* typo fix

* update readme

---------

Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>
Co-authored-by: binary-husky <qingxu.fu@outlook.com>

.github/workflows/conda-pack-windows.yml

@@ -0,0 +1,56 @@
+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

.github/workflows/stale.yml

@@ -7,7 +7,7 @@
 name: 'Close stale issues and PRs'
 on:
   schedule:
-    - cron: '*/5 * * * *'
+    - cron: '*/30 * * * *'
 
 jobs:
   stale:
@@ -19,7 +19,6 @@ 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 1 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 7 days.'
           days-before-stale: 100
-          days-before-close: 1
-          debug-only: true
+          days-before-close: 7

.gitignore

@@ -161,3 +161,5 @@ temp.*
 objdump*
 *.min.*.js
 TODO
+experimental_mods
+search_results

Dockerfile

@@ -15,6 +15,7 @@ RUN echo '[global]' > /etc/pip.conf && \
 # 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）
@@ -33,6 +34,7 @@ RUN pip3 install -r requirements.txt
 
 # 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()'
+RUN python3 -m pip cache purge
 
 
 # 启动（必要）

README.md

@@ -1,9 +1,14 @@
 > [!IMPORTANT]
-> 2024.10.10: 突发停电，紧急恢复了提供[whl包](https://drive.google.com/file/d/19U_hsLoMrjOlQSzYS3pzWX9fTzyusArP/view?usp=sharing)的文件服务器  
-> 2024.10.8: 版本3.90加入对llama-index的初步支持，版本3.80加入插件二级菜单功能（详见wiki）  
+> `master主分支`最新动态(2025.3.2): 修复大量代码typo / 联网组件支持Jina的api / 增加deepseek-r1支持  
+> `frontier开发分支`最新动态(2024.12.9): 更新对话时间线功能，优化xelatex论文翻译  
+> `wiki文档`最新动态(2024.12.5): 更新ollama接入指南  
+>
+> 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  
 > 2024.5.1: 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`。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。
+> 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`。
 
 <br>
 
@@ -124,20 +129,20 @@ 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)
@@ -170,26 +175,32 @@ flowchart TD
     ```
 
 
-<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>
+<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>
 <p>
 
-【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）：
+【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）：
 
 ```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】支持复旦MOSS
+# 【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
 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III】支持RWKV Runner
+# 【可选步骤IV】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)：
+# 【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)：
 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）
+# 【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）
 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
@@ -417,7 +428,6 @@ 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
 

config.py

@@ -7,11 +7,16 @@
     Configuration reading priority: environment variable > config_private.py > config.py
 """
 
-# [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 1-1]>> ( 接入GPT等模型 ) 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 = "在此处填写APIKEY"    # 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]>> ( 接入通义 qwen-max ) 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
 
-# [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
+# [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 = ""
+
+# [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:
     """
@@ -32,11 +37,13 @@ else:
 
 # [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-3.5-turbo-16k" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
+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"
+                    "gemini-1.5-pro", "chatglm3", "chatglm4",
+                    "deepseek-chat", "deepseek-coder", "deepseek-reasoner"
                     ]
 
 EMBEDDING_MODEL = "text-embedding-3-small"
@@ -47,7 +54,7 @@ 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-max", "qwen-local",
+#   "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",
@@ -55,6 +62,7 @@ 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，
@@ -73,7 +81,7 @@ 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 = 3
+DEFAULT_WORKER_NUM = 8
 
 
 # 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
@@ -81,6 +89,31 @@ DEFAULT_WORKER_NUM = 3
 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."
@@ -132,16 +165,15 @@ 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"
 
@@ -235,13 +267,11 @@ MOONSHOT_API_KEY = ""
 YIMODEL_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 = ""
@@ -273,8 +303,8 @@ GROBID_URLS = [
 ]
 
 
-# Searxng互联网检索服务
-SEARXNG_URL = "https://cloud-1.agent-matrix.com/"
+# 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
@@ -298,7 +328,7 @@ 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 = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
+WHEN_TO_USE_PROXY = ["Connect_OpenAI", "Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
                      "Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]
 
 
@@ -310,6 +340,12 @@ 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 = ""
 
 """
 --------------- 配置关联关系说明 ---------------
@@ -369,6 +405,7 @@ NUM_CUSTOM_BASIC_BTN = 4
 
 本地大模型示意图
 │
+├── "chatglm4"
 ├── "chatglm3"
 ├── "chatglm"
 ├── "chatglm_onnx"
@@ -399,7 +436,7 @@ NUM_CUSTOM_BASIC_BTN = 4
 插件在线服务配置依赖关系示意图
 │
 ├── 互联网检索
-│   └── SEARXNG_URL
+│   └── SEARXNG_URLS
 │
 ├── 语音功能
 │   ├── ENABLE_AUDIO

config_private.py

@@ -0,0 +1,444 @@
+"""
+    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GPT等模型 ) 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-api2dkey1,fkxxxx-api2dkey2"
+
+# [step 1-2]>> ( 接入通义 qwen-max ) 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
+
+# [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 = True
+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
+DEFAULT_WORKER_NUM = 256
+
+# [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-4-32k" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["deepseek-chat", "deepseek-coder", "deepseek-reasoner",
+                    "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",
+                    ]
+
+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 = 64
+
+
+# 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)",
+    "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 = 60
+
+
+# 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 = 5
+
+
+# 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 = 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）
+# [("username", "password"), ("username2", "password2"), ...]
+AUTHENTICATION = [("van", "L807878712"),("林", "L807878712"),("源", "L807878712"),("欣", "L807878712"),("z", "czh123456789")]
+
+
+# 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 = "DISABLE" # 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 = ""
+
+
+# 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) ]
+
+
+
+"""
+--------------- 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
+
+
+"""
+
+
+

crazy_functional.py

@@ -2,7 +2,6 @@ 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.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
@@ -17,24 +16,24 @@ 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全文润色 import Latex英文润色
+    from crazy_functions.Latex_Project_Polish 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.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.辅助功能 import 清除缓存
-    from crazy_functions.批量文件询问 import 批量文件询问
     from crazy_functions.Markdown_Translate import Markdown英译中
     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.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.Latex_Project_Polish import Latex中文润色
+    from crazy_functions.Latex_Project_Polish import Latex英文纠错
     from crazy_functions.Markdown_Translate import Markdown中译英
     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
@@ -50,8 +49,16 @@ def get_crazy_functions():
     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
 
     function_plugins = {
+        "多媒体智能体": {
+            "Group": "智能体",
+            "Color": "stop",
+            "AsButton": False,
+            "Info": "【仅测试】多媒体任务",
+            "Function": HotReload(多媒体任务),
+        },
         "虚空终端": {
             "Group": "对话|编程|学术|智能体",
             "Color": "stop",
@@ -106,17 +113,16 @@ def get_crazy_functions():
             "Group": "学术",
             "Color": "stop",
             "AsButton": True,
-            "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+            "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“虚空终端”中起作用
             "Class": Arxiv_Localize,    # 新一代插件需要注册Class
         },
-        "批量文件询问": {
+        "批量总结Word文档": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,
-            "AdvancedArgs": True,
-            "Info": "通过在高级参数区写入prompt，可自定义询问逻辑，默认情况下为总结逻辑 | 输入参数为路径",
-            "Function": HotReload(批量文件询问),
+            "Info": "批量总结word文档 | 输入参数为路径",
+            "Function": HotReload(总结word文档),
         },
         "解析整个Matlab项目": {
             "Group": "编程",
@@ -346,7 +352,7 @@ 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": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+            "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“虚空终端”中起作用
             "Class": Arxiv_Localize,    # 新一代插件需要注册Class
         },
@@ -428,36 +434,6 @@ 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
@@ -721,12 +697,6 @@ def get_crazy_functions():
         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({
@@ -741,19 +711,6 @@ 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:
@@ -773,3 +730,26 @@ 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
+    }

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
+from toolbox import CatchException, update_ui, get_conf, update_ui_latest_msg
 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:
-        querys_json = predict_no_ui_long_connection(
+        query_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:
-        querys_json = "1234"
+        query_json = "null"
     #* 尝试解码优化后的搜索结果
-    querys_json = re.sub(r"```json|```", "", querys_json)
+    query_json = re.sub(r"```json|```", "", query_json)
     try:
-        querys = json.loads(querys_json)
+        queries = json.loads(query_json)
     except Exception:
         #* 如果解码失败,降低温度再试一次
         try:
             llm_kwargs["temperature"] = 0.4
-            querys_json = predict_no_ui_long_connection(
+            query_json = predict_no_ui_long_connection(
                 inputs=query,
                 llm_kwargs=llm_kwargs,
                 history=[],
                 sys_prompt=sys_prompt,
                 observe_window=mutable,
             )
-            querys_json = re.sub(r"```json|```", "", querys_json)
-            querys = json.loads(querys_json)
+            query_json = re.sub(r"```json|```", "", query_json)
+            queries = json.loads(query_json)
         except Exception:
             #* 如果再次失败，直接返回原始问题
-            querys = [query]
+            queries = [query]
     links = []
     success = 0
     Exceptions = ""
-    for q in querys:
+    for q in queries:
         try:
             link = searxng_request(q, proxies, categories, searxng_url, engines=engines)
             if len(link) > 0:
@@ -115,7 +115,8 @@ def get_auth_ip():
 
 def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
     if searxng_url is None:
-        url = get_conf("SEARXNG_URL")
+        urls = get_conf("SEARXNG_URLS")
+        url = random.choice(urls)
     else:
         url = searxng_url
 
@@ -174,10 +175,17 @@ 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
@@ -193,6 +201,56 @@ 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]
@@ -213,23 +271,52 @@ 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
-    chatbot.append(["联网检索中 ...", None])
+    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 = []
         for index, url in enumerate(urls[:max_search_result]):
-        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]}）："
+            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
             history.extend([prefix, res])
-        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
+            yield from update_ui_latest_msg(lastmsg=buffer, chatbot=chatbot, history=history, delay=0.1)  # 刷新界面
 
     # ------------- < 第3步：ChatGPT综合 > -------------
     if (optimizer != "开启(增强)"):

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
@@ -20,6 +20,9 @@ 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
@@ -30,16 +33,17 @@ 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=get_conf("SEARXNG_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=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, chatbot, history, system_prompt, user_request):
+    def execute(txt, llm_kwargs, plugin_kwargs:dict, chatbot, history, system_prompt, user_request):
         """
         执行插件
         """
-        if plugin_kwargs["categories"] == "网页": plugin_kwargs["categories"] = "general"
-        if plugin_kwargs["categories"] == "学术论文": plugin_kwargs["categories"] = "science"
+        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"
         yield from 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
 

crazy_functions/Latex_Function.py

@@ -1,5 +1,5 @@
 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_lastest_msg, zip_result, gen_time_str
+from toolbox import CatchException, report_exception, update_ui_latest_msg, zip_result, gen_time_str
 from functools import partial
 from loguru import logger
 
@@ -41,7 +41,7 @@ def switch_prompt(pfg, mode, more_requirement):
     return inputs_array, sys_prompt_array
 
 
-def desend_to_extracted_folder_if_exist(project_folder):
+def descend_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_lastest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
+        yield from update_ui_latest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
         return msg, None
     # <-------------- set format ------------->
     arxiv_id = url_.split('/abs/')[-1]
@@ -156,16 +156,16 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
         return False
 
     if os.path.exists(dst) and allow_cache:
-        yield from update_ui_lastest_msg(f"调用缓存 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
+        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_lastest_msg(f"开始下载 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
+        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_lastest_msg(f"下载完成 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
+        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_lastest_msg(f"下载失败 {arxiv_id}", chatbot=chatbot, history=history)
+        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 ------------->
@@ -288,7 +288,7 @@ 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 = desend_to_extracted_folder_if_exist(project_folder)
+    project_folder = descend_to_extracted_folder_if_exist(project_folder)
 
     # <-------------- move latex project away from temp folder ------------->
     from shared_utils.fastapi_server import validate_path_safety
@@ -365,7 +365,7 @@ 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_lastest_msg(
+        yield from update_ui_latest_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
@@ -404,7 +404,7 @@ 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 = desend_to_extracted_folder_if_exist(project_folder)
+    project_folder = descend_to_extracted_folder_if_exist(project_folder)
 
     # <-------------- move latex project away from temp folder ------------->
     from shared_utils.fastapi_server import validate_path_safety
@@ -518,7 +518,7 @@ 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_lastest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
+    #     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)
     #     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 +531,7 @@ 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_lastest_msg(f"未发现重复上传", chatbot=chatbot, history=history)
+    #     yield from update_ui_latest_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 +543,7 @@ 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_lastest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
+    yield from update_ui_latest_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 +551,7 @@ 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 = desend_to_extracted_folder_if_exist(project_folder)
+    project_folder = descend_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 +559,7 @@ 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') as f:
+    with open(pj(project_folder, hash_tag + '.tag'), 'w', encoding='utf8') as f:
         f.write(hash_tag)
         f.close()
 
@@ -571,7 +571,7 @@ 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_lastest_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_latest_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,

crazy_functions/PDF_Translate.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, check_packages, get_conf
-from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import update_ui, update_ui_latest_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，现在将执行效果稍差的旧版代码。{trimmed_format_exc_markdown()}"])
+                chatbot.append([None, f"DOC2X服务不可用，请检查报错详细。{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_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 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 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_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 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 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
         return
 

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
 

crazy_functions/Rag_Interface.py

@@ -1,4 +1,11 @@
-from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
+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 crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 
@@ -7,6 +14,37 @@ 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):
 
@@ -23,6 +61,7 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
     # 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:
@@ -30,21 +69,37 @@ 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"
-    if txt == "清空向量数据库":
-        chatbot.append([txt, f'正在清空 ({current_context}) ...'])
+
+    # 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
-        rag_worker.purge()
-        yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 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":
+        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
+        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
 
-    # 2. clip history to reduce token consumption
-    #   2-1. reduce chat round
+    # 4. Clip history to reduce token consumption
     txt_origin = txt
 
     if len(history) > MAX_HISTORY_ROUND * 2:
@@ -52,41 +107,47 @@ 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"])
 
-    #   2-2. if input is clipped, add input to vector store before retrieve
+    # 5. If input is clipped, add input to vector store before retrieve
     if input_is_clipped_flag:
-        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
+        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
         rag_worker.add_text_to_vector_store(txt_origin)
-        yield from update_ui_lastest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
+        yield from update_ui_latest_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
 
-    # 3. we search vector store and build prompts
+    # 6. 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
     )
 
-    # 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
+    # 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
+    )
     rag_worker.remember_qa(i_say_to_remember, model_say)
     history.extend([i_say, model_say])
 
-    yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip) # 刷新界面
+    # 9. Final UI Update
+    yield from update_ui_latest_msg(model_say, chatbot, history, delay=0, msg=tip)

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_lastest_msg
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_latest_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_NETWOK_WORKER_REGISTER = {}
+SOCIAL_NETWORK_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_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=0)
+            yield from update_ui_latest_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:
-            Explaination = '\n'.join([f'{k}: {v["explain_to_llm"]}' for k, v in self.tools_to_select.items()])
+            Explanation = '\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"Explaination:\n{Explaination}", 
+                        f"Explanation:\n{Explanation}", 
                     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_lastest_msg(
+            yield from update_ui_latest_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_NETWOK_WORKER_REGISTER:
-        social_network_worker = SOCIAL_NETWOK_WORKER_REGISTER[user_name]
+    if user_name in SOCIAL_NETWORK_WORKER_REGISTER:
+        social_network_worker = SOCIAL_NETWORK_WORKER_REGISTER[user_name]
     else:
-        social_network_worker = SOCIAL_NETWOK_WORKER_REGISTER[user_name] = SocialNetworkWorker(
+        social_network_worker = SOCIAL_NETWORK_WORKER_REGISTER[user_name] = SocialNetworkWorker(
             user_name, 
             llm_kwargs, 
             checkpoint_dir=checkpoint_dir, 

crazy_functions/SourceCode_Comment.py

@@ -1,5 +1,5 @@
 import os, copy, time
-from toolbox import CatchException, report_exception, update_ui, zip_result, promote_file_to_downloadzone, update_ui_lastest_msg, get_conf, generate_file_link
+from toolbox import CatchException, report_exception, update_ui, zip_result, promote_file_to_downloadzone, update_ui_latest_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
@@ -117,7 +117,7 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
                 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_lastest_msg(
+        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/>" + 

crazy_functions/VideoResource_GPT.py

@@ -0,0 +1,204 @@
+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)

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_lastest_msg, Singleton
+from toolbox import report_exception, get_log_folder, update_ui_latest_msg, Singleton
 from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
 from crazy_functions.agent_fns.general import AutoGenGeneral
 

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 futher user input
+                # wait father 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:

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_slience=True
+        console_silence=True
     )
     assumed_done = reply.endswith('\nTERMINATE')
     return True, reply

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 sperate 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 separate 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_funtion_prompt = '''
+revise_function_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).
@@ -117,7 +117,7 @@ def zip_result(folder):
 '''
 
 
-revise_funtion_prompt_chinese = '''
+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
@@ -188,9 +188,9 @@ class PythonCodeComment():
         self.language = language
         self.observe_window_update = observe_window_update
         if self.language == "chinese":
-            self.core_prompt = revise_funtion_prompt_chinese
+            self.core_prompt = revise_function_prompt_chinese
         else:
-            self.core_prompt = revise_funtion_prompt
+            self.core_prompt = revise_function_prompt
         self.path = None
         self.file_basename = None
         self.file_brief = ""
@@ -222,7 +222,7 @@ class PythonCodeComment():
             history=[],
             sys_prompt="",
             observe_window=[],
-            console_slience=True
+            console_silence=True
         )
 
         def extract_number(text):
@@ -316,7 +316,7 @@ 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 preseve 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 preserve 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
@@ -333,7 +333,7 @@ class PythonCodeComment():
             history=[],
             sys_prompt="",
             observe_window=[],
-            console_slience=True
+            console_silence=True
         )
 
         def get_code_block(reply):
@@ -400,7 +400,7 @@ class PythonCodeComment():
         return revised
 
     def begin_comment_source_code(self, chatbot=None, history=None):
-        # from toolbox import update_ui_lastest_msg
+        # from toolbox import update_ui_latest_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 +409,10 @@ class PythonCodeComment():
         # with open(self.path + '.revised.py', 'w+', encoding='utf8') as f:
         while True:
             try:
-                # 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)
+                # 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)
                 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_lastest_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_latest_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

crazy_functions/chatglm微调工具.py

@@ -1,141 +0,0 @@
-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

crazy_functions/crazy_utils.py

@@ -1,7 +1,7 @@
 import os
 import threading
 from loguru import logger
-from shared_utils.char_visual_effect import scolling_visual_effect
+from shared_utils.char_visual_effect import scrolling_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):
@@ -169,6 +169,7 @@ 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
@@ -255,7 +256,7 @@ 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_slience=True
+                    sys_prompt=sys_prompt, observe_window=mutable[index], console_silence=True
                 )
                 mutable[index][2] = "已成功"
                 return gpt_say
@@ -325,7 +326,7 @@ 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"[ ...`{scolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
+            print_something_really_funny = f"[ ...`{scrolling_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'
@@ -388,11 +389,11 @@ def read_and_clean_pdf_text(fp):
         """
         提取文本块主字体
         """
-        fsize_statiscs = {}
+        fsize_statistics = {}
         for wtf in l['spans']:
-            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)
+            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)
 
     def ffsize_same(a,b):
         """
@@ -432,11 +433,11 @@ def read_and_clean_pdf_text(fp):
 
         ############################## <第 2 步，获取正文主字体> ##################################
         try:
-            fsize_statiscs = {}
+            fsize_statistics = {}
             for span in meta_span:
-                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 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 REMOVE_FOOT_NOTE:
                 give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
         except:
@@ -609,9 +610,9 @@ class nougat_interface():
 
 
     def NOUGAT_parse_pdf(self, fp, chatbot, history):
-        from toolbox import update_ui_lastest_msg
+        from toolbox import update_ui_latest_msg
 
-        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...",
+        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...",
                                          chatbot=chatbot, history=history, delay=0)
         self.threadLock.acquire()
         import glob, threading, os
@@ -619,7 +620,7 @@ class nougat_interface():
         dst = os.path.join(get_log_folder(plugin_name='nougat'), gen_time_str())
         os.makedirs(dst)
 
-        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）",
+        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）",
                                          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)

crazy_functions/doc_fns/batch_file_query_doc.py

@@ -1,450 +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
-
-
-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"""
-        paragraph = self.doc.add_paragraph(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
-
-        返回:
-            str: 格式化后的文件总结字符串
-
-        标题层级规则:
-        1. 一级标题为"各文件详细总结"
-        2. 如果文件有目录路径:
-           - 目录路径作为二级标题 (2.1, 2.2 等)
-           - 该目录下所有文件作为三级标题 (2.1.1, 2.1.2 等)
-        3. 如果文件没有目录路径:
-           - 文件直接作为二级标题 (2.1, 2.2 等)
-        """
-        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)
-                self._add_content(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)
-                self._add_content(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
-        self._add_heading("总体摘要", 1)
-        self._add_content(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
-
-
-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"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.css_styles = """
-        body {
-            font-family: "Microsoft YaHei", Arial, sans-serif;
-            line-height: 1.6;
-            max-width: 1000px;
-            margin: 0 auto;
-            padding: 20px;
-            color: #333;
-        }
-        h1 {
-            color: #2c3e50;
-            border-bottom: 2px solid #eee;
-            padding-bottom: 10px;
-            font-size: 24px;
-            text-align: center;
-        }
-        h2 {
-            color: #34495e;
-            margin-top: 30px;
-            font-size: 20px;
-            border-left: 4px solid #3498db;
-            padding-left: 10px;
-        }
-        h3 {
-            color: #2c3e50;
-            font-size: 18px;
-            margin-top: 20px;
-        }
-        .summary {
-            background-color: #f8f9fa;
-            padding: 20px;
-            border-radius: 5px;
-            margin: 20px 0;
-            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-        }
-        .details {
-            margin-top: 40px;
-        }
-        .failed-files {
-            background-color: #fff3f3;
-            padding: 15px;
-            border-left: 4px solid #e74c3c;
-            margin: 20px 0;
-        }
-        .file-summary {
-            background-color: #fff;
-            padding: 15px;
-            margin: 15px 0;
-            border-radius: 4px;
-            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
-        }
-        """
-
-    def format_failed_files(self) -> str:
-        if not self.failed_files:
-            return ""
-
-        failed_files_html = ['<div class="failed-files">']
-        failed_files_html.append("<h2>⚠️ 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> {reason}</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(f'<h2>📁 {dir_path}</h2>')
-                current_dir = dir_path
-
-            file_name = os.path.basename(path)
-            formatted_html.append('<div class="file-summary">')
-            formatted_html.append(f'<h3>📄 {file_name}</h3>')
-            formatted_html.append(f'<p>{self.file_summaries_map[path]}</p>')
-            formatted_html.append('</div>')
-
-        return "\n".join(formatted_html)
-
-    def create_document(self) -> str:
-        return f"""
-        <!DOCTYPE html>
-        <html>
-        <head>
-            <meta charset='utf-8'>
-            <title>文档总结报告</title>
-            <style>{self.css_styles}</style>
-        </head>
-        <body>
-            <h1>📑 文档总结报告</h1>
-            <h2>总体摘要</h2>
-            <div class="summary">{self.final_summary}</div>
-            {self.format_failed_files()}
-            <div class="details">
-                <h2>📚 各文件详细总结</h2>
-                {self.format_file_summaries()}
-            </div>
-        </body>
-        </html>
-        """
-
-

crazy_functions/game_fns/game_ascii_art.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
+from toolbox import CatchException, update_ui, update_ui_latest_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_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_latest_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_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_latest_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_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_latest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
             else:
                 self.cur_task = 'identify user guess'
-                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.)
+                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.)

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_lastest_msg
+from toolbox import CatchException, update_ui, update_ui_latest_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_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                yield from update_ui_latest_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_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.)
+            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.)
 
             # # 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_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.)
+            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.)
 
             # # 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_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.)
+            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.)
 
             # terminate game
             self.delete_game = True

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_lastest_msg
+from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_latest_msg
 import multiprocessing
 
 def get_class_name(class_string):

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('Repaire json success.')
+                logging.info('Repair json success.')
             except Exception as e:
                 # 没辙了，放弃治疗
-                logging.info('Repaire json fail.')
+                logging.info('Repair json fail.')
                 raise JsonStringError('Cannot repair json.', str(e))
         return result
 

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_lastest_msg, get_log_folder, gen_time_str
+from toolbox import update_ui, update_ui_latest_msg, get_log_folder, gen_time_str
 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 proccessed by GPT.
+    be processed 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 proccessed by GPT.
+    be processed 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REAME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README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 proccessed by GPT.
+        be processed by GPT.
         P.S. use multiprocessing to avoid timeout error
         """
         import multiprocessing
@@ -300,7 +300,8 @@ 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 ---------->
-    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']}。"
+    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']}。"
     final_tex = lps.merge_result(pfg.file_result, mode, msg)
     objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))
 
@@ -350,7 +351,42 @@ 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_lastest_msg('编译已经开始...', chatbot, history)   # 刷新Gradio前端界面
+    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。")
 
     while True:
         import os
@@ -360,36 +396,36 @@ 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_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)
+        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)
 
         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_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
+            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
             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_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)
+            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)
 
             if mode!='translate_zh':
-                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
+                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
                 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_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)
+                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)
                 ok = compile_latex_with_timeout(f'bibtex    merge_diff.aux', 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)
+                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)
 
         # <---------- 检查结果 ----------->
         results_ = ""
@@ -399,13 +435,13 @@ 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_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
+        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
 
         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_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
+            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
             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')):
@@ -436,7 +472,7 @@ 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_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
+            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
             if not can_retry: break
 
     return False # 失败啦

crazy_functions/latex_fns/latex_pickle_io.py

@@ -6,12 +6,16 @@ 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
 
@@ -22,8 +26,6 @@ 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

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 compelete text area.
+    count the number of the braces so as to catch complete 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 compelete text area.
+    count the number of the braces so as to catch complete 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 compelete text area.
+    count the number of the braces so as to catch complete 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)
     """
-    canidates = []
+    candidates = []
     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:
-            canidates.append(texf)
+            candidates.append(texf)
         else:
             continue
 
-    if len(canidates) == 0:
+    if len(candidates) == 0:
         raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
-    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 = []
+    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 = []
         # 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 canidates:
-            canidates_score.append(0)
+        for texf in candidates:
+            candidates_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:
-                    canidates_score[-1] -= 1
+                    candidates_score[-1] -= 1
             for uw in expected_words:
                 if uw in file_content:
-                    canidates_score[-1] += 1
-        select = np.argmax(canidates_score)  # 取评分最高者返回
-        return canidates[select]
+                    candidates_score[-1] += 1
+        select = np.argmax(candidates_score)  # 取评分最高者返回
+        return candidates[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 recrusively
+    Merge Tex project recursively
     """
     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 recrusively
+    Merge Tex project recursively
     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
     """

crazy_functions/media_fns/get_media.py

@@ -0,0 +1,43 @@
+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']
+

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_lastest_msg, disable_auto_promotion
+from toolbox import update_ui_latest_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

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 seperation
+            # find a smooth token limit to achieve even separation
             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'])

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_lastest_msg, disable_auto_promotion
+from toolbox import update_ui, promote_file_to_downloadzone, update_ui_latest_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
 

crazy_functions/pdf_fns/parse_pdf_via_doc2x.py

@@ -6,75 +6,128 @@ 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 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}
-    )
-    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 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}"
+                        )
+                        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
 
 
 def 解析PDF_DOC2X_转Latex(pdf_file_path):
-    zip_file_path, unzipped_folder = 解析PDF_DOC2X(pdf_file_path, format='tex')
+    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'):
+def 解析PDF_DOC2X(pdf_file_path, format="tex"):
     """
     format: 'tex', 'md', 'docx'
     """
-    import requests, json, os
-    DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
+
+    DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
     latex_dir = get_log_folder(plugin_name="pdf_ocr_latex")
     markdown_dir = get_log_folder(plugin_name="pdf_ocr")
     doc2x_api_key = DOC2X_API_KEY
 
-
-    # < ------ 第1步：上传 ------ >
-    logger.info("Doc2x 第1步：上传")
-    with open(pdf_file_path, 'rb') as file:
-        res = requests.post(
-            "https://v2.doc2x.noedgeai.com/api/v2/parse/pdf",
+    # < ------ 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},
-            data=file
+        timeout=15,
     )
-    # res_json = []
-    if res.status_code == 200:
-        res_json = res.json()
-    else:
-        raise RuntimeError(f"Doc2x return an error: {res.json()}")
-    uuid = res_json['data']['uid']
+    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 第2步：轮询等待")
-    params = {'uid': uuid}
-    while True:
-        res = requests.get(
-            'https://v2.doc2x.noedgeai.com/api/v2/parse/status',
+    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
+            params=params,
+            timeout=15,
         )
-        res_json = res.json()
-        if res_json['data']['status'] == "success":
+        res_data = doc2x_api_response_status(res)
+        if res_data["status"] == "success":
             break
-        elif res_json['data']['status'] == "processing":
-            time.sleep(3)
-            logger.info(f"Doc2x is processing at {res_json['data']['progress']}%")
-        elif res_json['data']['status'] == "failed":
-            raise RuntimeError(f"Doc2x return an error: {res_json}")
-
+        elif res_data["status"] == "processing":
+            time.sleep(5)
+            logger.info(f"Doc2x is processing at {res_data['progress']}%")
+            attempt += 1
+        else:
+            raise RuntimeError(f"Doc2x return an error: {res_data}")
+    if attempt >= max_attempts:
+        raise RuntimeError("Doc2x processing timeout after maximum attempts")
 
     # < ------ 第3步：提交转化 ------ >
     logger.info("Doc2x 第3步：提交转化")
@@ -84,42 +137,44 @@ def 解析PDF_DOC2X(pdf_file_path, format='tex'):
         "formula_mode": "dollar",
         "filename": "output"
     }
-    res = requests.post(
-        'https://v2.doc2x.noedgeai.com/api/v2/convert/parse',
+    res = make_request(
+        "POST",
+        "https://v2.doc2x.noedgeai.com/api/v2/convert/parse",
         headers={"Authorization": "Bearer " + doc2x_api_key},
-        json=data
+        json=data,
+        timeout=15,
     )
-    if res.status_code == 200:
-        res_json = res.json()
-    else:
-        raise RuntimeError(f"Doc2x return an error: {res.json()}")
-
+    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}
-    while True:
-        res = requests.get(
-            'https://v2.doc2x.noedgeai.com/api/v2/convert/parse/result',
+    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
+            params=params,
+            timeout=15,
         )
-        res_json = res.json()
-        if res_json['data']['status'] == "success":
+        res_data = doc2x_api_response_status(res, uid=f"uid: {uuid}")
+        if res_data["status"] == "success":
             break
-        elif res_json['data']['status'] == "processing":
+        elif res_data["status"] == "processing":
             time.sleep(3)
-            logger.info(f"Doc2x still processing")
-        elif res_json['data']['status'] == "failed":
-            raise RuntimeError(f"Doc2x return an error: {res_json}")
-
+            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：最后的处理")
+    logger.info("Doc2x 第5步：下载转换后的文件")
 
-    if format=='tex':
+    if format == "tex":
         target_path = latex_dir
-    if format=='md':
+    if format == "md":
         target_path = markdown_dir
     os.makedirs(target_path, exist_ok=True)
 
@@ -127,17 +182,18 @@ def 解析PDF_DOC2X(pdf_file_path, format='tex'):
     # < ------ 下载 ------ >
     for attempt in range(max_attempt):
         try:
-            result_url = res_json['data']['url']
-            res = requests.get(result_url)
-            zip_path = os.path.join(target_path, gen_time_str() + '.zip')
+            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)
+                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 latex file, retrying... {e}")
+                logger.error(f"Failed to download uid = {uuid} file, retrying... {e}")
                 time.sleep(3)
                 continue
             else:
@@ -145,18 +201,27 @@ def 解析PDF_DOC2X(pdf_file_path, format='tex'):
 
     # < ------ 解压 ------ >
     import zipfile
-    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    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 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"))
         yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
 
-        md_zip_path, unzipped_folder = 解析PDF_DOC2X(filepath, format='md')
+        md_zip_path, unzipped_folder = 解析PDF_DOC2X(filepath, format="md")
 
         promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
         chatbot.append((None, f"完成解析 {md_zip_path} ..."))
@@ -174,77 +239,97 @@ def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, cha
         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
+        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)
+        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
-
-

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
-        excption(string):需要用户手动处理的信息,如没出错则保持为空
+        exception(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 = []
-    excption = ""
+    exception = ""
 
     if txt == "":
         final_result.append(txt)
-        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
+        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
 
     #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:
-        excption = "word"
-        return False, final_result, page_one, file_manifest, excption
+        exception = "word"
+        return False, final_result, page_one, file_manifest, exception
 
     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, excption   #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, exception   #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:
-            excption = "pdf"
-            return False, final_result, page_one, file_manifest, excption
+            exception = "pdf"
+            return False, final_result, page_one, file_manifest, exception
         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:
-            excption = "word_pip"
-            return False, final_result, page_one, file_manifest, excption
+            exception = "word_pip"
+            return False, final_result, page_one, file_manifest, exception
         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, excption
+    return True, final_result, page_one, file_manifest, exception

crazy_functions/rag_fns/rag_file_support.py

@@ -1,39 +1,16 @@
 import os
 from llama_index.core import SimpleDirectoryReader
 
-supports_format = ['.csv', '.docx','.doc', '.epub', '.ipynb',  '.mbox', '.md', '.pdf',  '.txt', '.ppt',
-                   '.pptm', '.pptx','.py', '.xls', '.xlsx', '.html', '.json', '.xml', '.yaml', '.yml' ,'.m']
+supports_format = ['.csv', '.docx', '.epub', '.ipynb',  '.mbox', '.md', '.pdf',  '.txt', '.ppt',
+                   '.pptm', '.pptx']
 
-def read_docx_doc(file_path):
-    if file_path.split(".")[-1] == "docx":
-        from docx import Document
-        doc = Document(file_path)
-        file_content = "\n".join([para.text for para in doc.paragraphs])
-    else:
-        try:
-            import win32com.client
-            word = win32com.client.Dispatch("Word.Application")
-            word.visible = False
-            # 打开文件
-            doc = word.Documents.Open(os.getcwd() + '/' + file_path)
-            # file_content = doc.Content.Text
-            doc = word.ActiveDocument
-            file_content = doc.Range().Text
-            doc.Close()
-            word.Quit()
-        except:
-            raise RuntimeError('请先将.doc文档转换为.docx文档。')
-    return file_content
 
 # 修改后的 extract_text 函数，结合 SimpleDirectoryReader 和自定义解析逻辑
-import os
-
 def extract_text(file_path):
     _, ext = os.path.splitext(file_path.lower())
 
     # 使用 SimpleDirectoryReader 处理它支持的文件格式
-    if ext in ['.docx', '.doc']:
-        return read_docx_doc(file_path)
+    if ext in supports_format:
         try:
             reader = SimpleDirectoryReader(input_files=[file_path])
             documents = reader.load_data()

crazy_functions/vector_fns/vector_database.py

@@ -60,7 +60,7 @@ def similarity_search_with_score_by_vector(
         self, embedding: List[float], k: int = 4
 ) -> List[Tuple[Document, float]]:
 
-    def seperate_list(ls: List[int]) -> List[List[int]]:
+    def separate_list(ls: List[int]) -> List[List[int]]:
         lists = []
         ls1 = [ls[0]]
         for i in range(1, len(ls)):
@@ -82,7 +82,7 @@ def similarity_search_with_score_by_vector(
             continue
         _id = self.index_to_docstore_id[i]
         doc = self.docstore.search(_id)
-        if not self.chunk_conent:
+        if not self.chunk_content:
             if not isinstance(doc, Document):
                 raise ValueError(f"Could not find document for id {_id}, got {doc}")
             doc.metadata["score"] = int(scores[0][j])
@@ -104,12 +104,12 @@ def similarity_search_with_score_by_vector(
                         id_set.add(l)
             if break_flag:
                 break
-    if not self.chunk_conent:
+    if not self.chunk_content:
         return docs
     if len(id_set) == 0 and self.score_threshold > 0:
         return []
     id_list = sorted(list(id_set))
-    id_lists = seperate_list(id_list)
+    id_lists = separate_list(id_list)
     for id_seq in id_lists:
         for id in id_seq:
             if id == id_seq[0]:
@@ -132,7 +132,7 @@ class LocalDocQA:
     embeddings: object = None
     top_k: int = VECTOR_SEARCH_TOP_K
     chunk_size: int = CHUNK_SIZE
-    chunk_conent: bool = True
+    chunk_content: bool = True
     score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
 
     def init_cfg(self,
@@ -209,16 +209,16 @@ class LocalDocQA:
 
     # query      查询内容
     # vs_path    知识库路径
-    # chunk_conent   是否启用上下文关联
+    # chunk_content   是否启用上下文关联
     # score_threshold    搜索匹配score阈值
     # vector_search_top_k   搜索知识库内容条数，默认搜索5条结果
     # chunk_sizes    匹配单段内容的连接上下文长度
-    def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
+    def get_knowledge_based_content_test(self, query, vs_path, chunk_content,
                                         score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
                                         vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE,
                                         text2vec=None):
         self.vector_store = FAISS.load_local(vs_path, text2vec)
-        self.vector_store.chunk_conent = chunk_conent
+        self.vector_store.chunk_content = chunk_content
         self.vector_store.score_threshold = score_threshold
         self.vector_store.chunk_size = chunk_size
 
@@ -241,7 +241,7 @@ class LocalDocQA:
 
 
 
-def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
+def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_content, one_content_segmentation, text2vec):
     for file in files:
         assert os.path.exists(file), "输入文件不存在：" + file
     import nltk
@@ -297,7 +297,7 @@ class knowledge_archive_interface():
             files=file_manifest,
             sentence_size=100,
             history=[],
-            one_conent="",
+            one_content="",
             one_content_segmentation="",
             text2vec = self.get_chinese_text2vec(),
         )
@@ -319,19 +319,19 @@ class knowledge_archive_interface():
                 files=[],
                 sentence_size=100,
                 history=[],
-                one_conent="",
+                one_content="",
                 one_content_segmentation="",
                 text2vec = self.get_chinese_text2vec(),
             )
         VECTOR_SEARCH_SCORE_THRESHOLD = 0
         VECTOR_SEARCH_TOP_K = 4
         CHUNK_SIZE = 512
-        resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
+        resp, prompt = self.qa_handle.get_knowledge_based_content_test(
             query = txt,
             vs_path = self.kai_path,
             score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
             vector_search_top_k=VECTOR_SEARCH_TOP_K,
-            chunk_conent=True,
+            chunk_content=True,
             chunk_size=CHUNK_SIZE,
             text2vec = self.get_chinese_text2vec(),
         )

crazy_functions/vt_fns/vt_call_plugin.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel, Field
 from typing import List
-from toolbox import update_ui_lastest_msg, disable_auto_promotion
+from toolbox import update_ui_latest_msg, disable_auto_promotion
 from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
 import copy, json, pickle, os, sys, time
@@ -9,14 +9,14 @@ import copy, json, pickle, os, sys, time
 def read_avail_plugin_enum():
     from crazy_functional import get_crazy_functions
     plugin_arr = get_crazy_functions()
-    # remove plugins with out explaination
+    # remove plugins with out explanation
     plugin_arr = {k:v for k, v in plugin_arr.items() if ('Info' in v) and ('Function' in v)}
     plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict_parse.update({f"F_{i}":v for i, v in enumerate(plugin_arr.values(), start=1)})
     prompt = json.dumps(plugin_arr_info, ensure_ascii=False, indent=2)
-    prompt = "\n\nThe defination of PluginEnum:\nPluginEnum=" + prompt
+    prompt = "\n\nThe definition of PluginEnum:\nPluginEnum=" + prompt
     return prompt, plugin_arr_dict, plugin_arr_dict_parse
 
 def wrap_code(txt):
@@ -55,7 +55,7 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
         plugin_selection: str = Field(description="The most related plugin from one of the PluginEnum.", default="F_0000")
         reason_of_selection: str = Field(description="The reason why you should select this plugin.", default="This plugin satisfy user requirement most")
     # ⭐ ⭐ ⭐ 选择插件
-    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
+    yield from update_ui_latest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
     gpt_json_io = GptJsonIO(Plugin)
     gpt_json_io.format_instructions = "The format of your output should be a json that can be parsed by json.loads.\n"
     gpt_json_io.format_instructions += """Output example: {"plugin_selection":"F_1234", "reason_of_selection":"F_1234 plugin satisfy user requirement most"}\n"""
@@ -74,13 +74,13 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
         msg += "请求的Prompt为：\n" + wrap_code(get_inputs_show_user(inputs, plugin_arr_enum_prompt))
         msg += "语言模型回复为：\n" + wrap_code(gpt_reply)
         msg += "\n但您可以尝试再试一次\n"
-        yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
+        yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
         return
     if plugin_sel.plugin_selection not in plugin_arr_dict_parse:
         msg = f"抱歉, 找不到合适插件执行该任务, 或者{llm_kwargs['llm_model']}无法理解您的需求。"
         msg += f"语言模型{llm_kwargs['llm_model']}选择了不存在的插件：\n" + wrap_code(gpt_reply)
         msg += "\n但您可以尝试再试一次\n"
-        yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
+        yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
         return
 
     # ⭐ ⭐ ⭐ 确认插件参数
@@ -90,7 +90,7 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
         appendix_info = get_recent_file_prompt_support(chatbot)
 
     plugin = plugin_arr_dict_parse[plugin_sel.plugin_selection]
-    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
+    yield from update_ui_latest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
     class PluginExplicit(BaseModel):
         plugin_selection: str = plugin_sel.plugin_selection
         plugin_arg: str = Field(description="The argument of the plugin.", default="")
@@ -109,6 +109,6 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
     fn = plugin['Function']
     fn_name = fn.__name__
     msg = f'{llm_kwargs["llm_model"]}为您选择了插件: `{fn_name}`\n\n插件说明：{plugin["Info"]}\n\n插件参数：{plugin_sel.plugin_arg}\n\n假如偏离了您的要求，按停止键终止。'
-    yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
+    yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
     yield from fn(plugin_sel.plugin_arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, -1)
     return

crazy_functions/vt_fns/vt_modify_config.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel, Field
 from typing import List
-from toolbox import update_ui_lastest_msg, get_conf
+from toolbox import update_ui_latest_msg, get_conf
 from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.json_fns.pydantic_io import GptJsonIO
 import copy, json, pickle, os, sys
@@ -9,7 +9,7 @@ import copy, json, pickle, os, sys
 def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
     ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
     if not ALLOW_RESET_CONFIG:
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
             chatbot=chatbot, history=history, delay=2
         )
@@ -30,7 +30,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         new_option_value: str = Field(description="the new value of the option", default=None)
 
     # ⭐ ⭐ ⭐ 分析用户意图
-    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n读取新配置中", chatbot=chatbot, history=history, delay=0)
+    yield from update_ui_latest_msg(lastmsg=f"正在执行任务: {txt}\n\n读取新配置中", chatbot=chatbot, history=history, delay=0)
     gpt_json_io = GptJsonIO(ModifyConfigurationIntention)
     inputs = "Analyze how to change configuration according to following user input, answer me with json: \n\n" + \
              ">> " + txt.rstrip('\n').replace('\n','\n>> ') + '\n\n' + \
@@ -44,11 +44,11 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
 
     ok = (explicit_conf in txt)
     if ok:
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}",
             chatbot=chatbot, history=history, delay=1
         )
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}\n\n正在修改配置中",
             chatbot=chatbot, history=history, delay=2
         )
@@ -57,25 +57,25 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         from toolbox import set_conf
         set_conf(explicit_conf, user_intention.new_option_value)
 
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，重新页面即可生效。", chatbot=chatbot, history=history, delay=1
         )
     else:
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"失败，如果需要配置{explicit_conf}，您需要明确说明并在指令中提到它。", chatbot=chatbot, history=history, delay=5
         )
 
 def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
     ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
     if not ALLOW_RESET_CONFIG:
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
             chatbot=chatbot, history=history, delay=2
         )
         return
 
     yield from modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
-    yield from update_ui_lastest_msg(
+    yield from update_ui_latest_msg(
         lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，五秒后即将重启！若出现报错请无视即可。", chatbot=chatbot, history=history, delay=5
     )
     os.execl(sys.executable, sys.executable, *sys.argv)

crazy_functions/vt_fns/vt_state.py

@@ -5,7 +5,7 @@ class VoidTerminalState():
         self.reset_state()
 
     def reset_state(self):
-        self.has_provided_explaination = False
+        self.has_provided_explanation = False
 
     def lock_plugin(self, chatbot):
         chatbot._cookies['lock_plugin'] = 'crazy_functions.虚空终端->虚空终端'

crazy_functions/互动小游戏.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
+from toolbox import CatchException, update_ui, update_ui_latest_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

crazy_functions/函数动态生成.py

@@ -15,7 +15,7 @@ Testing:
 
 
 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_lastest_msg
+from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_latest_msg
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
 from crazy_functions.crazy_utils import input_clipping, try_install_deps
 from crazy_functions.gen_fns.gen_fns_shared import is_function_successfully_generated
@@ -27,7 +27,7 @@ import time
 import glob
 import multiprocessing
 
-templete = """
+template = """
 ```python
 import ...  # Put dependencies here, e.g. import numpy as np.
 
@@ -77,10 +77,10 @@ def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
 
     # 第二步
     prompt_compose = [
-        "If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
-        templete
+        "If previous stage is successful, rewrite the function you have just written to satisfy following template: \n",
+        template
     ]
-    i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
+    i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable template. "
     gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
         inputs=i_say, inputs_show_user=inputs_show_user,
         llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
@@ -164,18 +164,18 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
     if get_plugin_arg(plugin_kwargs, key="file_path_arg", default=False):
         file_path = get_plugin_arg(plugin_kwargs, key="file_path_arg", default=None)
         file_list.append(file_path)
-        yield from update_ui_lastest_msg(f"当前文件: {file_path}", chatbot, history, 1)
+        yield from update_ui_latest_msg(f"当前文件: {file_path}", chatbot, history, 1)
     elif have_any_recent_upload_files(chatbot):
         file_dir = get_recent_file_prompt_support(chatbot)
         file_list = glob.glob(os.path.join(file_dir, '**/*'), recursive=True)
-        yield from update_ui_lastest_msg(f"当前文件处理列表: {file_list}", chatbot, history, 1)
+        yield from update_ui_latest_msg(f"当前文件处理列表: {file_list}", chatbot, history, 1)
     else:
         chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
-        yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
+        yield from update_ui_latest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
         return  # 2. 如果没有文件
     if len(file_list) == 0:
         chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
-        yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
+        yield from update_ui_latest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
         return  # 2. 如果没有文件
 
     # 读取文件
@@ -183,7 +183,7 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
 
     # 粗心检查
     if is_the_upload_folder(txt):
-        yield from update_ui_lastest_msg(f"请在输入框内填写需求, 然后再次点击该插件! 至于您的文件，不用担心, 文件路径 {txt} 已经被记忆. ", chatbot, history, 1)
+        yield from update_ui_latest_msg(f"请在输入框内填写需求, 然后再次点击该插件! 至于您的文件，不用担心, 文件路径 {txt} 已经被记忆. ", chatbot, history, 1)
         return
 
     # 开始干正事
@@ -195,7 +195,7 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
             code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
                 yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
             chatbot.append(["代码生成阶段结束", ""])
-            yield from update_ui_lastest_msg(f"正在验证上述代码的有效性 ...", chatbot, history, 1)
+            yield from update_ui_latest_msg(f"正在验证上述代码的有效性 ...", chatbot, history, 1)
             # ⭐ 分离代码块
             code = get_code_block(code)
             # ⭐ 检查模块
@@ -206,11 +206,11 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
             if not traceback: traceback = trimmed_format_exc()
         # 处理异常
         if not traceback: traceback = trimmed_format_exc()
-        yield from update_ui_lastest_msg(f"第 {j+1}/{MAX_TRY} 次代码生成尝试, 失败了~ 别担心, 我们5秒后再试一次... \n\n此次我们的错误追踪是\n```\n{traceback}\n```\n", chatbot, history, 5)
+        yield from update_ui_latest_msg(f"第 {j+1}/{MAX_TRY} 次代码生成尝试, 失败了~ 别担心, 我们5秒后再试一次... \n\n此次我们的错误追踪是\n```\n{traceback}\n```\n", chatbot, history, 5)
 
     # 代码生成结束, 开始执行
     TIME_LIMIT = 15
-    yield from update_ui_lastest_msg(f"开始创建新进程并执行代码! 时间限制 {TIME_LIMIT} 秒. 请等待任务完成... ", chatbot, history, 1)
+    yield from update_ui_latest_msg(f"开始创建新进程并执行代码! 时间限制 {TIME_LIMIT} 秒. 请等待任务完成... ", chatbot, history, 1)
     manager = multiprocessing.Manager()
     return_dict = manager.dict()
 

crazy_functions/多智能体.py

@@ -8,7 +8,7 @@
 
 import time
 from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
-from toolbox import get_conf, select_api_key, update_ui_lastest_msg, Singleton
+from toolbox import get_conf, select_api_key, update_ui_latest_msg, Singleton
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
 from crazy_functions.crazy_utils import input_clipping, try_install_deps
 from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses

crazy_functions/总结word文档.py

@@ -0,0 +1,127 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_exception
+from toolbox import write_history_to_file, promote_file_to_downloadzone
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+fast_debug = False
+
+
+def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import time, os
+    # pip install python-docx 用于docx格式，跨平台
+    # pip install pywin32 用于doc格式，仅支持Win平台
+    for index, fp in enumerate(file_manifest):
+        if fp.split(".")[-1] == "docx":
+            from docx import Document
+            doc = Document(fp)
+            file_content = "\n".join([para.text for para in doc.paragraphs])
+        else:
+            try:
+                import win32com.client
+                word = win32com.client.Dispatch("Word.Application")
+                word.visible = False
+                # 打开文件
+                doc = word.Documents.Open(os.getcwd() + '/' + fp)
+                # file_content = doc.Content.Text
+                doc = word.ActiveDocument
+                file_content = doc.Range().Text
+                doc.Close()
+                word.Quit()
+            except:
+                raise RuntimeError('请先将.doc文档转换为.docx文档。')
+
+        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
+        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        from request_llms.bridge_all import model_info
+        max_token = model_info[llm_kwargs['llm_model']]['max_token']
+        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
+        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        this_paper_history = []
+        for i, paper_frag in enumerate(paper_fragments):
+            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
+            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say,
+                inputs_show_user=i_say_show_user,
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot,
+                history=[],
+                sys_prompt="总结文章。"
+            )
+
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.extend([i_say_show_user,gpt_say])
+            this_paper_history.extend([i_say_show_user,gpt_say])
+
+        # 已经对该文章的所有片段总结完毕，如果文章被切分了，
+        if len(paper_fragments) > 1:
+            i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say,
+                inputs_show_user=i_say,
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot,
+                history=this_paper_history,
+                sys_prompt="总结文章。"
+            )
+
+            history.extend([i_say,gpt_say])
+            this_paper_history.extend([i_say,gpt_say])
+
+        res = write_history_to_file(history)
+        promote_file_to_downloadzone(res, chatbot=chatbot)
+        chatbot.append(("完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    res = write_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
+    chatbot.append(("所有文件都总结完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+@CatchException
+def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    import glob, os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        from docx import Document
+    except:
+        report_exception(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 搜索需要处理的文件清单
+    if txt.endswith('.docx') or txt.endswith('.doc'):
+        file_manifest = [txt]
+    else:
+        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
+                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
+
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 开始正式执行任务
+    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/批量文件询问.py

@@ -1,496 +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
-
-
-@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, plugin_kwargs: Dict, chatbot: List, history: List, system_prompt: str):
-        """初始化总结器"""
-        self.llm_kwargs = llm_kwargs
-        self.plugin_kwargs = plugin_kwargs
-        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.plugin_kwargs.get("advanced_arg"):
-                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.plugin_kwargs["advanced_arg"]}：'
-                         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
-
-        # 设置超时定时器
-        timer = threading.Timer(self.watch_dog_patience, 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("处理超时")
-
-            # 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
-
-            check_timeout()
-
-            # 更新状态
-            mutable_status[0] = "提取文件内容"
-            mutable_status[1] = time.time()
-
-            # 提取内容
-            content = extract_text(fp)
-            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
-            try:
-                paper_fragments = breakdown_text_to_satisfy_token_limit(
-                    txt=content,
-                    limit=self._get_token_limit(),
-                    llm_model=self.llm_kwargs['llm_model']
-                )
-            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
-
-            check_timeout()
-
-            # 处理片段
-            rel_path = os.path.relpath(fp, project_folder)
-            for i, frag in enumerate(paper_fragments):
-                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, "处理超时"))
-            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"
-                    else:
-                        status_str += f"文件 {file_name}: {status} {desc}\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
-        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)
-
-        # 保存 HTML
-        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)
-
-        # 保存 Word
-        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)
-
-        # 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
-
-    summarizer = BatchDocumentSummarizer(llm_kwargs, plugin_kwargs, 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
-    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)

crazy_functions/批量翻译PDF文档_NOUGAT.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, report_exception, get_log_folder, gen_time_str
-from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import update_ui, promote_file_to_downloadzone, update_ui_latest_msg, disable_auto_promotion
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency

crazy_functions/数学动画生成manim.py

@@ -166,7 +166,7 @@ class PointWithTrace(Scene):
 
 ```
 
-# do not use get_graph, this funciton is deprecated
+# do not use get_graph, this function is deprecated
 
 class ExampleFunctionGraph(Scene):
     def construct(self):

crazy_functions/生成多种Mermaid图表.py

@@ -324,16 +324,16 @@ def 生成多种Mermaid图表(
     if os.path.exists(txt):  # 如输入区无内容则直接解析历史记录
         from crazy_functions.pdf_fns.parse_word import extract_text_from_files
 
-        file_exist, final_result, page_one, file_manifest, excption = (
+        file_exist, final_result, page_one, file_manifest, exception = (
             extract_text_from_files(txt, chatbot, history)
         )
     else:
         file_exist = False
-        excption = ""
+        exception = ""
         file_manifest = []
 
-    if excption != "":
-        if excption == "word":
+    if exception != "":
+        if exception == "word":
             report_exception(
                 chatbot,
                 history,
@@ -341,7 +341,7 @@ def 生成多种Mermaid图表(
                 b=f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。",
             )
 
-        elif excption == "pdf":
+        elif exception == "pdf":
             report_exception(
                 chatbot,
                 history,
@@ -349,7 +349,7 @@ def 生成多种Mermaid图表(
                 b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。",
             )
 
-        elif excption == "word_pip":
+        elif exception == "word_pip":
             report_exception(
                 chatbot,
                 history,

crazy_functions/知识库问答.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
+from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_latest_msg, get_log_folder, get_user
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
 from loguru import logger
 install_msg ="""
@@ -42,7 +42,7 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         # from crazy_functions.crazy_utils import try_install_deps
         # try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
-        # yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
+        # yield from update_ui_latest_msg("安装完成，您可以再次重试。", chatbot, history)
         return
 
     # < --------------------读取文件--------------- >
@@ -95,7 +95,7 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         # from crazy_functions.crazy_utils import try_install_deps
         # try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
-        # yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
+        # yield from update_ui_latest_msg("安装完成，您可以再次重试。", chatbot, history)
         return
 
     # < -------------------  --------------- >

crazy_functions/虚空终端.py

@@ -47,7 +47,7 @@ explain_msg = """
 from pydantic import BaseModel, Field
 from typing import List
 from toolbox import CatchException, update_ui, is_the_upload_folder
-from toolbox import update_ui_lastest_msg, disable_auto_promotion
+from toolbox import update_ui_latest_msg, disable_auto_promotion
 from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from crazy_functions.crazy_utils import input_clipping
@@ -113,19 +113,19 @@ def 虚空终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
     # 用简单的关键词检测用户意图
     is_certain, _ = analyze_intention_with_simple_rules(txt)
     if is_the_upload_folder(txt):
-        state.set_state(chatbot=chatbot, key='has_provided_explaination', value=False)
+        state.set_state(chatbot=chatbot, key='has_provided_explanation', value=False)
         appendix_msg = "\n\n**很好，您已经上传了文件**，现在请您描述您的需求。"
 
-    if is_certain or (state.has_provided_explaination):
+    if is_certain or (state.has_provided_explanation):
         # 如果意图明确，跳过提示环节
-        state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
+        state.set_state(chatbot=chatbot, key='has_provided_explanation', value=True)
         state.unlock_plugin(chatbot=chatbot)
         yield from update_ui(chatbot=chatbot, history=history)
         yield from 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
         return
     else:
         # 如果意图模糊，提示
-        state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
+        state.set_state(chatbot=chatbot, key='has_provided_explanation', value=True)
         state.lock_plugin(chatbot=chatbot)
         chatbot.append(("虚空终端状态:", explain_msg+appendix_msg))
         yield from update_ui(chatbot=chatbot, history=history)
@@ -141,7 +141,7 @@ def 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     # ⭐ ⭐ ⭐ 分析用户意图
     is_certain, user_intention = analyze_intention_with_simple_rules(txt)
     if not is_certain:
-        yield from update_ui_lastest_msg(
+        yield from update_ui_latest_msg(
             lastmsg=f"正在执行任务: {txt}\n\n分析用户意图中", chatbot=chatbot, history=history, delay=0)
         gpt_json_io = GptJsonIO(UserIntention)
         rf_req = "\nchoose from ['ModifyConfiguration', 'ExecutePlugin', 'Chat']"
@@ -154,13 +154,13 @@ def 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
             user_intention = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
             lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 意图={explain_intention_to_user[user_intention.intention_type]}",
         except JsonStringError as e:
-            yield from update_ui_lastest_msg(
+            yield from update_ui_latest_msg(
                 lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 失败 当前语言模型（{llm_kwargs['llm_model']}）不能理解您的意图", chatbot=chatbot, history=history, delay=0)
             return
     else:
         pass
 
-    yield from update_ui_lastest_msg(
+    yield from update_ui_latest_msg(
         lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 意图={explain_intention_to_user[user_intention.intention_type]}",
         chatbot=chatbot, history=history, delay=0)
 

crazy_functions/语音助手.py

@@ -42,7 +42,7 @@ class AsyncGptTask():
             MAX_TOKEN_ALLO = 2560
             i_say, history = input_clipping(i_say, history, max_token_limit=MAX_TOKEN_ALLO)
             gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt,
-                                                            observe_window=observe_window[index], console_slience=True)
+                                                            observe_window=observe_window[index], console_silence=True)
         except ConnectionAbortedError as token_exceed_err:
             logger.error('至少一个线程任务Token溢出而失败', e)
         except Exception as e:

crazy_functions/谷歌检索小助手.py

@@ -1,6 +1,6 @@
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from toolbox import CatchException, report_exception, promote_file_to_downloadzone
-from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
+from toolbox import update_ui, update_ui_latest_msg, disable_auto_promotion, write_history_to_file
 import logging
 import requests
 import time
@@ -156,7 +156,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     history = []
     meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
     if len(meta_paper_info_list) == 0:
-        yield from update_ui_lastest_msg(lastmsg='获取文献失败，可能触发了google反爬虫机制。',chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_latest_msg(lastmsg='获取文献失败，可能触发了google反爬虫机制。',chatbot=chatbot, history=history, delay=0)
         return
     batchsize = 5
     for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):

docs/GithubAction+AllCapacity

@@ -5,6 +5,10 @@ FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
 
 # edge-tts需要的依赖，某些pip包所需的依赖
 RUN apt update && apt install ffmpeg build-essential -y
+RUN apt-get install -y fontconfig
+RUN ln -s /usr/local/texlive/2023/texmf-dist/fonts/truetype /usr/share/fonts/truetype/texlive
+RUN fc-cache -fv
+RUN apt-get clean
 
 # use python3 as the system default python
 WORKDIR /gpt
@@ -30,7 +34,7 @@ RUN python3 -m pip install -r request_llms/requirements_qwen.txt
 RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 RUN python3 -m pip install nougat-ocr
-
+RUN python3 -m pip cache purge
 
 # 预热Tiktoken模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'

docs/GithubAction+ChatGLM+Moss

@@ -7,6 +7,7 @@ RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
 
 # edge-tts需要的依赖，某些pip包所需的依赖
 RUN apt update && apt install ffmpeg build-essential -y
+RUN apt-get clean
 
 # use python3 as the system default python
 RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
@@ -22,6 +23,7 @@ RUN python3 -m pip install -r request_llms/requirements_moss.txt
 RUN python3 -m pip install -r request_llms/requirements_qwen.txt
 RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llms/requirements_newbing.txt
+RUN python3 -m pip cache purge
 
 
 # 预热Tiktoken模块

docs/GithubAction+NoLocal

@@ -18,5 +18,7 @@ RUN apt update && apt install ffmpeg -y
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 
+RUN python3 -m pip cache purge && apt-get clean
+
 # 启动
 CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal+Latex

@@ -30,5 +30,7 @@ COPY --chown=gptuser:gptuser . .
 # 可选步骤，用于预热模块
 RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 
+RUN python3 -m pip cache purge
+
 # 启动
 CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal+Vectordb

@@ -24,6 +24,8 @@ RUN apt update && apt install ffmpeg -y
 
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+RUN python3 -m pip cache purge && apt-get clean
+
 
 # 启动
 CMD ["python3", "-u", "main.py"]

docs/WindowsRun.bat

@@ -0,0 +1,26 @@
+@echo off
+setlocal
+
+:: 设置环境变量
+set ENV_NAME=gpt
+set ENV_PATH=%~dp0%ENV_NAME%
+set SCRIPT_PATH=%~dp0main.py
+
+:: 判断环境是否已解压
+if not exist "%ENV_PATH%" (
+    echo Extracting environment...
+    mkdir "%ENV_PATH%"
+    tar -xzf gpt.tar.gz -C "%ENV_PATH%"
+    
+    :: 运行conda环境激活脚本
+    call "%ENV_PATH%\Scripts\activate.bat"
+) else (
+    :: 如果环境已存在,直接激活
+    call "%ENV_PATH%\Scripts\activate.bat"
+)
+echo Start to run program:
+:: 运行Python脚本
+python "%SCRIPT_PATH%"
+
+endlocal
+pause

docs/translate_english.json

@@ -1141,7 +1141,7 @@
     "内容太长了都会触发token数量溢出的错误": "An error of token overflow will be triggered if the content is too long",
     "chatbot 为WebUI中显示的对话列表": "chatbot is the conversation list displayed in WebUI",
     "修改它": "Modify it",
-    "然后yeild出去": "Then yield it out",
+    "然后yield出去": "Then yield it out",
     "可以直接修改对话界面内容": "You can directly modify the conversation interface content",
     "additional_fn代表点击的哪个按钮": "additional_fn represents which button is clicked",
     "按钮见functional.py": "See functional.py for buttons",
@@ -1732,7 +1732,7 @@
     "或者重启之后再度尝试": "Or try again after restarting",
     "免费": "Free",
     "仅在Windows系统进行了测试": "Tested only on Windows system",
-    "欢迎加REAME中的QQ联系开发者": "Feel free to contact the developer via QQ in REAME",
+    "欢迎加README中的QQ联系开发者": "Feel free to contact the developer via QQ in README",
     "当前知识库内的有效文件": "Valid files in the current knowledge base",
     "您可以到Github Issue区": "You can go to the Github Issue area",
     "刷新Gradio前端界面": "Refresh the Gradio frontend interface",
@@ -1759,7 +1759,7 @@
     "报错信息如下. 如果是与网络相关的问题": "Error message as follows. If it is related to network issues",
     "功能描述": "Function description",
     "禁止移除或修改此警告": "Removal or modification of this warning is prohibited",
-    "Arixv翻译": "Arixv translation",
+    "ArXiv翻译": "ArXiv translation",
     "读取优先级": "Read priority",
     "包含documentclass关键字": "Contains the documentclass keyword",
     "根据文本使用GPT模型生成相应的图像": "Generate corresponding images using GPT model based on the text",
@@ -1998,7 +1998,7 @@
     "开始最终总结": "Start final summary",
     "openai的官方KEY需要伴随组织编码": "Openai's official KEY needs to be accompanied by organizational code",
     "将子线程的gpt结果写入chatbot": "Write the GPT result of the sub-thread into the chatbot",
-    "Arixv论文精细翻译": "Fine translation of Arixv paper",
+    "ArXiv论文精细翻译": "Fine translation of ArXiv paper",
     "开始接收chatglmft的回复": "Start receiving replies from chatglmft",
     "请先将.doc文档转换为.docx文档": "Please convert .doc documents to .docx documents first",
     "避免多用户干扰": "Avoid multiple user interference",
@@ -2360,7 +2360,7 @@
     "请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件": "Please set ALLOW_RESET_CONFIG=True in config.py and restart the software",
     "按照自然语言描述生成一个动画 | 输入参数是一段话": "Generate an animation based on natural language description | Input parameter is a sentence",
     "你的hf用户名如qingxu98": "Your hf username is qingxu98",
-    "Arixv论文精细翻译 | 输入参数arxiv论文的ID": "Fine translation of Arixv paper | Input parameter is the ID of arxiv paper",
+    "ArXiv论文精细翻译 | 输入参数arxiv论文的ID": "Fine translation of ArXiv paper | Input parameter is the ID of arxiv paper",
     "无法获取 abstract": "Unable to retrieve abstract",
     "尽可能地仅用一行命令解决我的要求": "Try to solve my request using only one command",
     "提取插件参数": "Extract plugin parameters",

docs/translate_japanese.json

@@ -753,7 +753,7 @@
     "手动指定和筛选源代码文件类型": "ソースコードファイルタイプを手動で指定およびフィルタリングする",
     "更多函数插件": "その他の関数プラグイン",
     "看门狗的耐心": "監視犬の忍耐力",
-    "然后yeild出去": "そして出力する",
+    "然后yield出去": "そして出力する",
     "拆分过长的IPynb文件": "長すぎるIPynbファイルを分割する",
     "1. 把input的余量留出来": "1. 入力の余裕を残す",
     "请求超时": "リクエストがタイムアウトしました",
@@ -1803,7 +1803,7 @@
     "默认值为1000": "デフォルト値は1000です",
     "写出文件": "ファイルに書き出す",
     "生成的视频文件路径": "生成されたビデオファイルのパス",
-    "Arixv论文精细翻译": "Arixv論文の詳細な翻訳",
+    "ArXiv论文精细翻译": "ArXiv論文の詳細な翻訳",
     "用latex编译为PDF对修正处做高亮": "LaTeXでコンパイルしてPDFに修正をハイライトする",
     "点击“停止”键可终止程序": "「停止」ボタンをクリックしてプログラムを終了できます",
     "否则将导致每个人的Claude问询历史互相渗透": "さもないと、各人のClaudeの問い合わせ履歴が相互に侵入します",
@@ -1987,7 +1987,7 @@
     "前面是中文逗号": "前面是中文逗号",
     "的依赖": "的依赖",
     "材料如下": "材料如下",
-    "欢迎加REAME中的QQ联系开发者": "欢迎加REAME中的QQ联系开发者",
+    "欢迎加README中的QQ联系开发者": "欢迎加README中的QQ联系开发者",
     "开始下载": "開始ダウンロード",
     "100字以内": "100文字以内",
     "创建request": "リクエストの作成",

docs/translate_traditionalchinese.json

@@ -771,7 +771,7 @@
     "查询代理的地理位置": "查詢代理的地理位置",
     "是否在输入过长时": "是否在輸入過長時",
     "chatGPT分析报告": "chatGPT分析報告",
-    "然后yeild出去": "然後yield出去",
+    "然后yield出去": "然後yield出去",
     "用户取消了程序": "使用者取消了程式",
     "琥珀色": "琥珀色",
     "这里是特殊函数插件的高级参数输入区": "這裡是特殊函數插件的高級參數輸入區",
@@ -1587,7 +1587,7 @@
     "否则将导致每个人的Claude问询历史互相渗透": "否則將導致每個人的Claude問詢歷史互相滲透",
     "提问吧! 但注意": "提問吧！但注意",
     "待处理的word文档路径": "待處理的word文檔路徑",
-    "欢迎加REAME中的QQ联系开发者": "歡迎加REAME中的QQ聯繫開發者",
+    "欢迎加README中的QQ联系开发者": "歡迎加README中的QQ聯繫開發者",
     "建议暂时不要使用": "建議暫時不要使用",
     "Latex没有安装": "Latex沒有安裝",
     "在这里放一些网上搜集的demo": "在這裡放一些網上搜集的demo",
@@ -1989,7 +1989,7 @@
     "请耐心等待": "請耐心等待",
     "在执行完成之后": "在執行完成之後",
     "参数简单": "參數簡單",
-    "Arixv论文精细翻译": "Arixv論文精細翻譯",
+    "ArXiv论文精细翻译": "ArXiv論文精細翻譯",
     "备份和下载": "備份和下載",
     "当前报错的latex代码处于第": "當前報錯的latex代碼處於第",
     "Markdown翻译": "Markdown翻譯",

main.py

@@ -1,10 +1,7 @@
-import os, json; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
+import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
 
 help_menu_description = \
-"""Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic),
-感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors).
-</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki),
-如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues).
+"""
 </br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交
 </br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮
 </br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮
@@ -34,7 +31,7 @@ def encode_plugin_info(k, plugin)->str:
 
 def main():
     import gradio as gr
-    if gr.__version__ not in ['3.32.9', '3.32.10', '3.32.11']:
+    if gr.__version__ not in ['3.32.12']:
         raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
 
     # 一些基础工具
@@ -49,7 +46,7 @@ def main():
     # 读取配置
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
-    ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME, ADD_WAIFU = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME', 'ADD_WAIFU')
+    ENABLE_AUDIO, AUTO_CLEAR_TXT, AVAIL_FONTS, AVAIL_THEMES, THEME, ADD_WAIFU = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'AVAIL_FONTS', 'AVAIL_THEMES', 'THEME', 'ADD_WAIFU')
     NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
     DARK_MODE, INIT_SYS_PROMPT, ADD_WAIFU, TTS_TYPE = get_conf('DARK_MODE', 'INIT_SYS_PROMPT', 'ADD_WAIFU', 'TTS_TYPE')
     if LLM_MODEL not in AVAIL_LLM_MODELS: AVAIL_LLM_MODELS += [LLM_MODEL]
@@ -57,8 +54,8 @@ def main():
     # 如果WEB_PORT是-1, 则随机选取WEB端口
     PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
     from check_proxy import get_current_version
-    from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_reset, js_code_show_or_hide, js_code_show_or_hide_group2
-    from themes.theme import js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
+    from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_show_or_hide
+    from themes.theme import js_code_for_toggle_darkmode
     from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
 
@@ -68,7 +65,7 @@ def main():
     functional = get_core_functions()
 
     # 高级函数插件
-    from crazy_functional import get_crazy_functions
+    from crazy_functional import get_crazy_functions, get_multiplex_button_functions
     DEFAULT_FN_GROUPS = get_conf('DEFAULT_FN_GROUPS')
     plugins = get_crazy_functions()
     all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')]))
@@ -106,7 +103,7 @@ def main():
             with gr_L2(scale=2, elem_id="gpt-chat"):
                 chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}", elem_id="gpt-chatbot")
                 if LAYOUT == "TOP-DOWN":  chatbot.style(height=CHATBOT_HEIGHT)
-                history, history_cache, history_cache_update = make_history_cache() # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
+                history, _, _ = make_history_cache() # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
             with gr_L2(scale=1, elem_id="gpt-panel"):
                 with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
                     with gr.Row():
@@ -114,12 +111,7 @@ def main():
                     with gr.Row(elem_id="gpt-submit-row"):
                         multiplex_submit_btn = gr.Button("提交", elem_id="elem_submit_visible", variant="primary")
                         multiplex_sel = gr.Dropdown(
-                            choices=[
-                                "常规对话", 
-                                "多模型对话", 
-                                "智能召回 RAG",
-                                # "智能上下文", 
-                            ], value="常规对话",
+                            choices=get_multiplex_button_functions().keys(), value="常规对话",
                             interactive=True, label='', show_label=False,
                             elem_classes='normal_mut_select', elem_id="gpt-submit-dropdown").style(container=False)
                         submit_btn = gr.Button("提交", elem_id="elem_submit", variant="primary", visible=False)
@@ -179,16 +171,20 @@ def main():
                         with gr.Accordion("点击展开“文件下载区”。", open=False) as area_file_up:
                             file_upload = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload")
 
+
         # 左上角工具栏定义
         from themes.gui_toolbar import define_gui_toolbar
         checkboxes, checkboxes_2, max_length_sl, theme_dropdown, system_prompt, file_upload_2, md_dropdown, top_p, temperature = \
-            define_gui_toolbar(AVAIL_LLM_MODELS, LLM_MODEL, INIT_SYS_PROMPT, THEME, AVAIL_THEMES, ADD_WAIFU, help_menu_description, js_code_for_toggle_darkmode)
+            define_gui_toolbar(AVAIL_LLM_MODELS, LLM_MODEL, INIT_SYS_PROMPT, THEME, AVAIL_THEMES, AVAIL_FONTS, ADD_WAIFU, help_menu_description, js_code_for_toggle_darkmode)
 
         # 浮动菜单定义
         from themes.gui_floating_menu import define_gui_floating_menu
         area_input_secondary, txt2, area_customize, _, resetBtn2, clearBtn2, stopBtn2 = \
             define_gui_floating_menu(customize_btns, functional, predefined_btns, cookies, web_cookie_cache)
 
+        # 浮动时间线定义
+        gr.Spark()
+
         # 插件二级菜单的实现
         from themes.gui_advanced_plugin_class import define_gui_advanced_plugin_class
         new_plugin_callback, route_switchy_bt_with_arg, usr_confirmed_arg = \
@@ -211,7 +207,7 @@ def main():
             ret.update({area_customize: gr.update(visible=("自定义菜单" in a))})
             return ret
         checkboxes_2.select(fn_area_visibility_2, [checkboxes_2], [area_customize] )
-        checkboxes_2.select(None, [checkboxes_2], None, _js=js_code_show_or_hide_group2)
+        checkboxes_2.select(None, [checkboxes_2], None, _js="""apply_checkbox_change_for_group2""")
 
         # 整理反复出现的控件句柄组合
         input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]
@@ -227,11 +223,8 @@ def main():
         multiplex_sel.select(
             None, [multiplex_sel], None, _js=f"""(multiplex_sel)=>run_multiplex_shift(multiplex_sel)""")
         cancel_handles.append(submit_btn.click(**predict_args))
-        resetBtn.click(None, None, [chatbot, history, status], _js=js_code_reset)   # 先在前端快速清除chatbot&status
-        resetBtn2.click(None, None, [chatbot, history, status], _js=js_code_reset)  # 先在前端快速清除chatbot&status
-        reset_server_side_args = (lambda history: ([], [], "已重置", json.dumps(history)), [history], [chatbot, history, status, history_cache])
-        resetBtn.click(*reset_server_side_args)    # 再在后端清除history，把history转存history_cache备用
-        resetBtn2.click(*reset_server_side_args)   # 再在后端清除history，把history转存history_cache备用
+        resetBtn.click(None, None, [chatbot, history, status], _js= """clear_conversation""")   # 先在前端快速清除chatbot&status
+        resetBtn2.click(None, None, [chatbot, history, status], _js="""clear_conversation""")  # 先在前端快速清除chatbot&status
         clearBtn.click(None, None, [txt, txt2], _js=js_code_clear)
         clearBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
         if AUTO_CLEAR_TXT:
@@ -331,7 +324,7 @@ def main():
         from shared_utils.cookie_manager import load_web_cookie_cache__fn_builder
         load_web_cookie_cache = load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)
         app_block.load(load_web_cookie_cache, inputs = [web_cookie_cache, cookies],
-            outputs = [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
+            outputs = [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()], _js="""persistent_cookie_init""")
         app_block.load(None, inputs=[], outputs=None, _js=f"""()=>GptAcademicJavaScriptInit("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}","{LAYOUT}","{TTS_TYPE}")""")    # 配置暗色主题或亮色主题
         app_block.load(None, inputs=[], outputs=None, _js="""()=>{REP}""".replace("REP", register_advanced_plugin_init_arr))
 

request_llms/bridge_all.py

@@ -26,6 +26,9 @@ from .bridge_chatglm import predict as chatglm_ui
 from .bridge_chatglm3 import predict_no_ui_long_connection as chatglm3_noui
 from .bridge_chatglm3 import predict as chatglm3_ui
 
+from .bridge_chatglm4 import predict_no_ui_long_connection as chatglm4_noui
+from .bridge_chatglm4 import predict as chatglm4_ui
+
 from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
 from .bridge_qianfan import predict as qianfan_ui
 
@@ -76,6 +79,7 @@ cohere_endpoint = "https://api.cohere.ai/v1/chat"
 ollama_endpoint = "http://localhost:11434/api/chat"
 yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
 deepseekapi_endpoint = "https://api.deepseek.com/v1/chat/completions"
+grok_model_endpoint = "https://api.x.ai/v1/chat/completions"
 
 if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
 azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
@@ -97,6 +101,7 @@ if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[coher
 if ollama_endpoint in API_URL_REDIRECT: ollama_endpoint = API_URL_REDIRECT[ollama_endpoint]
 if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
 if deepseekapi_endpoint in API_URL_REDIRECT: deepseekapi_endpoint = API_URL_REDIRECT[deepseekapi_endpoint]
+if grok_model_endpoint in API_URL_REDIRECT: grok_model_endpoint = API_URL_REDIRECT[grok_model_endpoint]
 
 # 获取tokenizer
 tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
@@ -212,6 +217,16 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
+    "chatgpt-4o-latest": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "has_multimodal_capacity": True,
+        "max_token": 128000,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+    },
+
     "gpt-4o-2024-05-13": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -258,7 +273,9 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
         "openai_disable_system_prompt": True,
         "openai_disable_stream": True,
+        "openai_force_temperature_one": True,
     },
+
     "o1-mini": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -268,6 +285,31 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
         "openai_disable_system_prompt": True,
         "openai_disable_stream": True,
+        "openai_force_temperature_one": True,
+    },
+
+    "o1-2024-12-17": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 200000,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+        "openai_disable_system_prompt": True,
+        "openai_disable_stream": True,
+        "openai_force_temperature_one": True,
+    },
+
+    "o1": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 200000,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+        "openai_disable_system_prompt": True,
+        "openai_disable_stream": True,
+        "openai_force_temperature_one": True,
     },
 
     "gpt-4-turbo": {
@@ -385,6 +427,14 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "glm-4-plus":{
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
 
     # api_2d (此后不需要在此处添加api2d的接口了，因为下面的代码会自动添加)
     "api2d-gpt-4": {
@@ -396,6 +446,7 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
+    # ChatGLM本地模型
     # 将 chatglm 直接对齐到 chatglm2
     "chatglm": {
         "fn_with_ui": chatglm_ui,
@@ -421,6 +472,14 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "chatglm4": {
+        "fn_with_ui": chatglm4_ui,
+        "fn_without_ui": chatglm4_noui,
+        "endpoint": None,
+        "max_token": 8192,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
     "qianfan": {
         "fn_with_ui": qianfan_ui,
         "fn_without_ui": qianfan_noui,
@@ -753,7 +812,8 @@ if "qwen-local" in AVAIL_LLM_MODELS:
     except:
         logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 通义-在线模型 -=-=-=-=-=-=-
-if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS:   # zhipuai
+qwen_models = ["qwen-max-latest", "qwen-max-2025-01-25","qwen-max","qwen-turbo","qwen-plus"]
+if any(item in qwen_models for item in AVAIL_LLM_MODELS):
     try:
         from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
         from .bridge_qwen import predict as qwen_ui
@@ -763,7 +823,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
                 "fn_without_ui": qwen_noui,
                 "can_multi_thread": True,
                 "endpoint": None,
-                "max_token": 6144,
+                "max_token": 100000,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
             },
@@ -772,7 +832,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
                 "fn_without_ui": qwen_noui,
                 "can_multi_thread": True,
                 "endpoint": None,
-                "max_token": 30720,
+                "max_token": 129024,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
             },
@@ -781,7 +841,25 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
                 "fn_without_ui": qwen_noui,
                 "can_multi_thread": True,
                 "endpoint": None,
-                "max_token": 28672,
+                "max_token": 30720,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "qwen-max-latest": {
+                "fn_with_ui": qwen_ui,
+                "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
+                "endpoint": None,
+                "max_token": 30720,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "qwen-max-2025-01-25": {
+                "fn_with_ui": qwen_ui,
+                "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
+                "endpoint": None,
+                "max_token": 30720,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
             }
@@ -868,6 +946,31 @@ if any(item in yi_models for item in AVAIL_LLM_MODELS):
         })
     except:
         logger.error(trimmed_format_exc())
+
+
+# -=-=-=-=-=-=- Grok model from x.ai -=-=-=-=-=-=-
+grok_models = ["grok-beta"]
+if any(item in grok_models for item in AVAIL_LLM_MODELS):
+    try:
+        grok_beta_128k_noui, grok_beta_128k_ui = get_predict_function(
+            api_key_conf_name="GROK_API_KEY", max_output_token=8192, disable_proxy=False
+            )
+        
+        model_info.update({
+            "grok-beta": {
+                "fn_with_ui": grok_beta_128k_ui,
+                "fn_without_ui": grok_beta_128k_noui,
+                "can_multi_thread": True,  
+                "endpoint": grok_model_endpoint,
+                "max_token": 128000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            
+        })
+    except:
+        logger.error(trimmed_format_exc())
+
 # -=-=-=-=-=-=- 讯飞星火认知大模型 -=-=-=-=-=-=-
 if "spark" in AVAIL_LLM_MODELS:
     try:
@@ -969,7 +1072,7 @@ if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容
         })
     except:
         logger.error(trimmed_format_exc())
-# -=-=-=-=-=-=- 幻方-深度求索大模型 -=-=-=-=-=-=-
+# -=-=-=-=-=-=- 幻方-深度求索本地大模型 -=-=-=-=-=-=-
 if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     try:
         from .bridge_deepseekcoder import predict_no_ui_long_connection as deepseekcoder_noui
@@ -987,7 +1090,7 @@ if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     except:
         logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 幻方-深度求索大模型在线API -=-=-=-=-=-=-
-if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
+if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS or "deepseek-reasoner" in AVAIL_LLM_MODELS:
     try:
         deepseekapi_noui, deepseekapi_ui = get_predict_function(
             api_key_conf_name="DEEPSEEK_API_KEY", max_output_token=4096, disable_proxy=False
@@ -998,7 +1101,7 @@ if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
                 "fn_without_ui": deepseekapi_noui,
                 "endpoint": deepseekapi_endpoint,
                 "can_multi_thread": True,
-                "max_token": 32000,
+                "max_token": 64000,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
             },
@@ -1011,6 +1114,16 @@ if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
             },
+            "deepseek-reasoner":{
+                "fn_with_ui": deepseekapi_ui,
+                "fn_without_ui": deepseekapi_noui,
+                "endpoint": deepseekapi_endpoint,
+                "can_multi_thread": True,
+                "max_token": 64000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+                "enable_reasoning": True
+            },
         })
     except:
         logger.error(trimmed_format_exc())
@@ -1152,9 +1265,9 @@ def LLM_CATCH_EXCEPTION(f):
     """
     装饰器函数，将错误显示出来
     """
-    def decorated(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_slience:bool):
+    def decorated(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_silence:bool):
         try:
-            return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
+            return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_silence)
         except Exception as e:
             tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
             observe_window[0] = tb_str
@@ -1162,7 +1275,7 @@ def LLM_CATCH_EXCEPTION(f):
     return decorated
 
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_silence:bool=False):
     """
     发送至LLM，等待回复，一次性完成，不显示中间过程。但内部（尽可能地）用stream的方法避免中途网线被掐。
     inputs：
@@ -1184,7 +1297,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
     if '&' not in model:
         # 如果只询问“一个”大语言模型（多数情况）：
         method = model_info[model]["fn_without_ui"]
-        return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
+        return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_silence)
     else:
         # 如果同时询问“多个”大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
         executor = ThreadPoolExecutor(max_workers=4)
@@ -1201,7 +1314,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
             method = model_info[model]["fn_without_ui"]
             llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
             llm_kwargs_feedin['llm_model'] = model
-            future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
+            future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_silence)
             futures.append(future)
 
         def mutex_manager(window_mutex, observe_window):
@@ -1278,6 +1391,11 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot,
 
     inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")
 
+    if llm_kwargs['llm_model'] not in model_info:
+        from toolbox import update_ui
+        chatbot.append([inputs, f"很抱歉，模型 '{llm_kwargs['llm_model']}' 暂不支持<br/>(1) 检查config中的AVAIL_LLM_MODELS选项<br/>(2) 检查request_llms/bridge_all.py中的模型路由"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
     method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]  # 如果这里报错，检查config中的AVAIL_LLM_MODELS选项
 
     if additional_fn: # 根据基础功能区 ModelOverride 参数调整模型类型

request_llms/bridge_chatglm3.py

@@ -23,39 +23,33 @@ class GetGLM3Handle(LocalLLMHandle):
         import os
         import platform
 
-        LOCAL_MODEL_QUANT, device = get_conf("LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
-        _model_name_ = "THUDM/chatglm3-6b"
-        # if LOCAL_MODEL_QUANT == "INT4":  # INT4
-        #     _model_name_ = "THUDM/chatglm3-6b-int4"
-        # elif LOCAL_MODEL_QUANT == "INT8":  # INT8
-        #     _model_name_ = "THUDM/chatglm3-6b-int8"
-        # else:
-        #     _model_name_ = "THUDM/chatglm3-6b"  # FP16
+        LOCAL_MODEL_PATH, LOCAL_MODEL_QUANT, device = get_conf("CHATGLM_LOCAL_MODEL_PATH", "LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
+        model_path = LOCAL_MODEL_PATH
         with ProxyNetworkActivate("Download_LLM"):
             chatglm_tokenizer = AutoTokenizer.from_pretrained(
-                _model_name_, trust_remote_code=True
+                model_path, trust_remote_code=True
             )
             if device == "cpu":
                 chatglm_model = AutoModel.from_pretrained(
-                    _model_name_,
+                    model_path,
                     trust_remote_code=True,
                     device="cpu",
                 ).float()
             elif LOCAL_MODEL_QUANT == "INT4":  # INT4
                 chatglm_model = AutoModel.from_pretrained(
-                    pretrained_model_name_or_path=_model_name_,
+                    pretrained_model_name_or_path=model_path,
                     trust_remote_code=True,
                     quantization_config=BitsAndBytesConfig(load_in_4bit=True),
                 )
             elif LOCAL_MODEL_QUANT == "INT8":  # INT8
                 chatglm_model = AutoModel.from_pretrained(
-                    pretrained_model_name_or_path=_model_name_,
+                    pretrained_model_name_or_path=model_path,
                     trust_remote_code=True,
                     quantization_config=BitsAndBytesConfig(load_in_8bit=True),
                 )
             else:
                 chatglm_model = AutoModel.from_pretrained(
-                    pretrained_model_name_or_path=_model_name_,
+                    pretrained_model_name_or_path=model_path,
                     trust_remote_code=True,
                     device="cuda",
                 )

request_llms/bridge_chatglm4.py

@@ -0,0 +1,81 @@
+model_name = "ChatGLM4"
+cmd_to_install = """
+`pip install -r request_llms/requirements_chatglm4.txt`
+`pip install modelscope`
+`modelscope download --model ZhipuAI/glm-4-9b-chat --local_dir ./THUDM/glm-4-9b-chat`
+"""
+
+
+from toolbox import get_conf, ProxyNetworkActivate
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+class GetGLM4Handle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        import torch
+        from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
+        import os
+
+        LOCAL_MODEL_PATH, device = get_conf("CHATGLM_LOCAL_MODEL_PATH", "LOCAL_MODEL_DEVICE")
+        model_path = LOCAL_MODEL_PATH
+        chatglm_tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+        chatglm_model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            torch_dtype=torch.bfloat16,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True,
+            device=device
+        ).eval().to(device)
+        self._model = chatglm_model
+        self._tokenizer = chatglm_tokenizer
+        return self._model, self._tokenizer
+
+
+    def llm_stream_generator(self, **kwargs):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        def adaptor(kwargs):
+            query = kwargs["query"]
+            max_length = kwargs["max_length"]
+            top_p = kwargs["top_p"]
+            temperature = kwargs["temperature"]
+            history = kwargs["history"]
+            return query, max_length, top_p, temperature, history
+
+        query, max_length, top_p, temperature, history = adaptor(kwargs)
+        inputs = self._tokenizer.apply_chat_template([{"role": "user", "content": query}],
+                                       add_generation_prompt=True,
+                                       tokenize=True,
+                                       return_tensors="pt",
+                                       return_dict=True
+                                       ).to(self._model.device)
+        gen_kwargs = {"max_length": max_length, "do_sample": True, "top_k": top_p}
+
+        outputs = self._model.generate(**inputs, **gen_kwargs)
+        outputs = outputs[:, inputs['input_ids'].shape[1]:]
+        response = self._tokenizer.decode(outputs[0], skip_special_tokens=True)
+        yield response
+
+    def try_to_import_special_deps(self, **kwargs):
+        # import something that will raise error if the user does not install requirement_*.txt
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
+        import importlib
+
+        # importlib.import_module('modelscope')
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 GPT-Academic Interface
+# ------------------------------------------------------------------------------------------------------------------------
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(
+    GetGLM4Handle, model_name, history_format="chatglm3"
+)

request_llms/bridge_chatglmft.py

@@ -139,7 +139,7 @@ global glmft_handle
 glmft_handle = None
 #################################################################################
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_chatgpt.py

@@ -23,8 +23,13 @@ from loguru import logger
 from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
 from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
 from toolbox import ChatBotWithCookies, have_any_recent_upload_image_files, encode_image
-proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
-    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
+proxies, WHEN_TO_USE_PROXY, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
+    get_conf('proxies', 'WHEN_TO_USE_PROXY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
+
+if "Connect_OpenAI" not in WHEN_TO_USE_PROXY:
+    if proxies is not None:
+        logger.error("虽然您配置了代理设置，但不会在连接OpenAI的过程中起作用，请检查WHEN_TO_USE_PROXY配置。")
+        proxies = None
 
 timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
                   '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
@@ -120,7 +125,7 @@ def verify_endpoint(endpoint):
         raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
     return endpoint
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_silence:bool=False):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -180,19 +185,25 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
                 raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
             else:
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
-        if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
+        if ('data: [DONE]' in chunk_decoded): break # api2d & one-api 正常完成
         # 提前读取一些信息 （用于判断异常）
         if has_choices and not choice_valid:
             # 一些垃圾第三方接口的出现这样的错误
             continue
         json_data = chunkjson['choices'][0]
         delta = json_data["delta"]
-        if len(delta) == 0: break
+
+        if len(delta) == 0:
+            is_termination_certain = False
+            if (has_choices) and (chunkjson['choices'][0].get('finish_reason', 'null') == 'stop'): is_termination_certain = True
+            if is_termination_certain: break
+            else: continue # 对于不符合规范的狗屎接口，这里需要继续
+
         if (not has_content) and has_role: continue
         if (not has_content) and (not has_role): continue # raise RuntimeError("发现不标准的第三方接口："+delta)
         if has_content: # has_role = True/False
             result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
+            if not console_silence: print(delta["content"], end='')
             if observe_window is not None:
                 # 观测窗，把已经获取的数据显示出去
                 if len(observe_window) >= 1:
@@ -220,7 +231,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     inputs 是本次问询的输入
     top_p, temperature是chatGPT的内部调优参数
     history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     from request_llms.bridge_all import model_info
@@ -285,6 +296,8 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         history.extend([inputs, ""])
 
     retry = 0
+    previous_ui_reflesh_time = 0
+    ui_reflesh_min_interval = 0.0
     while True:
         try:
             # make a POST request to the API endpoint, stream=True
@@ -297,13 +310,13 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
             yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
             if retry > MAX_RETRY: raise TimeoutError
 
-
     if not stream:
         # 该分支仅适用于不支持stream的o1模型，其他情形一律不适用
         yield from handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history)
         return
 
     if stream:
+        reach_termination = False   # 处理一些 new-api 的奇葩异常
         gpt_replying_buffer = ""
         is_head_of_the_stream = True
         stream_response =  response.iter_lines()
@@ -316,11 +329,14 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                 error_msg = chunk_decoded
                 # 首先排除一个one-api没有done数据包的第三方Bug情形
                 if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0:
-                    yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。")
+                    yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的接口，建议选择更稳定的接口。")
+                    if not reach_termination:
+                        reach_termination = True
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                     break
                 # 其他情况，直接返回报错
                 chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面
+                yield from update_ui(chatbot=chatbot, history=history, msg="接口返回了错误:" + chunk.decode()) # 刷新界面
                 return
 
             # 提前读取一些信息 （用于判断异常）
@@ -330,6 +346,8 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                 # 数据流的第一帧不携带content
                 is_head_of_the_stream = False; continue
 
+            if "error" in chunk_decoded: logger.error(f"接口返回了未知错误: {chunk_decoded}")
+
             if chunk:
                 try:
                     if has_choices and not choice_valid:
@@ -338,14 +356,25 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     if ('data: [DONE]' not in chunk_decoded) and len(chunk_decoded) > 0 and (chunkjson is None):
                         # 传递进来一些奇怪的东西
                         raise ValueError(f'无法读取以下数据，请检查配置。\n\n{chunk_decoded}')
-                    # 前者是API2D的结束条件，后者是OPENAI的结束条件
-                    if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
-                        # 判定为数据流的结束，gpt_replying_buffer也写完了
+                    # 前者是API2D & One-API的结束条件，后者是OPENAI的结束条件
+                    one_api_terminate = ('data: [DONE]' in chunk_decoded)
+                    openai_terminate = (has_choices) and (len(chunkjson['choices'][0]["delta"]) == 0)
+                    if one_api_terminate or openai_terminate:
+                        is_termination_certain = False
+                        if one_api_terminate: is_termination_certain = True # 抓取符合规范的结束条件
+                        elif (has_choices) and (chunkjson['choices'][0].get('finish_reason', 'null') == 'stop'): is_termination_certain = True # 抓取符合规范的结束条件
+                        if is_termination_certain:
+                            reach_termination = True
                             log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
-                        break
-                    # 处理数据流的主体
+                            break # 对于符合规范的接口，这里可以break
+                        else:
+                            continue # 对于不符合规范的狗屎接口，这里需要继续
+                    # 到这里，我们已经可以假定必须包含choice了
+                    try:
                         status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
-                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
+                    except:
+                        logger.error(f"一些垃圾第三方接口出现这样的错误，兼容一下吧: {chunk_decoded}")
+                    # 处理数据流的主体
                     if has_content:
                         # 正常情况
                         gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
@@ -354,21 +383,26 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                         continue
                     else:
                         # 至此已经超出了正常接口应该进入的范围，一些垃圾第三方接口会出现这样的错误
-                        if chunkjson['choices'][0]["delta"]["content"] is None: continue # 一些垃圾第三方接口出现这样的错误，兼容一下吧
+                        if chunkjson['choices'][0]["delta"].get("content", None) is None:
+                            logger.error(f"一些垃圾第三方接口出现这样的错误，兼容一下吧: {chunk_decoded}")
+                            continue
                         gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
 
                     history[-1] = gpt_replying_buffer
                     chatbot[-1] = (history[-2], history[-1])
+                    if time.time() - previous_ui_reflesh_time > ui_reflesh_min_interval:
                         yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
+                        previous_ui_reflesh_time = time.time()
                 except Exception as e:
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
                     chunk = get_full_error(chunk, stream_response)
                     chunk_decoded = chunk.decode()
                     error_msg = chunk_decoded
                     chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析异常" + error_msg) # 刷新界面
                     logger.error(error_msg)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析异常" + error_msg) # 刷新界面
                     return
+        yield from update_ui(chatbot=chatbot, history=history, msg="完成") # 刷新界面
         return  # return from stream-branch
 
 def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
@@ -536,6 +570,8 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
         "n": 1,
         "stream": stream,
     }
-
+    openai_force_temperature_one = model_info[llm_kwargs['llm_model']].get('openai_force_temperature_one', False)
+    if openai_force_temperature_one:
+        payload.pop('temperature')
     return headers,payload
 

request_llms/bridge_chatgpt_vision.py

@@ -16,7 +16,7 @@ import base64
 import glob
 from loguru import logger
 from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, \
-    update_ui_lastest_msg, get_max_token, encode_image, have_any_recent_upload_image_files, log_chat
+    update_ui_latest_msg, get_max_token, encode_image, have_any_recent_upload_image_files, log_chat
 
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
@@ -67,7 +67,7 @@ def verify_endpoint(endpoint):
     """
     return endpoint
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_silence=False):
     raise NotImplementedError
 
 
@@ -183,7 +183,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
                         lastmsg = chatbot[-1][-1] + f"\n\n\n\n「{llm_kwargs['llm_model']}调用结束，该模型不具备上下文对话能力，如需追问，请及时切换模型。」"
-                        yield from update_ui_lastest_msg(lastmsg, chatbot, history, delay=1)
+                        yield from update_ui_latest_msg(lastmsg, chatbot, history, delay=1)
                         log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                         break
                     # 处理数据流的主体

request_llms/bridge_claude.py

@@ -69,7 +69,7 @@ def decode_chunk(chunk):
     return need_to_pass, chunkjson, is_last_chunk
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_silence=False):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -151,7 +151,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     inputs 是本次问询的输入
     top_p, temperature是chatGPT的内部调优参数
     history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     if inputs == "":     inputs = "空空如也的输入栏"

request_llms/bridge_cohere.py

@@ -68,7 +68,7 @@ def verify_endpoint(endpoint):
         raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
     return endpoint
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_silence:bool=False):
     """
     发送，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -111,7 +111,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
         if chunkjson['event_type'] == 'stream-start': continue
         if chunkjson['event_type'] == 'text-generation':
             result += chunkjson["text"]
-            if not console_slience: print(chunkjson["text"], end='')
+            if not console_silence: print(chunkjson["text"], end='')
             if observe_window is not None:
                 # 观测窗，把已经获取的数据显示出去
                 if len(observe_window) >= 1:
@@ -132,7 +132,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     inputs 是本次问询的输入
     top_p, temperature是chatGPT的内部调优参数
     history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     # if is_any_api_key(inputs):

request_llms/bridge_deepseekcoder.py

@@ -6,7 +6,6 @@ from toolbox import get_conf
 from request_llms.local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 from threading import Thread
 from loguru import logger
-import torch
 import os
 
 def download_huggingface_model(model_name, max_retry, local_dir):
@@ -29,6 +28,7 @@ class GetCoderLMHandle(LocalLLMHandle):
         self.cmd_to_install = cmd_to_install
 
     def load_model_and_tokenizer(self):
+        import torch
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         with ProxyNetworkActivate('Download_LLM'):
             from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer

request_llms/bridge_google_gemini.py

@@ -8,7 +8,7 @@ import os
 import time
 from request_llms.com_google import GoogleChatInit
 from toolbox import ChatBotWithCookies
-from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat, encode_image
+from toolbox import get_conf, update_ui, update_ui_latest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat, encode_image
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
 timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
@@ -16,7 +16,7 @@ timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check
 
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[],
-                                  console_slience:bool=False):
+                                  console_silence:bool=False):
     # 检查API_KEY
     if get_conf("GEMINI_API_KEY") == "":
         raise ValueError(f"请配置 GEMINI_API_KEY。")
@@ -60,7 +60,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
 
     # 检查API_KEY
     if get_conf("GEMINI_API_KEY") == "":
-        yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_latest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
         return
 
     # 适配润色区域

request_llms/bridge_jittorllms_llama.py

@@ -55,7 +55,7 @@ class GetGLMHandle(Process):
                 if self.jittorllms_model is None:
                     device = get_conf('LOCAL_MODEL_DEVICE')
                     from .jittorllms.models import get_model
-                    # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
+                    # available_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
                     args_dict = {'model': 'llama'}
                     print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
                     self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
@@ -107,7 +107,7 @@ global llama_glm_handle
 llama_glm_handle = None
 #################################################################################
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_jittorllms_pangualpha.py

@@ -55,7 +55,7 @@ class GetGLMHandle(Process):
                 if self.jittorllms_model is None:
                     device = get_conf('LOCAL_MODEL_DEVICE')
                     from .jittorllms.models import get_model
-                    # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
+                    # available_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
                     args_dict = {'model': 'pangualpha'}
                     print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
                     self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
@@ -107,7 +107,7 @@ global pangu_glm_handle
 pangu_glm_handle = None
 #################################################################################
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_jittorllms_rwkv.py

@@ -55,7 +55,7 @@ class GetGLMHandle(Process):
                 if self.jittorllms_model is None:
                     device = get_conf('LOCAL_MODEL_DEVICE')
                     from .jittorllms.models import get_model
-                    # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
+                    # available_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
                     args_dict = {'model': 'chatrwkv'}
                     print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
                     self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
@@ -107,7 +107,7 @@ global rwkv_glm_handle
 rwkv_glm_handle = None
 #################################################################################
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_llama2.py

@@ -26,7 +26,7 @@ class GetLlamaHandle(LocalLLMHandle):
         import platform
         huggingface_token, device = get_conf('HUGGINGFACE_ACCESS_TOKEN', 'LOCAL_MODEL_DEVICE')
         assert len(huggingface_token) != 0, "没有填写 HUGGINGFACE_ACCESS_TOKEN"
-        with open(os.path.expanduser('~/.cache/huggingface/token'), 'w') as f:
+        with open(os.path.expanduser('~/.cache/huggingface/token'), 'w', encoding='utf8') as f:
             f.write(huggingface_token)
         model_id = 'meta-llama/Llama-2-7b-chat-hf'
         with ProxyNetworkActivate('Download_LLM'):
@@ -46,8 +46,8 @@ class GetLlamaHandle(LocalLLMHandle):
             top_p = kwargs['top_p']
             temperature = kwargs['temperature']
             history = kwargs['history']
-            console_slience = kwargs.get('console_slience', True)
-            return query, max_length, top_p, temperature, history, console_slience
+            console_silence = kwargs.get('console_silence', True)
+            return query, max_length, top_p, temperature, history, console_silence
 
         def convert_messages_to_prompt(query, history):
             prompt = ""
@@ -57,7 +57,7 @@ class GetLlamaHandle(LocalLLMHandle):
             prompt += f"\n[INST]{query}[/INST]"
             return prompt
 
-        query, max_length, top_p, temperature, history, console_slience = adaptor(kwargs)
+        query, max_length, top_p, temperature, history, console_silence = adaptor(kwargs)
         prompt = convert_messages_to_prompt(query, history)
         # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
         # code from transformers.llama
@@ -72,9 +72,9 @@ class GetLlamaHandle(LocalLLMHandle):
         generated_text = ""
         for new_text in streamer:
             generated_text += new_text
-            if not console_slience: print(new_text, end='')
+            if not console_silence: print(new_text, end='')
             yield generated_text.lstrip(prompt_tk_back).rstrip("</s>")
-        if not console_slience: print()
+        if not console_silence: print()
         # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
 
     def try_to_import_special_deps(self, **kwargs):

request_llms/bridge_moonshot.py

@@ -31,7 +31,7 @@ class MoonShotInit:
                     files.append(f)
                 for file in files:
                     if file.split('.')[-1] in ['pdf']:
-                        with open(file, 'r') as fp:
+                        with open(file, 'r', encoding='utf8') as fp:
                             from crazy_functions.crazy_utils import read_and_clean_pdf_text
                             file_content, _ = read_and_clean_pdf_text(fp)
                         what_ask.append({"role": "system", "content": file_content})
@@ -169,7 +169,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result)
 
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
-                                  console_slience=False):
+                                  console_silence=False):
     gpt_bro_init = MoonShotInit()
     watch_dog_patience = 60  # 看门狗的耐心, 设置10秒即可
     stream_response = gpt_bro_init.generate_messages(inputs, llm_kwargs, history, sys_prompt, True)

request_llms/bridge_moss.py

@@ -95,7 +95,7 @@ class GetGLMHandle(Process):
         - Its responses must not be vague, accusatory, rude, controversial, off-topic, or defensive.
         - It should avoid giving subjective opinions but rely on objective facts or phrases like \"in this context a human might say...\", \"some people might think...\", etc.
         - Its responses must also be positive, polite, interesting, entertaining, and engaging.
-        - It can provide additional relevant details to answer in-depth and comprehensively covering mutiple aspects.
+        - It can provide additional relevant details to answer in-depth and comprehensively covering multiple aspects.
         - It apologizes and accepts the user's suggestion if the user corrects the incorrect answer generated by MOSS.
         Capabilities and tools that MOSS can possess.
         """
@@ -172,7 +172,7 @@ global moss_handle
 moss_handle = None
 #################################################################################
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_newbingfree.py

@@ -209,7 +209,7 @@ def predict_no_ui_long_connection(
     history=[],
     sys_prompt="",
     observe_window=[],
-    console_slience=False,
+    console_silence=False,
 ):
     """
     多线程方法

request_llms/bridge_ollama.py

@@ -52,7 +52,7 @@ def decode_chunk(chunk):
         pass
     return chunk_decoded, chunkjson, is_last_chunk
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_silence=False):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -75,7 +75,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             # make a POST request to the API endpoint, stream=False
             from .bridge_all import model_info
             endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, proxies=proxies,
+            response = requests.post(endpoint, headers=headers, proxies=None,
                                     json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
         except requests.exceptions.ReadTimeout as e:
             retry += 1
@@ -99,7 +99,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                     logger.info(f'[response] {result}')
                     break
                 result += chunkjson['message']["content"]
-                if not console_slience: print(chunkjson['message']["content"], end='')
+                if not console_silence: print(chunkjson['message']["content"], end='')
                 if observe_window is not None:
                     # 观测窗，把已经获取的数据显示出去
                     if len(observe_window) >= 1:
@@ -124,7 +124,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     inputs 是本次问询的输入
     top_p, temperature是chatGPT的内部调优参数
     history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     if inputs == "":     inputs = "空空如也的输入栏"
@@ -152,10 +152,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     history.append(inputs); history.append("")
 
     retry = 0
+    if proxies is not None:
+        logger.error("Ollama不会使用代理服务器, 忽略了proxies的设置。")
     while True:
         try:
             # make a POST request to the API endpoint, stream=True
-            response = requests.post(endpoint, headers=headers, proxies=proxies,
+            response = requests.post(endpoint, headers=headers, proxies=None,
                                     json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
         except:
             retry += 1

request_llms/bridge_openrouter.py

@@ -119,7 +119,7 @@ def verify_endpoint(endpoint):
         raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
     return endpoint
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_silence:bool=False):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -170,7 +170,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
         except requests.exceptions.ConnectionError:
             chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
         chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
-        if len(chunk_decoded)==0: continue
+        if len(chunk_decoded)==0 or chunk_decoded.startswith(':'): continue
         if not chunk_decoded.startswith('data:'):
             error_msg = get_full_error(chunk, stream_response).decode()
             if "reduce the length" in error_msg:
@@ -181,9 +181,6 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
         if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
         # 提前读取一些信息 （用于判断异常）
-        if (has_choices and not choice_valid) or ('OPENROUTER PROCESSING' in chunk_decoded):
-            # 一些垃圾第三方接口的出现这样的错误，openrouter的特殊处理
-            continue
         json_data = chunkjson['choices'][0]
         delta = json_data["delta"]
         if len(delta) == 0: break
@@ -191,7 +188,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
         if (not has_content) and (not has_role): continue # raise RuntimeError("发现不标准的第三方接口："+delta)
         if has_content: # has_role = True/False
             result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
+            if not console_silence: print(delta["content"], end='')
             if observe_window is not None:
                 # 观测窗，把已经获取的数据显示出去
                 if len(observe_window) >= 1:
@@ -216,7 +213,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     inputs 是本次问询的输入
     top_p, temperature是chatGPT的内部调优参数
     history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     from request_llms.bridge_all import model_info
@@ -328,8 +325,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
 
             if chunk:
                 try:
-                    if (has_choices and not choice_valid) or ('OPENROUTER PROCESSING' in chunk_decoded):
-                        # 一些垃圾第三方接口的出现这样的错误, 或者OPENROUTER的特殊处理,因为OPENROUTER的数据流未连接到模型时会出现OPENROUTER PROCESSING
+                    if (has_choices and not choice_valid) or chunk_decoded.startswith(':'):
                         continue
                     if ('data: [DONE]' not in chunk_decoded) and len(chunk_decoded) > 0 and (chunkjson is None):
                         # 传递进来一些奇怪的东西
@@ -516,7 +512,7 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
         model, _ = read_one_api_model_name(model)
     if llm_kwargs['llm_model'].startswith('openrouter-'):
         model = llm_kwargs['llm_model'][len('openrouter-'):]
-        model= read_one_api_model_name(model)
+        model, _= read_one_api_model_name(model)
     if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制
         model = random.choice([
             "gpt-3.5-turbo",

request_llms/bridge_qianfan.py

@@ -121,7 +121,7 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
 
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_qwen.py

@@ -1,12 +1,12 @@
 import time
 import os
-from toolbox import update_ui, get_conf, update_ui_lastest_msg
+from toolbox import update_ui, get_conf, update_ui_latest_msg
 from toolbox import check_packages, report_exception, log_chat
 
 model_name = 'Qwen'
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -35,13 +35,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     try:
         check_packages(["dashscope"])
     except:
-        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade dashscope```。",
+        yield from update_ui_latest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade dashscope```。",
                                          chatbot=chatbot, history=history, delay=0)
         return
 
     # 检查DASHSCOPE_API_KEY
     if get_conf("DASHSCOPE_API_KEY") == "":
-        yield from update_ui_lastest_msg(f"请配置 DASHSCOPE_API_KEY。",
+        yield from update_ui_latest_msg(f"请配置 DASHSCOPE_API_KEY。",
                                          chatbot=chatbot, history=history, delay=0)
         return
 

request_llms/bridge_skylark2.py

@@ -1,5 +1,5 @@
 import time
-from toolbox import update_ui, get_conf, update_ui_lastest_msg
+from toolbox import update_ui, get_conf, update_ui_latest_msg
 from toolbox import check_packages, report_exception
 
 model_name = '云雀大模型'
@@ -10,7 +10,7 @@ def validate_key():
     return True
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         ⭐ 多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -42,12 +42,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     try:
         check_packages(["zhipuai"])
     except:
-        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
+        yield from update_ui_latest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
                                          chatbot=chatbot, history=history, delay=0)
         return
 
     if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置HUOSHAN_API_KEY", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_latest_msg(lastmsg="[Local Message] 请配置HUOSHAN_API_KEY", chatbot=chatbot, history=history, delay=0)
         return
 
     if additional_fn is not None:

request_llms/bridge_spark.py

@@ -2,7 +2,7 @@
 import time
 import threading
 import importlib
-from toolbox import update_ui, get_conf, update_ui_lastest_msg
+from toolbox import update_ui, get_conf, update_ui_latest_msg
 from multiprocessing import Process, Pipe
 
 model_name = '星火认知大模型'
@@ -14,7 +14,7 @@ def validate_key():
     return True
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -43,7 +43,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     yield from update_ui(chatbot=chatbot, history=history)
 
     if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_latest_msg(lastmsg="[Local Message] 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
         return
 
     if additional_fn is not None:

request_llms/bridge_stackclaude.py

@@ -225,7 +225,7 @@ def predict_no_ui_long_connection(
     history=[],
     sys_prompt="",
     observe_window=None,
-    console_slience=False,
+    console_silence=False,
 ):
     """
     多线程方法

request_llms/bridge_taichu.py

@@ -1,6 +1,6 @@
 import time
 import os
-from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
+from toolbox import update_ui, get_conf, update_ui_latest_msg, log_chat
 from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
 from toolbox import ChatBotWithCookies
 
@@ -13,7 +13,7 @@ def validate_key():
     return True
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -49,7 +49,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     yield from update_ui(chatbot=chatbot, history=history)
 
     if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_latest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
         return
 
     if additional_fn is not None:

request_llms/bridge_tgui.py

@@ -91,7 +91,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         inputs 是本次问询的输入
         top_p, temperature是chatGPT的内部调优参数
         history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+        chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
         additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     if additional_fn is not None:
@@ -112,7 +112,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
 
     mutable = ["", time.time()]
-    def run_coorotine(mutable):
+    def run_coroutine(mutable):
         async def get_result(mutable):
             # "tgui:galactica-1.3b@localhost:7860"
 
@@ -126,7 +126,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     break
         asyncio.run(get_result(mutable))
 
-    thread_listen = threading.Thread(target=run_coorotine, args=(mutable,), daemon=True)
+    thread_listen = threading.Thread(target=run_coroutine, args=(mutable,), daemon=True)
     thread_listen.start()
 
     while thread_listen.is_alive():
@@ -142,7 +142,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_silence=False):
     raw_input = "What I would like to say is the following: " + inputs
     prompt = raw_input
     tgui_say = ""
@@ -151,7 +151,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
     addr, port = addr_port.split(':')
 
 
-    def run_coorotine(observe_window):
+    def run_coroutine(observe_window):
         async def get_result(observe_window):
             async for response in run(context=prompt, max_token=llm_kwargs['max_length'],
                                       temperature=llm_kwargs['temperature'],
@@ -162,6 +162,6 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
                     print('exit when no listener')
                     break
         asyncio.run(get_result(observe_window))
-    thread_listen = threading.Thread(target=run_coorotine, args=(observe_window,))
+    thread_listen = threading.Thread(target=run_coroutine, args=(observe_window,))
     thread_listen.start()
     return observe_window[0]

request_llms/bridge_zhipu.py

@@ -1,6 +1,6 @@
 import time
 import os
-from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
+from toolbox import update_ui, get_conf, update_ui_latest_msg, log_chat
 from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
 from toolbox import ChatBotWithCookies
 
@@ -18,7 +18,7 @@ def make_media_input(inputs, image_paths):
     return inputs
 
 def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+                                  observe_window:list=[], console_silence:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -57,12 +57,12 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     try:
         check_packages(["zhipuai"])
     except:
-        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
+        yield from update_ui_latest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
             chatbot=chatbot, history=history, delay=0)
         return
 
     if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_latest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
         return
 
     if additional_fn is not None:

request_llms/com_google.py

@@ -202,11 +202,24 @@ class GoogleChatInit:
             )  # 处理 history
             
         messages.append(self.__conversation_user(inputs, llm_kwargs, enable_multimodal_capacity))  # 处理用户对话
+        stop_sequences = str(llm_kwargs.get("stop", "")).split(" ")
+        # 过滤空字符串并确保至少有一个停止序列
+        stop_sequences = [s for s in stop_sequences if s]
+        if not stop_sequences:
+            payload = {
+                "contents": messages,
+                "generationConfig": {
+                    "temperature": llm_kwargs.get("temperature", 1),
+                    "topP": llm_kwargs.get("top_p", 0.8),
+                    "topK": 10,
+                },
+            }
+        else:
             payload = {
                 "contents": messages,
                 "generationConfig": {
                     # "maxOutputTokens": llm_kwargs.get("max_token", 1024),
-                "stopSequences": str(llm_kwargs.get("stop", "")).split(" "),
+                    "stopSequences": stop_sequences,
                     "temperature": llm_kwargs.get("temperature", 1),
                     "topP": llm_kwargs.get("top_p", 0.8),
                     "topK": 10,

request_llms/com_qwenapi.py

@@ -24,18 +24,13 @@ class QwenRequestInstance():
     def generate(self, inputs, llm_kwargs, history, system_prompt):
         # import _thread as thread
         from dashscope import Generation
-        QWEN_MODEL = {
-            'qwen-turbo': Generation.Models.qwen_turbo,
-            'qwen-plus': Generation.Models.qwen_plus,
-            'qwen-max': Generation.Models.qwen_max,
-        }[llm_kwargs['llm_model']]
         top_p = llm_kwargs.get('top_p', 0.8)
         if top_p == 0: top_p += 1e-5
         if top_p == 1: top_p -= 1e-5
 
         self.result_buf = ""
         responses = Generation.call(
-            model=QWEN_MODEL,
+            model=llm_kwargs['llm_model'],
             messages=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
             top_p=top_p,
             temperature=llm_kwargs.get('temperature', 1.0),

request_llms/local_llm_class.py

@@ -216,7 +216,7 @@ class LocalLLMHandle(Process):
 def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
     load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
 
-    def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_slience:bool=False):
+    def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_silence:bool=False):
         """
             refer to request_llms/bridge_all.py
         """

request_llms/oai_std_model_template.py

@@ -2,15 +2,9 @@ import json
 import time
 import traceback
 import requests
-from loguru import logger
 
-# config_private.py放自己的秘密如API和代理网址
-# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import (
-    get_conf,
-    update_ui,
-    is_the_upload_folder,
-)
+from loguru import logger
+from toolbox import get_conf, is_the_upload_folder, update_ui, update_ui_latest_msg
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
     "proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
@@ -36,35 +30,50 @@ def get_full_error(chunk, stream_response):
 
 def decode_chunk(chunk):
     """
-    用于解读"content"和"finish_reason"的内容
+    用于解读"content"和"finish_reason"的内容（如果支持思维链也会返回"reasoning_content"内容）
     """
     chunk = chunk.decode()
-    respose = ""
+    response = ""
+    reasoning_content = ""
     finish_reason = "False"
+
+    # 考虑返回类型是 text/json 和 text/event-stream 两种
+    if chunk.startswith("data: "):
+        chunk = chunk[6:]
+    else:
+        chunk = chunk
+    
     try:
-        chunk = json.loads(chunk[6:])
+        chunk = json.loads(chunk)
     except:
-        respose = ""
+        response = ""
         finish_reason = chunk
+
     # 错误处理部分
     if "error" in chunk:
-        respose = "API_ERROR"
+        response = "API_ERROR"
         try:
             chunk = json.loads(chunk)
             finish_reason = chunk["error"]["code"]
         except:
             finish_reason = "API_ERROR"
-        return respose, finish_reason
+        return response, reasoning_content, finish_reason
 
     try:
-        respose = chunk["choices"][0]["delta"]["content"]
+        if chunk["choices"][0]["delta"]["content"] is not None:
+            response = chunk["choices"][0]["delta"]["content"]
+    except:
+        pass
+    try:
+        if chunk["choices"][0]["delta"]["reasoning_content"] is not None:
+            reasoning_content = chunk["choices"][0]["delta"]["reasoning_content"]
     except:
         pass
     try:
         finish_reason = chunk["choices"][0]["finish_reason"]
     except:
         pass
-    return respose, finish_reason
+    return response, reasoning_content, finish_reason, str(chunk)
 
 
 def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
@@ -99,7 +108,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
     what_i_ask_now["role"] = "user"
     what_i_ask_now["content"] = input
     messages.append(what_i_ask_now)
-    playload = {
+    payload = {
         "model": model,
         "messages": messages,
         "temperature": temperature,
@@ -107,7 +116,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
         "max_tokens": max_output_token,
     }
 
-    return headers, playload
+    return headers, payload
 
 
 def get_predict_function(
@@ -134,7 +143,7 @@ def get_predict_function(
         history=[],
         sys_prompt="",
         observe_window=None,
-        console_slience=False,
+        console_silence=False,
     ):
         """
         发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
@@ -149,12 +158,13 @@ def get_predict_function(
         observe_window = None：
             用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
         """
-        watch_dog_patience = 5  # 看门狗的耐心，设置5秒不准咬人(咬的也不是人
+        from .bridge_all import model_info
+        watch_dog_patience = 5  # 看门狗的耐心，设置5秒不准咬人 (咬的也不是人)
         if len(APIKEY) == 0:
             raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
         if inputs == "":
             inputs = "你好👋"
-        headers, playload = generate_message(
+        headers, payload = generate_message(
             input=inputs,
             model=llm_kwargs["llm_model"],
             key=APIKEY,
@@ -163,26 +173,18 @@ def get_predict_function(
             system_prompt=sys_prompt,
             temperature=llm_kwargs["temperature"],
         )
+
+        reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
+
         retry = 0
         while True:
             try:
-                from .bridge_all import model_info
-
                 endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
-                if not disable_proxy:
                 response = requests.post(
                     endpoint,
                     headers=headers,
-                        proxies=proxies,
-                        json=playload,
-                        stream=True,
-                        timeout=TIMEOUT_SECONDS,
-                    )
-                else:
-                    response = requests.post(
-                        endpoint,
-                        headers=headers,
-                        json=playload,
+                    proxies=None if disable_proxy else proxies,
+                    json=payload,
                     stream=True,
                     timeout=TIMEOUT_SECONDS,
                 )
@@ -195,9 +197,12 @@ def get_predict_function(
                 if MAX_RETRY != 0:
                     logger.error(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
         
-        stream_response = response.iter_lines()
         result = ""
         finish_reason = ""
+        if reasoning:
+            reasoning_buffer = ""
+        
+        stream_response = response.iter_lines()
         while True:
             try:
                 chunk = next(stream_response)
@@ -207,9 +212,9 @@ def get_predict_function(
                 break
             except requests.exceptions.ConnectionError:
                 chunk = next(stream_response)  # 失败了，重试一次？再失败就没办法了。
-            response_text, finish_reason = decode_chunk(chunk)
+            response_text, reasoning_content, finish_reason, decoded_chunk = decode_chunk(chunk)
             # 返回的数据流第一次为空，继续等待
-            if response_text == "" and finish_reason != "False":
+            if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
                 continue
             if response_text == "API_ERROR" and (
                 finish_reason != "False" or finish_reason != "stop"
@@ -223,10 +228,12 @@ def get_predict_function(
             if chunk:
                 try:
                     if finish_reason == "stop":
-                        if not console_slience:
+                        if not console_silence:
                             print(f"[response] {result}")
                         break
                     result += response_text
+                    if reasoning:
+                        reasoning_buffer += reasoning_content
                     if observe_window is not None:
                         # 观测窗，把已经获取的数据显示出去
                         if len(observe_window) >= 1:
@@ -241,6 +248,9 @@ def get_predict_function(
                     error_msg = chunk_decoded
                     logger.error(error_msg)
                     raise RuntimeError("Json解析不合常规")
+        if reasoning:
+            paragraphs = ''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in reasoning_buffer.split('\n')])
+            return f'''<div class="reasoning_process" >{paragraphs}</div>\n\n''' + result
         return result
 
     def predict(
@@ -259,9 +269,10 @@ def get_predict_function(
         inputs 是本次问询的输入
         top_p, temperature是chatGPT的内部调优参数
         history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+        chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
         additional_fn代表点击的哪个按钮，按钮见functional.py
         """
+        from .bridge_all import model_info
         if len(APIKEY) == 0:
             raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
         if inputs == "":
@@ -289,7 +300,7 @@ def get_predict_function(
             )  # 刷新界面
             time.sleep(2)
 
-        headers, playload = generate_message(
+        headers, payload = generate_message(
             input=inputs,
             model=llm_kwargs["llm_model"],
             key=APIKEY,
@@ -299,28 +310,19 @@ def get_predict_function(
             temperature=llm_kwargs["temperature"],
         )
         
+        reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
+
         history.append(inputs)
         history.append("")
         retry = 0
         while True:
             try:
-                from .bridge_all import model_info
-
                 endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
-                if not disable_proxy:
                 response = requests.post(
                     endpoint,
                     headers=headers,
-                        proxies=proxies,
-                        json=playload,
-                        stream=True,
-                        timeout=TIMEOUT_SECONDS,
-                    )
-                else:
-                    response = requests.post(
-                        endpoint,
-                        headers=headers,
-                        json=playload,
+                    proxies=None if disable_proxy else proxies,
+                    json=payload,
                     stream=True,
                     timeout=TIMEOUT_SECONDS,
                 )
@@ -338,18 +340,27 @@ def get_predict_function(
                     raise TimeoutError
 
         gpt_replying_buffer = ""
+        if reasoning:
+            gpt_reasoning_buffer = ""
 
         stream_response = response.iter_lines()
+        wait_counter = 0
         while True:
             try:
                 chunk = next(stream_response)
             except StopIteration:
+                if wait_counter != 0 and gpt_replying_buffer == "":
+                    yield from update_ui_latest_msg(lastmsg="模型调用失败 ...", chatbot=chatbot, history=history, msg="failed")
                 break
             except requests.exceptions.ConnectionError:
                 chunk = next(stream_response)  # 失败了，重试一次？再失败就没办法了。
-            response_text, finish_reason = decode_chunk(chunk)
+            response_text, reasoning_content, finish_reason, decoded_chunk = decode_chunk(chunk)
+            if decoded_chunk == ': keep-alive':
+                wait_counter += 1
+                yield from update_ui_latest_msg(lastmsg="等待中 " + "".join(["."] * (wait_counter%10)), chatbot=chatbot, history=history, msg="waiting ...")
+                continue
             # 返回的数据流第一次为空，继续等待
-            if response_text == "" and finish_reason != "False":
+            if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
                 status_text = f"finish_reason: {finish_reason}"
                 yield from update_ui(
                     chatbot=chatbot, history=history, msg=status_text
@@ -364,7 +375,7 @@ def get_predict_function(
                         chunk_decoded = chunk.decode()
                         chatbot[-1] = (
                             chatbot[-1][0],
-                            "[Local Message] {finish_reason},获得以下报错信息：\n"
+                            f"[Local Message] {finish_reason}, 获得以下报错信息：\n"
                             + chunk_decoded,
                         )
                         yield from update_ui(
@@ -379,6 +390,12 @@ def get_predict_function(
                         logger.info(f"[response] {gpt_replying_buffer}")
                         break
                     status_text = f"finish_reason: {finish_reason}"
+                    if reasoning:
+                        gpt_replying_buffer += response_text
+                        gpt_reasoning_buffer += reasoning_content
+                        paragraphs = ''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in gpt_reasoning_buffer.split('\n')])
+                        history[-1] = f'<div class="reasoning_process">{paragraphs}</div>\n\n---\n\n' + gpt_replying_buffer
+                    else:
                         gpt_replying_buffer += response_text
                         # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
                         history[-1] = gpt_replying_buffer

request_llms/requirements_chatglm4.txt

@@ -0,0 +1,7 @@
+protobuf
+cpm_kernels
+torch>=1.10
+transformers>=4.44
+mdtex2html
+sentencepiece
+accelerate

requirements.txt

@@ -1,4 +1,4 @@
-https://public.agent-matrix.com/publish/gradio-3.32.10-py3-none-any.whl
+https://public.agent-matrix.com/publish/gradio-3.32.12-py3-none-any.whl
 fastapi==0.110
 gradio-client==0.8
 pypdf2==2.12.1
@@ -13,7 +13,7 @@ scipdf_parser>=0.52
 spacy==3.7.4
 anthropic>=0.18.1
 python-markdown-math
-pymdown-extensions
+pymdown-extensions>=10.14
 websocket-client
 beautifulsoup4
 prompt_toolkit
@@ -25,7 +25,7 @@ pyautogen
 colorama
 Markdown
 pygments
-edge-tts
+edge-tts>=7.0.0
 pymupdf
 openai
 rjsmin