Merge Frontier, Update to Version 3.72 (#1553)
* Zhipu sdk update 适配最新的智谱SDK，支持GLM4v (#1502) * 适配 google gemini 优化为从用户input中提取文件 * 适配最新的智谱SDK、支持glm-4v * requirements.txt fix * pending history check --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * Update "生成多种Mermaid图表" plugin: Separate out the file reading function (#1520) * Update crazy_functional.py with new functionality deal with PDF * Update crazy_functional.py and Mermaid.py for plugin_kwargs * Update crazy_functional.py with new chart type: mind map * Update SELECT_PROMPT and i_say_show_user messages * Update ArgsReminder message in get_crazy_functions() function * Update with read md file and update PROMPTS * Return the PROMPTS as the test found that the initial version worked best * Update Mermaid chart generation function * version 3.71 * 解决issues #1510 * Remove unnecessary text from sys_prompt in 解析历史输入 function * Remove sys_prompt message in 解析历史输入 function * Update bridge_all.py: supports gpt-4-turbo-preview (#1517) * Update bridge_all.py: supports gpt-4-turbo-preview supports gpt-4-turbo-preview * Update bridge_all.py --------- Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com> * Update config.py: supports gpt-4-turbo-preview (#1516) * Update config.py: supports gpt-4-turbo-preview supports gpt-4-turbo-preview * Update config.py --------- Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com> * Refactor 解析历史输入 function to handle file input * Update Mermaid chart generation functionality * rename files and functions --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> Co-authored-by: hongyi-zhao <hongyi.zhao@gmail.com> Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com> * 接入mathpix ocr功能 (#1468) * Update Latex输出PDF结果.py 借助mathpix实现了PDF翻译中文并重新编译PDF * Update config.py add mathpix appid & appkey * Add 'PDF翻译中文并重新编译PDF' feature to plugins. --------- Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com> * fix zhipuai * check picture * remove glm-4 due to bug * 修改config * 检查MATHPIX_APPID * Remove unnecessary code and update function_plugins dictionary * capture non-standard token overflow * bug fix #1524 * change mermaid style * 支持mermaid 滚动放大缩小重置,鼠标滚动和拖拽 (#1530) * 支持mermaid 滚动放大缩小重置,鼠标滚动和拖拽 * 微调未果 先stage一下 * update --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com> * ver 3.72 * change live2d * save the status of ``clear btn` in cookie * 前端选择保持 * js ui bug fix * reset btn bug fix * update live2d tips * fix missing get_token_num method * fix live2d toggle switch * fix persistent custom btn with cookie * fix zhipuai feedback with core functionality * Refactor button update and clean up functions --------- Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com> Co-authored-by: Menghuan1918 <menghuan2003@outlook.com> Co-authored-by: hongyi-zhao <hongyi.zhao@gmail.com> Co-authored-by: Hao Ma <893017927@qq.com> Co-authored-by: zeyuan huang <599012428@qq.com>
This commit is contained in:
binary-husky
@@ -31,6 +31,9 @@ from .bridge_qianfan import predict as qianfan_ui
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from .bridge_google_gemini import predict as genai_ui
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from .bridge_google_gemini import predict_no_ui_long_connection as genai_noui
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from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
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from .bridge_zhipu import predict as zhipu_ui
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colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
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class LazyloadTiktoken(object):
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@@ -44,13 +47,13 @@ class LazyloadTiktoken(object):
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tmp = tiktoken.encoding_for_model(model)
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print('加载tokenizer完毕')
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return tmp
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def encode(self, *args, **kwargs):
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encoder = self.get_encoder(self.model)
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encoder = self.get_encoder(self.model)
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return encoder.encode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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encoder = self.get_encoder(self.model)
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encoder = self.get_encoder(self.model)
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return encoder.decode(*args, **kwargs)
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# Endpoint 重定向
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@@ -63,7 +66,7 @@ azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/compl
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# 兼容旧版的配置
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try:
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API_URL = get_conf("API_URL")
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if API_URL != "https://api.openai.com/v1/chat/completions":
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if API_URL != "https://api.openai.com/v1/chat/completions":
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openai_endpoint = API_URL
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print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
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except:
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@@ -95,7 +98,7 @@ model_info = {
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gpt-3.5-turbo-16k": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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@@ -185,7 +188,7 @@ model_info = {
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"tokenizer": tokenizer_gpt4,
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"token_cnt": get_token_num_gpt4,
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},
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"gpt-4-vision-preview": {
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"fn_with_ui": chatgpt_vision_ui,
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"fn_without_ui": chatgpt_vision_noui,
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@@ -215,16 +218,25 @@ model_info = {
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"token_cnt": get_token_num_gpt4,
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},
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# 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)
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"api2d-gpt-3.5-turbo": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": api2d_endpoint,
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"max_token": 4096,
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# 智谱AI
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"glm-4": {
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"fn_with_ui": zhipu_ui,
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"fn_without_ui": zhipu_noui,
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"endpoint": None,
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"max_token": 10124 * 8,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"glm-3-turbo": {
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"fn_with_ui": zhipu_ui,
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"fn_without_ui": zhipu_noui,
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"endpoint": None,
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"max_token": 10124 * 4,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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# 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)
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"api2d-gpt-4": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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@@ -580,19 +592,17 @@ if "llama2" in AVAIL_LLM_MODELS: # llama2
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})
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except:
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print(trimmed_format_exc())
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if "zhipuai" in AVAIL_LLM_MODELS: # zhipuai
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if "zhipuai" in AVAIL_LLM_MODELS: # zhipuai 是glm-4的别名，向后兼容配置
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try:
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from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
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from .bridge_zhipu import predict as zhipu_ui
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model_info.update({
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"zhipuai": {
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"fn_with_ui": zhipu_ui,
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"fn_without_ui": zhipu_noui,
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"endpoint": None,
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"max_token": 4096,
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"max_token": 10124 * 8,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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},
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})
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except:
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print(trimmed_format_exc())
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@@ -635,7 +645,7 @@ AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")
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if len(AZURE_CFG_ARRAY) > 0:
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for azure_model_name, azure_cfg_dict in AZURE_CFG_ARRAY.items():
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# 可能会覆盖之前的配置，但这是意料之中的
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if not azure_model_name.startswith('azure'):
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if not azure_model_name.startswith('azure'):
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raise ValueError("AZURE_CFG_ARRAY中配置的模型必须以azure开头")
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endpoint_ = azure_cfg_dict["AZURE_ENDPOINT"] + \
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f'openai/deployments/{azure_cfg_dict["AZURE_ENGINE"]}/chat/completions?api-version=2023-05-15'
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@@ -701,7 +711,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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executor = ThreadPoolExecutor(max_workers=4)
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models = model.split('&')
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n_model = len(models)
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window_len = len(observe_window)
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assert window_len==3
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window_mutex = [["", time.time(), ""] for _ in range(n_model)] + [True]
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@@ -720,7 +730,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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time.sleep(0.25)
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if not window_mutex[-1]: break
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# 看门狗（watchdog）
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for i in range(n_model):
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for i in range(n_model):
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window_mutex[i][1] = observe_window[1]
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# 观察窗（window）
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chat_string = []
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@@ -113,6 +113,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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error_msg = get_full_error(chunk, stream_response).decode()
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if "reduce the length" in error_msg:
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raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
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elif """type":"upstream_error","param":"307""" in error_msg:
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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。")
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else:
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raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
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if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
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@@ -57,6 +57,10 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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if "vision" in llm_kwargs["llm_model"]:
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have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
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if not have_recent_file:
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chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
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return
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def make_media_input(inputs, image_paths):
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for image_path in image_paths:
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inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
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@@ -1,15 +1,21 @@
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import time
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import os
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from toolbox import update_ui, get_conf, update_ui_lastest_msg
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from toolbox import check_packages, report_exception
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from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
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model_name = '智谱AI大模型'
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zhipuai_default_model = 'glm-4'
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def validate_key():
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ZHIPUAI_API_KEY = get_conf("ZHIPUAI_API_KEY")
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if ZHIPUAI_API_KEY == '': return False
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return True
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def make_media_input(inputs, image_paths):
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for image_path in image_paths:
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inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
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return inputs
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
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"""
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⭐多线程方法
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@@ -18,34 +24,40 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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watch_dog_patience = 5
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response = ""
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if llm_kwargs["llm_model"] == "zhipuai":
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llm_kwargs["llm_model"] = zhipuai_default_model
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if validate_key() is False:
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raise RuntimeError('请配置ZHIPUAI_API_KEY')
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from .com_zhipuapi import ZhipuRequestInstance
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sri = ZhipuRequestInstance()
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for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
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# 开始接收回复
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from .com_zhipuglm import ZhipuChatInit
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zhipu_bro_init = ZhipuChatInit()
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for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, sys_prompt):
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if len(observe_window) >= 1:
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observe_window[0] = response
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if len(observe_window) >= 2:
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if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
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if (time.time() - observe_window[1]) > watch_dog_patience:
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raise RuntimeError("程序终止。")
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return response
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
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"""
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⭐单线程方法
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函数的说明请见 request_llms/bridge_all.py
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"""
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chatbot.append((inputs, ""))
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chatbot.append([inputs, ""])
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yield from update_ui(chatbot=chatbot, history=history)
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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try:
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check_packages(["zhipuai"])
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except:
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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 zhipuai==1.0.7```。",
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chatbot=chatbot, history=history, delay=0)
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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```。",
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chatbot=chatbot, history=history, delay=0)
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return
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if validate_key() is False:
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yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
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return
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@@ -53,16 +65,29 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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if additional_fn is not None:
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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
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# 开始接收回复
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from .com_zhipuapi import ZhipuRequestInstance
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sri = ZhipuRequestInstance()
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for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
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chatbot[-1] = (inputs, response)
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chatbot[-1] = [inputs, ""]
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yield from update_ui(chatbot=chatbot, history=history)
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# 总结输出
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if response == f"[Local Message] 等待{model_name}响应中 ...":
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response = f"[Local Message] {model_name}响应异常 ..."
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if llm_kwargs["llm_model"] == "zhipuai":
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llm_kwargs["llm_model"] = zhipuai_default_model
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if llm_kwargs["llm_model"] in ["glm-4v"]:
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have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
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if not have_recent_file:
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chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
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return
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if have_recent_file:
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inputs = make_media_input(inputs, image_paths)
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chatbot[-1] = [inputs, ""]
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yield from update_ui(chatbot=chatbot, history=history)
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# 开始接收回复
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from .com_zhipuglm import ZhipuChatInit
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zhipu_bro_init = ZhipuChatInit()
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for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, system_prompt):
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chatbot[-1] = [inputs, response]
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yield from update_ui(chatbot=chatbot, history=history)
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history.extend([inputs, response])
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yield from update_ui(chatbot=chatbot, history=history)
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@@ -1,70 +0,0 @@
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from toolbox import get_conf
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import threading
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import logging
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timeout_bot_msg = '[Local Message] Request timeout. Network error.'
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class ZhipuRequestInstance():
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def __init__(self):
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self.time_to_yield_event = threading.Event()
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self.time_to_exit_event = threading.Event()
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self.result_buf = ""
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def generate(self, inputs, llm_kwargs, history, system_prompt):
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# import _thread as thread
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import zhipuai
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ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
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zhipuai.api_key = ZHIPUAI_API_KEY
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self.result_buf = ""
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response = zhipuai.model_api.sse_invoke(
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model=ZHIPUAI_MODEL,
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prompt=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
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top_p=llm_kwargs['top_p']*0.7, # 智谱的API抽风，手动*0.7给做个线性变换
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temperature=llm_kwargs['temperature']*0.95, # 智谱的API抽风，手动*0.7给做个线性变换
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)
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for event in response.events():
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if event.event == "add":
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# if self.result_buf == "" and event.data.startswith(" "):
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# event.data = event.data.lstrip(" ") # 每次智谱为啥都要带个空格开头呢？
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self.result_buf += event.data
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yield self.result_buf
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elif event.event == "error" or event.event == "interrupted":
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raise RuntimeError("Unknown error:" + event.data)
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elif event.event == "finish":
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yield self.result_buf
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break
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else:
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raise RuntimeError("Unknown error:" + str(event))
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if self.result_buf == "":
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yield "智谱没有返回任何数据, 请检查ZHIPUAI_API_KEY和ZHIPUAI_MODEL是否填写正确."
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logging.info(f'[raw_input] {inputs}')
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logging.info(f'[response] {self.result_buf}')
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return self.result_buf
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def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
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conversation_cnt = len(history) // 2
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messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
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||||
if conversation_cnt:
|
||||
for index in range(0, 2*conversation_cnt, 2):
|
||||
what_i_have_asked = {}
|
||||
what_i_have_asked["role"] = "user"
|
||||
what_i_have_asked["content"] = history[index]
|
||||
what_gpt_answer = {}
|
||||
what_gpt_answer["role"] = "assistant"
|
||||
what_gpt_answer["content"] = history[index+1]
|
||||
if what_i_have_asked["content"] != "":
|
||||
if what_gpt_answer["content"] == "":
|
||||
continue
|
||||
if what_gpt_answer["content"] == timeout_bot_msg:
|
||||
continue
|
||||
messages.append(what_i_have_asked)
|
||||
messages.append(what_gpt_answer)
|
||||
else:
|
||||
messages[-1]['content'] = what_gpt_answer['content']
|
||||
what_i_ask_now = {}
|
||||
what_i_ask_now["role"] = "user"
|
||||
what_i_ask_now["content"] = inputs
|
||||
messages.append(what_i_ask_now)
|
||||
return messages
|
||||
84
request_llms/com_zhipuglm.py
Normal file
84
request_llms/com_zhipuglm.py
Normal file
@@ -0,0 +1,84 @@
|
||||
# encoding: utf-8
|
||||
# @Time : 2024/1/22
|
||||
# @Author : Kilig947 & binary husky
|
||||
# @Descr : 兼容最新的智谱Ai
|
||||
from toolbox import get_conf
|
||||
from zhipuai import ZhipuAI
|
||||
from toolbox import get_conf, encode_image, get_pictures_list
|
||||
import logging, os
|
||||
|
||||
|
||||
def input_encode_handler(inputs, llm_kwargs):
|
||||
if llm_kwargs["most_recent_uploaded"].get("path"):
|
||||
image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
|
||||
md_encode = []
|
||||
for md_path in image_paths:
|
||||
type_ = os.path.splitext(md_path)[1].replace(".", "")
|
||||
type_ = "jpeg" if type_ == "jpg" else type_
|
||||
md_encode.append({"data": encode_image(md_path), "type": type_})
|
||||
return inputs, md_encode
|
||||
|
||||
|
||||
class ZhipuChatInit:
|
||||
|
||||
def __init__(self):
|
||||
ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
|
||||
if len(ZHIPUAI_MODEL) > 0:
|
||||
logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
|
||||
self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
|
||||
self.model = ''
|
||||
|
||||
def __conversation_user(self, user_input: str, llm_kwargs):
|
||||
if self.model not in ["glm-4v"]:
|
||||
return {"role": "user", "content": user_input}
|
||||
else:
|
||||
input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
|
||||
what_i_have_asked = {"role": "user", "content": []}
|
||||
what_i_have_asked['content'].append({"type": 'text', "text": user_input})
|
||||
if encode_img:
|
||||
img_d = {"type": "image_url",
|
||||
"image_url": {'url': encode_img}}
|
||||
what_i_have_asked['content'].append(img_d)
|
||||
return what_i_have_asked
|
||||
|
||||
def __conversation_history(self, history, llm_kwargs):
|
||||
messages = []
|
||||
conversation_cnt = len(history) // 2
|
||||
if conversation_cnt:
|
||||
for index in range(0, 2 * conversation_cnt, 2):
|
||||
what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
|
||||
what_gpt_answer = {
|
||||
"role": "assistant",
|
||||
"content": history[index + 1]
|
||||
}
|
||||
messages.append(what_i_have_asked)
|
||||
messages.append(what_gpt_answer)
|
||||
return messages
|
||||
|
||||
def __conversation_message_payload(self, inputs, llm_kwargs, history, system_prompt):
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append({"role": "system", "content": system_prompt})
|
||||
self.model = llm_kwargs['llm_model']
|
||||
messages.extend(self.__conversation_history(history, llm_kwargs)) # 处理 history
|
||||
messages.append(self.__conversation_user(inputs, llm_kwargs)) # 处理用户对话
|
||||
response = self.zhipu_bro.chat.completions.create(
|
||||
model=self.model, messages=messages, stream=True,
|
||||
temperature=llm_kwargs.get('temperature', 0.95) * 0.95, # 只能传默认的 temperature 和 top_p
|
||||
top_p=llm_kwargs.get('top_p', 0.7) * 0.7,
|
||||
max_tokens=llm_kwargs.get('max_tokens', 1024 * 4), # 最大输出模型的一半
|
||||
)
|
||||
return response
|
||||
|
||||
def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
|
||||
self.model = llm_kwargs['llm_model']
|
||||
response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
|
||||
bro_results = ''
|
||||
for chunk in response:
|
||||
bro_results += chunk.choices[0].delta.content
|
||||
yield chunk.choices[0].delta.content, bro_results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
zhipu = ZhipuChatInit()
|
||||
zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')
|
||||
Reference in New Issue
Block a user