diff --git a/configs/model_config.py.example b/configs/model_config.py.example index 875dfd1..a105708 100644 --- a/configs/model_config.py.example +++ b/configs/model_config.py.example @@ -39,10 +39,10 @@ MODEL_PATH = { # TODO: add all supported llm models "llm_model": { # 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持 - "chatglm-6b": "THUDM/chatglm-6b", "chatglm2-6b": "THUDM/chatglm2-6b", - "chatglm2-6b-int4": "THUDM/chatglm2-6b-int4", "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k", + "chatglm3-6b": "THUDM/chatglm3-6b-32k", + "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k", "baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat", "baichuan2-7b":"baichuan-inc/Baichuan2-7B-Chat", @@ -122,6 +122,13 @@ TEMPERATURE = 0.7 ONLINE_LLM_MODEL = { # 线上模型。请在server_config中为每个在线API设置不同的端口 + + "openai-api": { + "model_name": "gpt-35-turbo", + "api_base_url": "https://api.openai.com/v1", + "api_key": "", + "openai_proxy": "", + }, # 具体注册及api key获取请前往 http://open.bigmodel.cn "zhipu-api": { "api_key": "", @@ -243,11 +250,13 @@ VLLM_MODEL_DICT = { ## 你认为支持Agent能力的模型，可以在这里添加，添加后不会出现可视化界面的警告 SUPPORT_AGENT_MODEL = [ - "Azure-OpenAI", - "OpenAI", - "Anthropic", + "azure-api", + "openai-api", + "claude-api", "Qwen", "qwen-api", "baichuan-api", "agentlm" -] \ No newline at end of file + "chatglm3-6b" + "xinghuo-api" +] diff --git a/configs/prompt_config.py.example b/configs/prompt_config.py.example index 6080ace..85f0986 100644 --- a/configs/prompt_config.py.example +++ b/configs/prompt_config.py.example @@ -127,9 +127,11 @@ PROMPT_TEMPLATES["agent_chat"] = { """, "中文版本": """ - 请请严格按照提供的思维方式来思考。你的知识不一定正确，所以你一定要用提供的工具来思考，并给出用户答案。 + 你的知识不一定正确，所以你一定要用提供的工具来思考，并给出用户答案。 你有以下工具可以使用: {tools} + + 请请严格按照提供的思维方式来思考，所有的关键词都要输出，例如Action，Action Input，Observation等 ``` Question: 用户的提问或者观察到的信息， Thought: 你应该思考该做什么，是根据工具的结果来回答问题，还是决定使用什么工具。 @@ -148,6 +150,5 @@ PROMPT_TEMPLATES["agent_chat"] = { 用户开始以提问： Question: {input} Thought: {agent_scratchpad} - """, } diff --git a/server/agent/custom_template.py b/server/agent/custom_template.py index 2744b87..c77066a 100644 --- a/server/agent/custom_template.py +++ b/server/agent/custom_template.py @@ -48,7 +48,7 @@ class CustomOutputParser(AgentOutputParser): parts = llm_output.split("Action:") if len(parts) < 2: return AgentFinish( - return_values={"output": f"调用agent失败: `{llm_output}`"}, + return_values={"output": f"调用agent工具失败，该回答为大模型自身能力的回答:\n\n `{llm_output}`"}, log=llm_output, ) diff --git a/server/agent/tools_select.py b/server/agent/tools_select.py index b3d753a..be5b30d 100644 --- a/server/agent/tools_select.py +++ b/server/agent/tools_select.py @@ -1,90 +1,91 @@ from langchain.tools import Tool from server.agent.tools import * +## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本，下面的内容是英文版本。 # tools = [ # Tool.from_function( # func=calculate, -# name="计算器工具", -# description="进行简单的数学运算, 只是简单的, 使用Wolfram数学工具进行更复杂的运算", +# name="Simple Calculator Tool", +# description="Perform simple mathematical operations, Just simple, Use Wolfram Math Tool for more complex operations" # ), # Tool.from_function( # func=translate, -# name="翻译工具", -# description="如果你无法访问互联网，并且需要翻译各种语言，应该使用这个工具" +# name="Translation Tool", +# description="Use this tool if you can't access the internet and need to translate various languages" # ), # Tool.from_function( # func=weathercheck, -# name="天气查询工具", -# description="无需访问互联网，使用这个工具查询中国各地未来24小时的天气", +# name="Weather Checking Tool", +# description="Check the weather for various places in China for the next 24 hours without needing internet access" # ), # Tool.from_function( # func=shell, -# name="shell工具", -# description="使用命令行工具输出", +# name="Shell Tool", +# description="Use command line tool output" # ), # Tool.from_function( # func=knowledge_search_more, -# name="知识库查询工具", -# description="优先访问知识库来获取答案", +# name="Knowledge Base Tool", +# description="Prioritize accessing the knowledge base to get answers" # ), # Tool.from_function( # func=search_internet, -# name="互联网查询工具", -# description="如果你无法访问互联网，这个工具可以帮助你访问Bing互联网来解答问题", +# name="Internet Tool", +# description="If you can't access the internet, this tool can help you access Bing to answer questions" # ), # Tool.from_function( # func=wolfram, -# name="Wolfram数学工具", -# description="高级的数学运算工具，能够完成非常复杂的数学问题" +# name="Wolfram Math Tool", +# description="Use this tool to perform more complex mathematical operations" # ), # Tool.from_function( # func=youtube_search, -# name="Youtube搜索工具", -# description="使用这个工具在Youtube上搜索视频" +# name="Youtube Search Tool", +# description="Use this tool to search for videos on Youtube" +# ) # ] -## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本，下面的内容是英文版本。 tools = [ Tool.from_function( func=calculate, - name="Simple Calculator Tool", - description="Perform simple mathematical operations, Just simple, Use Wolfram Math Tool for more complex operations" + name="计算器工具", + description="进行简单的数学运算, 只是简单的, 使用Wolfram数学工具进行更复杂的运算", ), Tool.from_function( func=translate, - name="Translation Tool", - description="Use this tool if you can't access the internet and need to translate various languages" + name="翻译工具", + description="如果你无法访问互联网，并且需要翻译各种语言，应该使用这个工具" ), Tool.from_function( func=weathercheck, - name="Weather Checking Tool", - description="Check the weather for various places in China for the next 24 hours without needing internet access" + name="天气查询工具", + description="无需访问互联网，使用这个工具查询中国各地未来24小时的天气", ), Tool.from_function( func=shell, - name="Shell Tool", - description="Use command line tool output" + name="shell工具", + description="使用命令行工具输出", ), Tool.from_function( func=knowledge_search_more, - name="Knowledge Base Tool", - description="Prioritize accessing the knowledge base to get answers" + name="知识库查询工具", + description="优先访问知识库来获取答案", ), Tool.from_function( func=search_internet, - name="Internet Tool", - description="If you can't access the internet, this tool can help you access Bing to answer questions" + name="互联网查询工具", + description="如果你无法访问互联网，这个工具可以帮助你访问Bing互联网来解答问题", ), Tool.from_function( func=wolfram, - name="Wolfram Math Tool", - description="Use this tool to perform more complex mathematical operations" + name="Wolfram数学工具", + description="高级的数学运算工具，能够完成非常复杂的数学问题" ), Tool.from_function( func=youtube_search, - name="Youtube Search Tool", - description="Use this tool to search for videos on Youtube" + name="Youtube搜索工具", + description="使用这个工具在Youtube上搜索视频" ) ] -tool_names = [tool.name for tool in tools] \ No newline at end of file +tool_names = [tool.name for tool in tools] diff --git a/server/chat/agent_chat.py b/server/chat/agent_chat.py index 2e96853..35ef869 100644 --- a/server/chat/agent_chat.py +++ b/server/chat/agent_chat.py @@ -5,7 +5,7 @@ from langchain.agents import AgentExecutor, LLMSingleActionAgent from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate from fastapi import Body from fastapi.responses import StreamingResponse -from configs import LLM_MODEL, TEMPERATURE, HISTORY_LEN,Agent_MODEL +from configs import LLM_MODEL, TEMPERATURE, HISTORY_LEN, Agent_MODEL from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template from langchain.chains import LLMChain from typing import AsyncIterable, Optional, Dict @@ -16,18 +16,21 @@ import json from server.agent import model_container from server.knowledge_base.kb_service.base import get_kb_details + async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), history: List[History] = Body([], description="历史对话", examples=[[ {"role": "user", "content": "请使用知识库工具查询今天北京天气"}, - {"role": "assistant", "content": "使用天气查询工具查询到今天北京多云，10-14摄氏度，东北风2级，易感冒"}]] + {"role": "assistant", + "content": "使用天气查询工具查询到今天北京多云，10-14摄氏度，东北风2级，易感冒"}]] ), stream: bool = Body(False, description="流式输出"), model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), - prompt_name: str = Body("default",description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), + prompt_name: str = Body("default", + description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), ): history = [History.from_data(h) for h in history] @@ -50,7 +53,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples kb_list = {x["kb_name"]: x for x in get_kb_details()} model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()} - if Agent_MODEL: ## 如果有指定使用Agent模型来完成任务 model_agent = get_ChatOpenAI( @@ -74,7 +76,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples agent = LLMSingleActionAgent( llm_chain=llm_chain, output_parser=output_parser, - stop=["\nObservation:", "Observation:", "<|im_end|>"], # Qwen模型中使用这个 + stop=["\nObservation:", "Observation:", "<|im_end|>", "<|observation|>"], allowed_tools=tool_names, ) # 把history转成agent的memory