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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Time : 2025/3/16 09:46
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# @Author : old-tom
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# @File : llm_agent
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# @Project : llmFunctionCallDemo
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# @Desc : llm代理
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from llmagent.llm_config import LLMConfigLoader
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from abc import ABC
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from llmtools import TOOLS_BIND_FUNCTION, STRUCT_TOOLS
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from llmagent import PROMPT_TEMPLATE
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from langchain_openai import ChatOpenAI
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from langchain.globals import set_debug, set_verbose
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.chat_history import BaseChatMessageHistory, InMemoryChatMessageHistory
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from langchain_core.runnables.history import RunnableWithMessageHistory
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from langchain_core.messages import HumanMessage
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from log_conf import log
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from llmagent.llm_config import base_conf
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from datetime import datetime
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# debug模式,有更多输出
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set_debug(base_conf.debug)
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set_verbose(base_conf.verbose)
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# 默认系统提示词
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DEFAULT_SYS_PROMPT = ''
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# 字符串解析器 会自动提取 content 字段
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parser = StrOutputParser()
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# 模型初始化，注意修改env.toml中的配置
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# llm_conf = LLMConfigLoader.load(item_name='ark')
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llm_conf = LLMConfigLoader.load(item_name='siliconflow')
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llm = ChatOpenAI(
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model=llm_conf.model, api_key=llm_conf.api_key,
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base_url=llm_conf.base_url, max_tokens=llm_conf.max_tokens,
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temperature=llm_conf.temperature,
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streaming=llm_conf.streaming
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)
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# 历史消息存储(内存)
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his_store = {}
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def get_session_history(session_id: str) -> BaseChatMessageHistory:
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"""
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获取历史消息
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:param session_id:
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:return:
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"""
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if session_id not in his_store:
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# 内存存储（可以替换为数据库或者其他,参考 BaseChatMessageHistory 实现类）
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his_store[session_id] = InMemoryChatMessageHistory()
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return his_store[session_id]
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def get_today():
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"""
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获取今天日期
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:return:
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"""
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return datetime.now().strftime('%Y-%m-%d')
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class BaseChatAgent(ABC):
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"""
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抽象Agent类
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"""
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def __init__(self, system_prompt: str = DEFAULT_SYS_PROMPT):
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"""
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:param system_prompt: 系统提示词
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"""
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# 单轮对话提示词模版
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self.prompt = ChatPromptTemplate(
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[
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("system", system_prompt),
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("human", "{user_input}")
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]
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)
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# 多轮对话提示词模版
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self.multi_round_prompt = ChatPromptTemplate.from_messages([
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("system", system_prompt),
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MessagesPlaceholder(variable_name="messages")
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])
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def invoke(self, user_input: str) -> str:
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"""
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单论对话并一次性返回
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:param user_input: 用户输入
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:return:
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"""
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chain = self.prompt | llm
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return chain.invoke({
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'user_input': user_input
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}).content
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def invoke_by_stream(self, user_input: str):
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"""
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单论对话并流式返回（同步流）
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:param user_input: 用户输入
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:return:
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"""
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chain = self.prompt | llm | parser
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response = chain.stream({'user_input': user_input})
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for chunk in response:
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print(chunk, flush=True, end='')
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def multi_with_stream(self, user_input: str, session_id: int):
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"""
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多轮对话
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:param user_input: 用户输入
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:param session_id: 对话sessionId
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:return:
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"""
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config = {"configurable": {"session_id": session_id}}
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chain = self.multi_round_prompt | llm | parser
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with_message_history = RunnableWithMessageHistory(chain, get_session_history, input_messages_key="messages")
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response = with_message_history.stream({
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'messages': [HumanMessage(content=user_input)]
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}, config=config)
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for chunk in response:
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print(chunk, flush=True, end='')
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@staticmethod
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def invoke_with_tool(user_input: str):
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"""
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工具调用,用于测试模型选择工具
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:param user_input:
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:return: 这里返回的是LLM推理出的tool信息，格式如下：
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[{'name': 'get_current_weather', 'args': {'location': 'Beijing, China'}, 'id': 'call_xeeq4q52fw9x61lkrqwy9cr6', 'type': 'tool_call'}]
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"""
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llm_with_tools = llm.bind_tools(STRUCT_TOOLS)
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return llm_with_tools.invoke(user_input).tool_calls
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def multi_with_tool_call_stream(self, user_input: str, session_id: int):
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"""
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多轮对话，包含工具调用
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:param session_id: 对话sessionId
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:param user_input:
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:return: 流式输出
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"""
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config = {"configurable": {"session_id": session_id}}
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# 总体任务描述及提示词
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user_msg = PROMPT_TEMPLATE.get('VOICE_ASSISTANT')['template'].format(user_input=user_input,
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today=get_today())
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messages = [HumanMessage(user_msg)]
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llm_with_tools = llm.bind_tools(STRUCT_TOOLS, tool_choice='auto')
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# 判断使用哪个工具，需要加提示词让模型判断参数是否符合规则
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user_input = PROMPT_TEMPLATE.get('TOOL_CALLER')['template'].format(user_input=user_input, today=get_today())
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# 工具chain加入历史对话
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too_chain = self.multi_round_prompt | llm_with_tools
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with_message_history = RunnableWithMessageHistory(too_chain, get_session_history, input_messages_key="messages")
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call_msg = with_message_history.invoke({'messages': user_input}, config=config)
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# 如果参数不满足要求 call_msg 的content会可能会包含参数校验失败信息，例：参数错误：分屏数量必须为大于0的整数。请检查指令中的"分屏数量"参数。
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# 用模型进行参数校验很不稳定，不是每次都能输出错误信息。还是在tool中手动校验靠谱。
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messages.append(call_msg)
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for tool_call in call_msg.tool_calls:
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selected_tool = TOOLS_BIND_FUNCTION[tool_call["name"].lower()]
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# 执行工具调用（同步）,返回ToolMessage
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tool_msg = selected_tool.invoke(tool_call)
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messages.append(tool_msg)
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log.info('【function call】构造输入为{}', messages)
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# messages 中包含了 人类指令、AI指令、工具指令, 模型根据历史聊天组装成最后的回答
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chat_chain = self.multi_round_prompt | llm_with_tools | parser
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# RunnableWithMessageHistory 会调用历史对话
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with_message_history = RunnableWithMessageHistory(chat_chain, get_session_history,
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input_messages_key="messages")
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response = with_message_history.stream({'messages': messages}, config=config)
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for chunk in response:
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print(chunk, flush=True, end='')
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class ChatAgent(BaseChatAgent):
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pass
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