258 lines
14 KiB
Python
258 lines
14 KiB
Python
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from langchain.memory import ConversationBufferWindowMemory
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from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
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from server.agent.tools_select import tools, tool_names, search_tool_names
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from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
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from langchain.agents import LLMSingleActionAgent, AgentExecutor
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from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
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from fastapi import Body
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from sse_starlette.sse import EventSourceResponse
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from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
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from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
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from langchain.chains import LLMChain
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from typing import AsyncIterable, Optional
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import asyncio
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from typing import List
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from server.chat.utils import History
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import json
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from server.agent import model_container
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from server.knowledge_base.kb_service.base import get_kb_details
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import ast
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import re
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from configs.basic_config import *
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async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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history: List[History] = Body([],
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description="历史对话",
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examples=[[
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{"role": "user",
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"content": "请使用天气查询工具查询今天北京天气"},
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{"role": "assistant",
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"content": "今天是2024年3月22日，受冷空气影响，白天有3、4级偏北风，阵风6、7"
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"级，西部山区阵风相对明显，局地伴有扬沙。白天晴，局地有扬沙,偏北风,1级转3、4级，阵风6、7级,"
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"最高气温22℃。夜间晴间多云,偏北风,1、2级,最低气温6℃。"}]]
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),
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stream: bool = Body(False, description="流式输出"),
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model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
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temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
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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"),
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prompt_name: str = Body("default",
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description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
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):
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history = [History.from_data(h) for h in history]
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query = "帮我搜索一下：" + query
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logger.info(f"agent query: {query}")
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async def agent_chat_iterator(
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query: str,
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history: Optional[List[History]],
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model_name: str = LLM_MODELS[0],
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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nonlocal max_tokens
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callback = CustomAsyncIteratorCallbackHandler()
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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model = get_ChatOpenAI(
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=[callback],
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)
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## 传入全局变量来实现agent调用
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kb_list = {x["kb_name"]: x for x in get_kb_details()}
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model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()}
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if Agent_MODEL:
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## 如果有指定使用Agent模型来完成任务
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model_agent = get_ChatOpenAI(
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model_name=Agent_MODEL,
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=[callback],
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)
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model_container.MODEL = model_agent
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else:
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model_container.MODEL = model
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prompt_template = get_prompt_template("agent_chat", prompt_name)
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type(prompt_template)
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prompt_template_agent = CustomPromptTemplate(
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template=prompt_template,
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tools=tools,
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input_variables=["input", "intermediate_steps", "history"]
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)
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output_parser = CustomOutputParser()
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llm_chain = LLMChain(llm=model, prompt=prompt_template_agent)
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# 把history转成agent的memory
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memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2)
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for message in history:
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# 检查消息的角色
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if message.role == 'user':
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# 添加用户消息
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memory.chat_memory.add_user_message(message.content)
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else:
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# 添加AI消息
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memory.chat_memory.add_ai_message(message.content)
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if "chatglm3" in model_container.MODEL.model_name:
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agent_executor = initialize_glm3_agent(
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llm=model,
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tools=tools,
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callback_manager=None,
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# Langchain Prompt is not constructed directly here, it is constructed inside the GLM3 agent.
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prompt=prompt_template,
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input_variables=["input", "intermediate_steps", "history"],
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memory=memory,
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verbose=True,
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)
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else:
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agent = LLMSingleActionAgent(
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llm_chain=llm_chain,
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output_parser=output_parser,
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stop=["\nObservation:", "Observation"],
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allowed_tools=tool_names,
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)
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agent_executor = AgentExecutor.from_agent_and_tools(agent=agent,
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tools=tools,
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verbose=True,
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memory=memory,
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)
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while True:
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try:
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task = asyncio.create_task(wrap_done(
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agent_executor.acall(query, callbacks=[callback], include_run_info=True),
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callback.done))
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break
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except:
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pass
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if stream:
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search_answer = ""
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policy_answer = ""
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async for chunk in callback.aiter():
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tools_use = []
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# Use server-sent-events to stream the response
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data = json.loads(chunk)
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if data["status"] == Status.start or data["status"] == Status.complete:
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continue
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elif data["status"] == Status.error:
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tools_use.append("\n```\n")
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tools_use.append("工具名称: " + data["tool_name"])
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tools_use.append("工具状态: " + "调用失败")
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tools_use.append("错误信息: " + data["error"])
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tools_use.append("重新开始尝试")
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tools_use.append("\n```\n")
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yield json.dumps({"tools": tools_use}, ensure_ascii=False)
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elif data["status"] == Status.tool_finish:
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tools_use.append("\n```\n")
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tools_use.append("工具名称: " + data["tool_name"])
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tools_use.append("工具状态: " + "调用成功")
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tools_use.append("工具输入: " + data["input_str"])
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if data["tool_name"] == "联网思索":
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if "政策类资料" in data["output_str"]:
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try:
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# 尝试使用ast.literal_eval来安全地解析字符串为列表
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output_arr = ast.literal_eval(data["output_str"])
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except ValueError:
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# 如果解析失败，处理错误
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print("无法解析字符串为列表")
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output_arr = []
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policy_content = ''.join(output_arr[:5])
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policy_answer = ''.join(output_arr[5:10])
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# search_content = output_arr[-2]
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search_answer_str = str(output_arr[-1])
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if "暂未找到相关资料" in search_answer_str:
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search_answer = "\n知识中心资料: 暂无"
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else:
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search_answer_arr = search_answer_str[2: len(search_answer_str) - 2].replace("\\n", "")
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search_answer = '\n'.join(search_answer_arr.split("\', \'"))
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data["output_str"] = ''.join(policy_content + policy_answer)
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print("<<<工具输出>>>\n", data["output_str"])
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elif "暂未找到相关资料" in data["output_str"]:
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print("output_str", data["output_str"])
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try:
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output_arr = ast.literal_eval(data["output_str"])
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except ValueError:
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# 如果解析失败，处理错误
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print("无法解析字符串为列表")
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output_arr = []
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# 计算列表中的列表和字符串数量
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search_content = output_arr[:0]
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search_answer = str(output_arr[-1])[2: len(output_arr[-1]) - 3]
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data["output_str"] = str(search_content)
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print("<<<工具输出>>>\n", data["output_str"])
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else:
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search_output_str = data["output_str"][2: len(data["output_str"]) - 3].replace("\\n", "")
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search_output_arr = search_output_str.split("\', [\'")
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search_content = str(search_output_arr[0])
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search_output_str1 = str(search_output_arr[1])
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search_answer = '\n'.join(search_output_str1.split("\', \'"))
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data["output_str"] = search_content
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print("<<<工具输出>>>\n", data["output_str"])
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if data["tool_name"] == "policy_knowledgebase":
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# policy_output_str = data["output_str"][2: len(data["output_str"]) - 2].replace("\n", "")
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policy_output_str = ast.literal_eval((data["output_str"].replace("\n", "\\n")))
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id_str = policy_output_str[0]
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processed_lines = [line.strip() + '\n' for line in policy_output_str[1]]
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policy_answer = id_str + '\n\n' + ''.join(processed_lines)
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# print("policy_output_str: ", policy_output_str)
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# policy_answer = '\n'.join(policy_output_str.split("\', \'"))
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# policy_answer += policy_output
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print("policy_answer：", policy_answer)
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tools_use.append("工具输出: " + data["output_str"])
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tools_use.append("\n```\n")
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# 格式化工具的输出
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yield json.dumps({"tools": tools_use}, ensure_ascii=False)
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elif data["status"] == Status.agent_finish and search_answer:
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# yield json.dumps({"final_answer": data["final_answer"] + "\n\n参考资料：\n\n" + search_answer.replace("\\n", '\n').replace("\', \'", '').replace("\'], [\'",'')}, ensure_ascii=False)
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if policy_answer:
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yield json.dumps({"final_answer": data["final_answer"] + "\n\n参考资料：\n\n" + str(policy_answer) + str(search_answer)}, ensure_ascii=False)
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else:
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yield json.dumps({"final_answer": data["final_answer"] + "\n\n参考资料：\n\n" + str(search_answer)}, ensure_ascii=False)
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print("search_answer_output：", search_answer)
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elif data["status"] == Status.agent_finish and policy_answer:
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# yield json.dumps({"final_answer": policy_answer.replace("\\n", '\n').replace("\', \'", '\n').replace("\', [\'",'\n\n')}, ensure_ascii=False)
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yield json.dumps({"final_answer": policy_answer}, ensure_ascii=False)
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print("policy_answer_output：", policy_answer)
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elif data["status"] == Status.agent_finish:
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yield json.dumps({"final_answer": data["final_answer"]}, ensure_ascii=False)
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else:
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yield json.dumps({"answer": data["llm_token"]}, ensure_ascii=False)
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else:
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answer = ""
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final_answer = ""
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async for chunk in callback.aiter():
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# Use server-sent-events to stream the response
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data = json.loads(chunk)
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if data["status"] == Status.start or data["status"] == Status.complete:
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continue
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if data["status"] == Status.error:
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answer += "\n```\n"
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answer += "工具名称: " + data["tool_name"] + "\n"
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answer += "工具状态: " + "调用失败" + "\n"
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answer += "错误信息: " + data["error"] + "\n"
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answer += "\n```\n"
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if data["status"] == Status.tool_finish:
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answer += "\n```\n"
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answer += "工具名称: " + data["tool_name"] + "\n"
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answer += "工具状态: " + "调用成功" + "\n"
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answer += "工具输入: " + data["input_str"] + "\n"
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answer += "工具输出: " + data["output_str"] + "\n"
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answer += "\n```\n"
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if data["status"] == Status.agent_finish:
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final_answer = data["final_answer"]
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else:
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answer += data["llm_token"]
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yield json.dumps({"answer": answer, "final_answer": final_answer}, ensure_ascii=False)
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await task
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return EventSourceResponse(agent_chat_iterator(query=query,
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history=history,
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model_name=model_name,
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prompt_name=prompt_name),
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)
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