139 lines
7.0 KiB
Python
139 lines
7.0 KiB
Python
from langchain.memory import ConversationBufferWindowMemory
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from server.agent.tools import tools, tool_names
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from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status, dumps
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from langchain.agents import AgentExecutor, LLMSingleActionAgent
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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 fastapi.responses import StreamingResponse
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from configs.model_config import LLM_MODEL, TEMPERATURE, HISTORY_LEN
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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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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", "content": "我们来玩成语接龙，我先来，生龙活虎"},
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{"role": "assistant", "content": "虎头虎脑"}]]
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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_MODEL, 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: int = Body(1024, description="限制LLM生成Token数量，当前默认为1024"), # TODO: fastchat更新后默认值设为None，自动使用LLM支持的最大值。
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prompt_name: str = Body("agent_chat",
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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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# top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
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):
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history = [History.from_data(h) for h in history]
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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_MODEL,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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callback = CustomAsyncIteratorCallbackHandler()
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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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)
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prompt_template = CustomPromptTemplate(
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template=get_prompt_template(prompt_name),
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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)
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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:", "<|im_end|>"], # Qwen模型中使用这个
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allowed_tools=tool_names,
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)
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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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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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input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
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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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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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if data["status"] == Status.error:
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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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if data["status"] == Status.agent_action:
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yield json.dumps({"answer": "\n\n```\n\n"}, ensure_ascii=False)
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if data["status"] == Status.tool_finish:
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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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tools_use.append("工具输出: " + data["output_str"])
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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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if 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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pass
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# agent必须要steram=True,这部分暂时没有完成
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# result = []
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# async for chunk in callback.aiter():
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# data = json.loads(chunk)
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# status = data["status"]
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# if status == Status.start:
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# result.append(chunk)
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# elif status == Status.running:
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# result[-1]["llm_token"] += chunk["llm_token"]
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# elif status == Status.complete:
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# result[-1]["status"] = Status.complete
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# elif status == Status.agent_finish:
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# result.append(chunk)
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# elif status == Status.agent_finish:
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# pass
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# yield dumps(result)
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await task
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return StreamingResponse(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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media_type="text/event-stream")
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