145 lines
8.0 KiB
Python
145 lines
8.0 KiB
Python
import uuid
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from fastapi import Body
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from langchain.memory import (
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CombinedMemory,
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ConversationBufferMemory,
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ConversationSummaryMemory,
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ConversationBufferWindowMemory
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)
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from sse_starlette.sse import EventSourceResponse
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from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN
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from server.utils import wrap_done, get_ChatOpenAI
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from langchain.chains import LLMChain, ConversationChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable
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import asyncio
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import json
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from langchain.prompts.chat import ChatPromptTemplate
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from typing import List, Optional, Union
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from server.chat.utils import History
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from langchain.prompts import PromptTemplate
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from server.utils import get_prompt_template, get_format_template
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from server.memory.conversation_db_buffer_memory import ConversationBufferDBMemory
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from server.db.repository import add_message_to_db
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from server.callback_handler.conversation_callback_handler import ConversationCallbackHandler
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from datetime import datetime
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from langchain_core.messages import SystemMessage
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import time as t
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from configs.basic_config import *
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async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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conversation_id: str = Body("", description="对话框ID"),
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history_len: int = Body(-1, description="从数据库中取历史消息的数量"),
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history: Union[int, List[History]] = Body([],
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description="历史对话，设为一个整数可以从数据库中读取历史消息",
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examples=[[
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{"role": "user",
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"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_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=2.0),
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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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# 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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prompt_name: str = Body("default", 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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async def chat_iterator() -> AsyncIterable[str]:
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nonlocal history, max_tokens
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callback = AsyncIteratorCallbackHandler()
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callbacks = [callback]
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memory = None
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time = datetime.now().strftime("%Y年%m月%d日")
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# 负责保存llm response到message db
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message_id = str(uuid.uuid1())+"q"
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conversation_callback = ConversationCallbackHandler(conversation_id=conversation_id, message_id=message_id,
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chat_type="llm_chat",
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query=query)
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callbacks.append(conversation_callback)
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logger.info(f"智能对话的入参信息：query：{query}，conversation_id：{conversation_id}，history：{history}，stream：{stream}，model_name：{model_name}，temperature：{temperature}，max_tokens：{max_tokens}prompt_name：{prompt_name}")
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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if prompt_name == "Search Summary":
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model = get_ChatOpenAI(
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model_name=LLM_MODELS[1],
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=callbacks,
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)
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# print ("model info >>>", LLM_MODELS[0])
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else:
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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=callbacks,
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)
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logger.info(f"当前使用的模型为：{model_name}")
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if history and prompt_name not in ["Search Summary", "get_policy_time"]:
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history = [History.from_data(h) for h in history]
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if prompt_name == "default":
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prompt_template = get_prompt_template("llm_chat", "default_with_history")
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if prompt_name == "Policy History Assistant":
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prompt_template = get_prompt_template("llm_chat", "Policy History Assistant_with_history")
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if prompt_name == "Topic Recommend Assistant":
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prompt_template = get_prompt_template("llm_chat", "Topic Recommend Assistant_with_history")
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if prompt_name == "Abstract Assistant":
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prompt_template = get_prompt_template("llm_chat", "Abstract Assistant_with_history")
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# input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
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# chat_prompt = ChatPromptTemplate.from_messages([input_prompt])
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chat_prompt = PromptTemplate.from_template(prompt_template)
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# 把history转成memory
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buff_memory = ConversationBufferMemory(human_prefix='user', ai_prefix='assistant', memory_key="history", input_key="input")
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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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buff_memory.chat_memory.add_user_message(message.content)
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elif message.role == 'assistant':
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# 添加AI消息
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buff_memory.chat_memory.add_ai_message(message.content)
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background_memory = ConversationBufferMemory(human_prefix='user', ai_prefix='assistant', memory_key="time", input_key="input")
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message = SystemMessage(content = f'当前的时间是：{time}')
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background_memory.chat_memory.add_message(message)
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memory = CombinedMemory(memories=[background_memory, buff_memory])
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chain = ConversationChain(llm=model, verbose=True, memory=memory, prompt=chat_prompt)
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# elif conversation_id and history_len > 0: # 前端要求从数据库取历史消息
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# # 使用memory 时必须 prompt 必须含有memory.memory_key 对应的变量
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# prompt = get_prompt_template("llm_chat", "with_history")
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# chat_prompt = PromptTemplate.from_template(prompt)
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# # 根据conversation_id 获取message 列表进而拼凑 memory
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# memory = ConversationBufferDBMemory(conversation_id=conversation_id,
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# llm=model,
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# message_limit=history_len)
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else:
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prompt_template = get_prompt_template("llm_chat", prompt_name)
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input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
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# input_msg = History(role="user", content=query).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages([input_prompt])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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# print(f'智能对话的chain>>>\n{chain}\n')
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task = asyncio.create_task(wrap_done(
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chain.acall({"input": query, "time": time}),
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callback.done),
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)
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if stream:
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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield json.dumps(
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{"text": token, "message_id": message_id},
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ensure_ascii=False)
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else:
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answer = ""
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async for token in callback.aiter():
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answer += token
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yield json.dumps(
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{"text": answer, "message_id": message_id},
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ensure_ascii=False)
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await task
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return EventSourceResponse(chat_iterator())
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