249 lines
14 KiB
Python
249 lines
14 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 server.utils import replace_variables
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from configs.basic_config import *
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from configs.outline_config import outlines
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MAX_RETRIES = 2
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RETRY_DELAY = 1
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async def solve_problem(
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user_prompt_name: Optional[str] = Body(None, description="用户输入"),
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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: Union[int, List[History]] = Body([], description="历史对话"),
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model_name: str = Body("default_model", description="LLM 模型名称。"),
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temperature: float = Body(0.7, 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"),
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prompt_name: str = Body("default", description="使用的prompt模板名称"),
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stream: bool = Body(False, description="流式输出")
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) -> AsyncIterable[str]:
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callback = AsyncIteratorCallbackHandler()
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callbacks = [callback]
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time = datetime.now().strftime("%Y年%m月%d日")
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message_id = str(uuid.uuid1())+"q"
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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 == "solve_problem":
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kwargs = {}
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kwargs["extra_body"] = {"chat_template_kwargs": {"enable_thinking": True}}
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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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**kwargs
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)
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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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if history and not prompt_name == "history_route":
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if prompt_name == "solve_problem":
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user_prompt = get_prompt_template("llm_chat", user_prompt_name+"_with_history")
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prompt_template = get_prompt_template("llm_chat", "solve_problem_history")
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prompt_template = replace_variables(prompt_template, replace_content=user_prompt, replace_param="{user_prompt}")
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elif prompt_name == "solve_problem_outline":
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prompt_template = get_prompt_template("llm_chat", "solve_problem_outline_history")
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elif prompt_name == "outlines_route":
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prompt_template = get_prompt_template("llm_chat", "outlines_route_with_history")
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outline_detail = [f"\"index\": \"{outline['index']}\", \"title\": \"{outline['title']}\", \"summary\": \"{outline['summary']}\""for outline in outlines[:-1]]
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prompt_template = replace_variables(prompt_template, replace_content=str(outline_detail), replace_param="{outlines}")
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else:
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prompt_template = get_prompt_template("llm_chat", "think_route_history")
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if user_prompt_name:
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user_prompt = get_prompt_template("llm_chat", user_prompt_name+"_with_history")
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prompt_template = replace_variables(prompt_template, replace_content=user_prompt, replace_param="{user_prompt}")
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history = [History.from_data(h) for h in history]
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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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else:
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prompt_template = get_prompt_template("llm_chat", prompt_name)
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if user_prompt_name and prompt_name == "think_route":
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user_prompt = get_prompt_template("llm_chat", user_prompt_name)
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prompt_template = replace_variables(prompt_template, replace_content=user_prompt, replace_param="{user_prompt}")
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if prompt_name == "history_route":
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history = [History.from_data(h) for h in history]
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buff_memory = ConversationBufferMemory(human_prefix='user', ai_prefix='assistant', memory_key="history", input_key="input")
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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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prompt_template = replace_variables(prompt_template, replace_content=str(history), replace_param="{history_summary}")
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if prompt_name == "outlines_route":
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outline_detail = [f"\"index\": \"{outline['index']}\", \"title\": \"{outline['title']}\", \"summary\": \"{outline['summary']}\""for outline in outlines[:-1]]
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prompt_template = replace_variables(prompt_template, replace_content=str(outline_detail), replace_param="{outlines}")
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if prompt_name == "solve_problem_outline":
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prompt_template = get_prompt_template("llm_chat", "solve_problem_outline")
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prompt_template = replace_variables(prompt_template, replace_content=datetime.now().strftime("%Y"), replace_param="{year}")
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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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chain = LLMChain(prompt=chat_prompt, llm=model,verbose=True)
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# 保存创建 chain 所需的信息，用于重试
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chain_kwargs = {
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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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"prompt_name": prompt_name,
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"user_prompt_name": user_prompt_name,
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}
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# 判断是否有历史对话
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has_history = history and not prompt_name == "history_route"
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use_conversation_chain = has_history
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answer = ""
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retry_count = 0
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while retry_count <= MAX_RETRIES:
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try:
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# 重新创建 callback 和 model
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callback = AsyncIteratorCallbackHandler()
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callbacks = [callback]
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if prompt_name == "solve_problem":
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kwargs = {}
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kwargs["extra_body"] = {"chat_template_kwargs": {"enable_thinking": True}}
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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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**kwargs
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)
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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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# 重新创建 chain
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if use_conversation_chain:
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if prompt_name == "solve_problem":
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user_prompt = get_prompt_template("llm_chat", user_prompt_name+"_with_history")
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prompt_template = get_prompt_template("llm_chat", "solve_problem_history")
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prompt_template = replace_variables(prompt_template, replace_content=user_prompt, replace_param="{user_prompt}")
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elif prompt_name == "solve_problem_outline":
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prompt_template = get_prompt_template("llm_chat", "solve_problem_outline_history")
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elif prompt_name == "outlines_route":
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prompt_template = get_prompt_template("llm_chat", "outlines_route_with_history")
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outline_detail = [f"\"index\": \"{outline['index']}\", \"title\": \"{outline['title']}\", \"summary\": \"{outline['summary']}\""for outline in outlines[:-1]]
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prompt_template = replace_variables(prompt_template, replace_content=str(outline_detail), replace_param="{outlines}")
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else:
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prompt_template = get_prompt_template("llm_chat", "think_route_history")
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if user_prompt_name:
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user_prompt = get_prompt_template("llm_chat", user_prompt_name+"_with_history")
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prompt_template = replace_variables(prompt_template, replace_content=user_prompt, replace_param="{user_prompt}")
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chat_prompt = PromptTemplate.from_template(prompt_template)
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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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if message.role == 'user':
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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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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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task = asyncio.create_task(wrap_done(
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chain.acall({"input": query}),
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callback.done),
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)
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else:
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prompt_template = get_prompt_template("llm_chat", prompt_name)
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if user_prompt_name and prompt_name == "think_route":
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user_prompt = get_prompt_template("llm_chat", user_prompt_name)
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prompt_template = replace_variables(prompt_template, replace_content=user_prompt, replace_param="{user_prompt}")
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if prompt_name == "history_route":
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buff_memory = ConversationBufferMemory(human_prefix='user', ai_prefix='assistant', memory_key="history", input_key="input")
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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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prompt_template = replace_variables(prompt_template, replace_content=str(history), replace_param="{history_summary}")
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if prompt_name == "outlines_route":
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outline_detail = [f"\"index\": \"{outline['index']}\", \"title\": \"{outline['title']}\", \"summary\": \"{outline['summary']}\""for outline in outlines[:-1]]
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prompt_template = replace_variables(prompt_template, replace_content=str(outline_detail), replace_param="{outlines}")
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if prompt_name == "solve_problem_outline":
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prompt_template = get_prompt_template("llm_chat", "solve_problem_outline")
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prompt_template = replace_variables(prompt_template, replace_content=datetime.now().strftime("%Y"), replace_param="{year}")
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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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chain = LLMChain(prompt=chat_prompt, llm=model, verbose=True)
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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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async for token in callback.aiter():
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if stream:
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yield json.dumps({"text": token}, ensure_ascii=False)
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else:
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answer += token
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logger.info(f'solve_problem: {str(answer)}')
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await task
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break
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except Exception as e:
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retry_count += 1
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if retry_count > MAX_RETRIES:
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logger.error(f"流式传输失败，已达到最大重试次数 {MAX_RETRIES}: {e}")
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raise
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logger.warning(f"流式传输第 {retry_count} 次失败，{RETRY_DELAY}秒后重试: {e}")
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await asyncio.sleep(RETRY_DELAY)
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if stream:
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return
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else:
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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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) |