101 lines
4.0 KiB
Python
101 lines
4.0 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 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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async def agent_chat_new(
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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_template: str = Body("default", description="使用的prompt模板内容"),
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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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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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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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if len(history)>0:
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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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else:
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buff_memory.chat_memory.add_user_message("无")
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buff_memory.chat_memory.add_ai_message("无")
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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, "time": time}),
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callback.done),
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)
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answer = ""
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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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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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) |