75 lines
4.1 KiB
Python
75 lines
4.1 KiB
Python
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from datetime import datetime
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import uuid
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from langchain.memory import ConversationBufferMemory
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from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
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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.chat import utils
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from server.chat.knowledge_base_name import KnowledgeBase
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from server.custom.AsyncIteratorCallbackHandlerNew import AsyncIteratorCallbackHandler
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from server.utils import replace_variables, 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, Union
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from server.agent.tools import rag_search
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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 import kb_config
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from configs.basic_config import *
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async def agent_chat_new(
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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_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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callback = AsyncIteratorCallbackHandler()
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# 加载计划者和执行者
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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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planner = load_chat_planner(model)
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executor = load_agent_executor(model, tools, verbose=True)
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agent1 = PlanAndExecute(planner=planner, executor=executor, verbose=True)
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# 初始化 response1
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response1 = ""
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# 确保传递的输入是字典
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input_data = {
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"input":"",
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"\"query\"": query
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}
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async for response in agent1.run(input_data):
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response1 += response
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yield response1 # 使用 yield 逐步返回响应
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return EventSourceResponse(chat_iterator())
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