[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/chat/agent_chat_new.py

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+from datetime import datetime
+import uuid
+from langchain.memory import ConversationBufferMemory
+from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
+from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
+from server.agent.tools_select import tools, tool_names, search_tool_names
+from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
+from langchain.agents import LLMSingleActionAgent, AgentExecutor
+from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
+from fastapi import Body
+from sse_starlette.sse import EventSourceResponse
+from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
+from server.chat import utils
+from server.chat.knowledge_base_name import KnowledgeBase
+from server.custom.AsyncIteratorCallbackHandlerNew import AsyncIteratorCallbackHandler
+from server.utils import replace_variables, wrap_done, get_ChatOpenAI, get_prompt_template
+from langchain.chains import LLMChain
+from typing import AsyncIterable, Optional, Union
+from server.agent.tools import rag_search
+import asyncio
+from typing import List
+from server.chat.utils import History
+import json
+from server.agent import model_container
+from server.knowledge_base.kb_service.base import get_kb_details
+import ast
+import re
+from configs import kb_config
+from configs.basic_config import *
+
+async def agent_chat_new(
+                        query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
+                        conversation_id: str = Body("", description="对话框ID"),
+                        history_len: int = Body(-1, description="从数据库中取历史消息的数量"),
+                        history: Union[int, List[History]] = Body([],
+                                                         description="历史对话，设为一个整数可以从数据库中读取历史消息",
+                                                         examples=[[
+                                                             {"role": "user",
+                                                              "content": "我们来玩成语接龙，我先来，生龙活虎"},
+                                                             {"role": "assistant", "content": "虎头虎脑"}]]
+                                                         ),
+                        stream: bool = Body(False, description="流式输出"),
+                        model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
+                        temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=2.0),
+                        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"),
+                        # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
+                        prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
+                        ):
+            async def chat_iterator() -> AsyncIterable[str]:
+                callback = AsyncIteratorCallbackHandler()
+                # 加载计划者和执行者
+                model = get_ChatOpenAI(
+                    model_name=model_name,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                    callbacks=[callback],
+                )
+                planner = load_chat_planner(model)
+                executor = load_agent_executor(model, tools, verbose=True)
+                agent1 = PlanAndExecute(planner=planner, executor=executor, verbose=True)
+
+                # 初始化 response1
+                response1 = ""
+
+                #    确保传递的输入是字典
+                input_data = {
+                    "input":"",
+                    "\"query\"": query
+                    }
+
+                async for response in agent1.run(input_data):
+                    response1 += response
+                    yield response1  # 使用 yield 逐步返回响应
+
+            return EventSourceResponse(chat_iterator())