更新python后端
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BBIT-Kai
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from typing import Literal
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from langchain_core.messages import AIMessage
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from langchain_core.runnables import RunnableConfig
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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from langchain_community.agent_toolkits import SQLDatabaseToolkit
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from config.llm import llm,llmThink
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from langgraph.graph import StateGraph, END
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from langchain.prompts import PromptTemplate
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from config.llm import llm
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from typing import Annotated
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from typing_extensions import TypedDict
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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from langchain_community.agent_toolkits import create_sql_agent
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from langchain.prompts import PromptTemplate
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from config.llm import llm
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from typing import Annotated
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from langgraph.graph.message import add_messages
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import os
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from langchain_tavily import TavilySearch
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from langgraph.prebuilt import ToolNode, tools_condition
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from llm.chatLLM import get_chat_response
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from typing import TypedDict
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from langgraph.graph import StateGraph, END
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from llm.summarizeLLM import getSummary
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import db.postgres as pgdb
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import db.sqlserver as sqlserver
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from typing import List, Dict
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import db.milvus as milvus
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# -------- 定义状态 --------
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class State(TypedDict):
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path: str # 开始聊天选择的路径
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memory:str # 记忆
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knowledge: str # 知识库内容
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history: str # 聊天历史
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ai_id : str # AI id
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ai_name:str # AI 名称
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ai_service: str # AI 角色 业务
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ai_role: str # AI 角色 性格特点
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kn_bases: List[str] # AI 所使用的知识库
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userInput: str # 用户输入
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reply: str # 最终回复
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# -------- 定义节点 --------
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# ------------------------------------------------------------------------ 向量数据库查询 --------
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gen_sql_prompt = PromptTemplate(
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input_variables=["userInput"],
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template = """你的任务是对用户输入进行意图分析，并将其分解成方便进行知识向量数据库搜索的关键词。
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以下是用户的输入：
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<用户输入>
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{userInput}
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</用户输入>
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在提取关键词时，请遵循以下方法和要求：
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1. 去除输入中的停用词（如“的”“是”“在”等）、语气词和无实际意义的符号。
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2. 识别输入中的核心概念、实体和关键动作。
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3. 尽量使用简洁、通用的词汇作为关键词。
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4. 确保关键词之间相互独立，不包含其他关键词。
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关键词之间用空格分隔。
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你的回答是：
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"""
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)
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sqlChain = gen_sql_prompt | llm
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def db_search(state: State):
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key_words = sqlChain.invoke({
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"userInput": state['userInput'],
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}).content
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print("关键词是：", key_words)
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knowledge = milvus.get_knowledge_by_key_words(key_words, state['kn_bases'])
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print("知识库内容是：", knowledge)
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state["knowledge"] = knowledge
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ai_ids = [state['ai_id']]
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memory = milvus.get_memory_by_key_words(key_words, ai_ids)
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print("记忆是：", memory)
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state["memory"] = memory
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return state
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# ------------------------------------------------------------------------ 意图分析 --------
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pathSelectPrompt = PromptTemplate(
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input_variables=[ "userInput","ai_service","history"],
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template = """
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你是一个意图分类器，负责判断用户提问是否与你的工作相关，进而确定是否需要去查知识库。
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以下是你负责的工作内容：
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<ai_service>
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{ai_service}
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</ai_service>
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这是你们的对话历史：
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<history>
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{history}
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</history>
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用户最新回复是：
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<userInput>
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{userInput}
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</userInput>
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判断规则如下：
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如果用户最新回复与你的负责工作相关，需要去查知识库，输出“kn”；如果不相关，则输出“chat”，不要包含任何标点符号以及空格。
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你生成的结果：
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"""
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)
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pathSelectChain = pathSelectPrompt | llmThink
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def decide_source(state: State, max_retry=3):
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"""根据用户输入选择数据来源"""
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for _ in range(max_retry):
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choice = pathSelectChain.invoke({
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"userInput": state["userInput"],
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"ai_service": state["ai_service"],
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"history": state["history"],
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}).content.strip().lower()
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print("根据用户输入选择数据来源，路径是：", choice)
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if choice in ["kn", "chat"]:
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state["path"] = choice
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break
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else:
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# 如果连续 max_retry 次都不合法，默认走 chat
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state["path"] = "chat"
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return state
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# ------------------------------------------------------------------------ !普通聊天 --------
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noChatPrompt = PromptTemplate(
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input_variables=[ "ai_name", "ai_service", "ai_role", "history"],
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template = """
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你的名字是：{ai_name}，你负责的业务是{ai_service}，你具有{ai_role}的性格特点。
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这是你和用户的对话历史
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<history>
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{history}
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</history>
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在回复用户时，请遵循以下指南:
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1. 回复要与AI角色业务相关，体现AI的专业能力。
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2. 回复内容的语气和风格要符合AI角色性格特点。
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3. 参考聊天历史，使回复具有连贯性和针对性。
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4. 回复要简洁明了，避免冗长和复杂的表述。
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你的回答：
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"""
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)
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noChatChain = noChatPrompt | llm
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def chat(state: State):
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state["reply"] = noChatChain.invoke({
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"ai_name": state["ai_name"],
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"ai_service": state["ai_service"],
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"ai_role": state["ai_role"],
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"history": state["history"],
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"userStr": state["userInput"]
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}).content
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print("直接回复")
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return state
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# ------------------------------------------------------------------------ 整理结果 --------
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summarizePrompt = PromptTemplate(
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input_variables=["ai_name", "ai_service", "ai_role", "history", "knowledge"],
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template = """
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你的任务是基于给定的AI名称、AI角色业务、AI角色性格特点和聊天历史来回复用户。请仔细阅读以下信息，并按照指示进行回复。
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你的名字是：{ai_name}，你负责的业务是{ai_service}，你具有{ai_role}的性格特点。
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这是你和用户的对话历史
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<history>
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{history}
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</history>
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这是给你参考的知识库：
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<knowledge>
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{knowledge}
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</knowledge>
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{memory}
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在回复时，请遵循以下指南：
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1. 回复内容要与你负责的业务相关。
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2. 回复的语气要结合你的性格特点。
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3. 确保回复内容清晰、简洁、有针对性。
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请生成你的回复：
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"""
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)
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summarizeChain = summarizePrompt | llm
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def summarize_ai(state: State):
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"""AI 总结输出"""
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mem = state['memory']
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if mem != "":
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memStr = """
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这是给你参考的相关历史记忆：
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<memory>
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%s
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</memory>
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""" % mem # 这里用 % 把 mem 填进去
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else:
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memStr = "没有记忆内容"
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print("历史记录是：" ,state["history"])
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state["reply"] = summarizeChain.invoke({
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"ai_role":state["ai_role"],
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"ai_name":state["ai_name"],
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"history":state["history"],
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"ai_service":state['ai_service'],
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"knowledge": state["knowledge"],
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"memory": memStr,
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}).content
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return state
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# ------------------------------------------------------------------------ 构建有向图 --------
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workflow = StateGraph(State)
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workflow.add_node("decide", decide_source)
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workflow.add_node("db_search", db_search)
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workflow.add_node("chat", chat)
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workflow.add_node("summarize", summarize_ai)
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workflow.set_entry_point("decide")
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# 条件边：根据 path 决定走向
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workflow.add_conditional_edges(
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"decide",
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lambda state: state["path"], # 返回 state["path"] 的值
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{
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"kn": "db_search",
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"chat": "chat",
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}
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)
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workflow.add_edge("db_search", "summarize")
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workflow.add_edge("summarize", END)
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workflow.add_edge("chat", END)
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graph = workflow.compile()
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# 执行函数
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def get_service_agent_reply(aiId:str, userInput: str,history:str, kn_bases:List[str]) :
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json = pgdb.get_ai_personality(aiId)
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ai_service = json["业务"]
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ai_role = json["性格"]
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ai_name = json["名字"]
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print("AI Name:", ai_name)
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print("AI Service:", ai_service)
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final_state = graph.invoke({
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"ai_service":ai_service,
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"ai_role":ai_role,
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"ai_name":ai_name,
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"history":history,
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"kn_bases":kn_bases,
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"table_info": pgdb.get_available_tables_str(aiId),
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"userInput": userInput,
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"ai_id": aiId,
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})
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return final_state["reply"]
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