升级新库
This commit is contained in:
BBIT-Kai
@@ -1,10 +1,10 @@
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# 使用官方 Python 镜像
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FROM python:3.10-slim
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FROM ubuntu:22.04
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WORKDIR /app
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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ca-certificates \
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libpq5 \
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unixodbc \
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curl \
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@@ -20,31 +20,4 @@ RUN apt-get update && \
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ACCEPT_EULA=Y apt-get install -y msodbcsql18 && \
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rm -rf /var/lib/apt/lists/*
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COPY app/requirements.txt .
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# 安装 Python 依赖
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RUN pip install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
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RUN python -m pip uninstall -y opencv-python
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RUN python -m pip install opencv-python-headless
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# 复制并解压 JRE
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COPY docker/OpenJDK17U-jre_x64_linux_hotspot_17.0.16_8.tar.gz /opt/
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RUN tar -xzf /opt/OpenJDK17U-jre_x64_linux_hotspot_17.0.16_8.tar.gz -C /opt/ && \
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rm /opt/OpenJDK17U-jre_x64_linux_hotspot_17.0.16_8.tar.gz
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# 配置 Java 环境
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ENV JAVA_HOME=/opt/jdk-17.0.16+8-jre
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ENV PATH="$JAVA_HOME/bin:$PATH"
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# 复制项目代码
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COPY app/ .
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# 复制 pyzxing 的 jar 文件到默认路径
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COPY docker/javase-3.4.1-SNAPSHOT-jar-with-dependencies.jar /root/.local/pyzxing/javase-3.4.1-SNAPSHOT-jar-with-dependencies.jar
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EXPOSE 13011
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# 启动命令（使用 uvicorn 启动 FastAPI）
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CMD ["python", "app.py"]
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COPY app/ /app
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@@ -1,41 +1,34 @@
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from langchain.prompts import PromptTemplate
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from config.llm import llm
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from config.ssDb import ssDBLC
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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 config.ssDb import ssDBLC
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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 langchain_community.agent_toolkits import create_sql_agent
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from langchain_core.prompts import PromptTemplate
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from langchain_tavily import TavilySearch
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from langgraph.graph import START
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from langgraph.graph import StateGraph, END
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from config.llm import llm
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from config.ssDb import ssDBLC
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from llm.chatLLM import get_chat_response
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from llm.summarizeLLM import getSummary
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# -------- 定义状态 --------
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class State(TypedDict):
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userInput: str # 用户输入
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source: str # 选择的数据来源：web 或 db 或 chat
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infomation: str # 查询到的内容
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aiRole: str # AI 角色
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history: str # 聊天历史
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reply: str # 最终回复
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userInput: str # 用户输入
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source: str # 选择的数据来源：web 或 db 或 chat
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infomation: str # 查询到的内容
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aiRole: str # AI 角色
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history: str # 聊天历史
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reply: str # 最终回复
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# -------- 定义节点 --------
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# ------------------------------------------------------------------------ 路径选择 --------
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pathSelectPrompt = PromptTemplate(
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input_variables=["aiRole", "history", "userStr", "infomation"],
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template = """
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template="""
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你是主干信息科技有限公司的业务员，是一家蚕桑服务公司，现在需要根据用户输入来判断应该使用哪种方式来回答用户的问题。
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你有三种选择：
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1. 如果用户的问题涉及最新的信息，比如新闻、事件、天气等涉及时间的内容时，请选择 "web
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@@ -45,19 +38,26 @@ pathSelectPrompt = PromptTemplate(
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用户最新输入：
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{userStr}
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请做出你的选择：
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"""
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""",
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)
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pathSelectChain = pathSelectPrompt | llm
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def decide_source(state: State, max_retry=3):
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print("根据用户输入选择数据来源，用户输入：", state["userInput"])
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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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"aiRole": state["aiRole"],
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"history": state["history"],
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"userStr": state["userInput"],
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}).content.strip().lower()
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choice = (
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pathSelectChain.invoke(
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{
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"aiRole": state["aiRole"],
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"history": state["history"],
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"userStr": state["userInput"],
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}
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)
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.content.strip()
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.lower()
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)
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if choice in ["web", "db", "chat"]:
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state["source"] = choice
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break
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@@ -72,36 +72,45 @@ def decide_source(state: State, max_retry=3):
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os.environ["TAVILY_API_KEY"] = "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"
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tool = TavilySearch(max_results=2)
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def fetch_web(state: State):
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result = tool.invoke(state["userInput"])
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state["infomation"] = result.get("content") or result
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state["infomation"] = result.get("content") or result
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print("调用了联网工具，结果是：", state["infomation"])
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return state
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# ------------------------------------------------------------------------ 数据库查询 --------
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agent = create_sql_agent(
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llm=llm,
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db=ssDBLC,
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agent_type="tool-calling",
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verbose=True
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)
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agent = create_sql_agent(llm=llm, db=ssDBLC, agent_type="tool-calling", verbose=True)
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def fetch_db(state: State):
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state["infomation"] = agent.invoke({"input": state["userInput"]})["output"]
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print("调用了数据库工具，结果是：", state["infomation"])
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return state
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# ------------------------------------------------------------------------ 整理结果 --------
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def summarize_ai(state: State):
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"""AI 总结输出"""
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state["reply"] = getSummary(aiRole=state["aiRole"], history=state["history"], userInput= state["userInput"], infomation= state["infomation"])
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state["reply"] = getSummary(
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aiRole=state["aiRole"],
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history=state["history"],
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userInput=state["userInput"],
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infomation=state["infomation"],
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)
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return state
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# ------------------------------------------------------------------------ 普通聊天 --------
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def chat(state: State):
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state["reply"] = get_chat_response(aiRole=state["aiRole"],history=state["history"], userInput= state["userInput"]).content
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state["reply"] = get_chat_response(
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aiRole=state["aiRole"], history=state["history"], userInput=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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# ------------------------------------------------------------------------ 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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@@ -119,21 +128,20 @@ workflow.add_edge("fetch_db", "summarize")
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workflow.add_conditional_edges(
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"decide",
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lambda state: state["source"], # 返回 state["source"] 的值
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{
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"web": "fetch_web",
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"chat": "chat",
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"db": "fetch_db"
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}
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{"web": "fetch_web", "chat": "chat", "db": "fetch_db"},
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)
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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_graph_output(aiRole:str,history: str, userInput: str) -> str:
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final_state = graph.invoke({
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"aiRole":aiRole,
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"history": history,
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"userInput": userInput,
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})
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return final_state["reply"]
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def get_graph_output(aiRole: str, history: str, userInput: str) -> str:
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final_state = graph.invoke(
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{
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"aiRole": aiRole,
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"history": history,
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"userInput": userInput,
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}
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)
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return final_state["reply"]
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@@ -1,31 +1,12 @@
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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 langchain_core.prompts import PromptTemplate
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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 config.llm import llm
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from config.llm import llmThink
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# -------- 定义状态 --------
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@@ -69,7 +50,7 @@ gen_sql_prompt = PromptTemplate(
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请直接输出完整可执行的 SQL 语句，不要任何其他文字或格式化，例如反引号或 ```sql。
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"""
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""",
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)
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sqlChain = gen_sql_prompt | llm
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@@ -89,33 +70,34 @@ fix_prompt = PromptTemplate(
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# 输出要求
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只返回修正后的 SQL 语句，不包含任何额外的解释或说明。
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"""
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""",
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)
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fixSQLChain = fix_prompt | llm
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def sql(state: State):
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if state["isFirstGenSQL"]:
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state['sql'] = sql_1(state)
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state["sql"] = sql_1(state)
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else:
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state['sql'] = sql_2(state)
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state["sql"] = sql_2(state)
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for attempt in range(2):
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try:
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# 执行 SQL
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result = sqlserver.executeSQL(state['sql'])
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state['sql_result'] = result
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result = sqlserver.executeSQL(state["sql"])
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state["sql_result"] = result
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# print("SQL 执行成功，结果:", result)
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break
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except Exception as e:
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error_msg = str(e)
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print(f"SQL 执行出错: {error_msg}")
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# 调用 LLM 修正 SQL
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state['sql'] = fixSQLChain.invoke({
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"sql": state['sql'],
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"error_msg": error_msg,
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"table_info": state['table_info'],
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"tenant_id": state['tenant_id']
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}
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state["sql"] = fixSQLChain.invoke(
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{
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"sql": state["sql"],
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"error_msg": error_msg,
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"table_info": state["table_info"],
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"tenant_id": state["tenant_id"],
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}
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).content
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# print(f"LLM 生成修正 SQL: {state['sql']}")
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else:
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@@ -124,11 +106,13 @@ def sql(state: State):
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def sql_1(state: State):
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return sqlChain.invoke({
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"table_info": state['table_info'],
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"userInput": state["userInput"],
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"tenant_id": state['tenant_id']
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}).content
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return sqlChain.invoke(
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{
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"table_info": state["table_info"],
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"userInput": state["userInput"],
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"tenant_id": state["tenant_id"],
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}
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).content
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improve_sql_prompt = PromptTemplate(
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@@ -151,18 +135,20 @@ improve_sql_prompt = PromptTemplate(
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7. 通常来说，不查询对用户来说意义不大的字段，比如主键、外键、id等。
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8. 查询的SQL字段要用别名，取名参考描述。
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9. 一般情况下，如果能限制租户Id（通常为tenantid 字段），则尽量加上WHERE tenantid = {tenant_id}。
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"""
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""",
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)
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improveSqlChain = improve_sql_prompt | llm
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def sql_2(state: State):
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return improveSqlChain.invoke({
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"sql": state['sql'],
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"table_info": state['table_info'],
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"userInput": state["userInput"],
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"tenant_id": state['tenant_id']
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}).content
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return improveSqlChain.invoke(
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{
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"sql": state["sql"],
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"table_info": state["table_info"],
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"userInput": state["userInput"],
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"tenant_id": state["tenant_id"],
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}
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).content
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# ------------------------------------------------------------------------ 路径选择 --------
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@@ -199,7 +185,7 @@ chat → 无法直接生成 SQL，需要进一步解释或澄清。
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回答内容仅限于db或者chat，请勿输出其他内容。
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你的回复：
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"""
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""",
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)
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pathSelectChain = pathSelectPrompt | llmThink
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@@ -207,12 +193,18 @@ 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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"table_info": state["table_info"],
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"ai_service": state["ai_service"],
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"sql": state["sql"]
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}).content.strip().lower()
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choice = (
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pathSelectChain.invoke(
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{
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"userInput": state["userInput"],
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"table_info": state["table_info"],
|
||||
"ai_service": state["ai_service"],
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"sql": state["sql"],
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}
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)
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.content.strip()
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.lower()
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)
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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 ["db", "chat"]:
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state["path"] = choice
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@@ -242,17 +234,16 @@ noChatPrompt = PromptTemplate(
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3. 引导用户提出与你业务相关的问题。
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4. 使用礼貌和友好的语气。
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||||
你的回答：
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||||
"""
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||||
""",
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)
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noChatChain = noChatPrompt | llm
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||||
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||||
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def chat(state: State):
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state["reply"] = noChatChain.invoke({
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"userInput": state["userInput"],
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"ai_service": state["ai_service"]
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}).content
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state["reply"] = noChatChain.invoke(
|
||||
{"userInput": state["userInput"], "ai_service": state["ai_service"]}
|
||||
).content
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print("直接回复")
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return state
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@@ -291,19 +282,21 @@ summarizePrompt = PromptTemplate(
|
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2. 提供进一步可选的查询示例，基于当前的数据库表结构，引导用户提出更具体需求。
|
||||
|
||||
你的回复：
|
||||
"""
|
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""",
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)
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summarizeChain = summarizePrompt | llm
|
||||
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||||
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def summarize_ai(state: State):
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"""AI 总结输出"""
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||||
state["reply"] = summarizeChain.invoke({
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||||
"ai_role": state["ai_role"],
|
||||
"sql": state['sql'],
|
||||
"userInput": state['userInput'],
|
||||
"table_info": state['table_info'],
|
||||
}).content
|
||||
state["reply"] = summarizeChain.invoke(
|
||||
{
|
||||
"ai_role": state["ai_role"],
|
||||
"sql": state["sql"],
|
||||
"userInput": state["userInput"],
|
||||
"table_info": state["table_info"],
|
||||
}
|
||||
).content
|
||||
return state
|
||||
|
||||
|
||||
@@ -322,7 +315,7 @@ workflow.add_conditional_edges(
|
||||
{
|
||||
"db": "sql_1",
|
||||
"chat": "chat",
|
||||
}
|
||||
},
|
||||
)
|
||||
workflow.add_edge("summarize", END)
|
||||
workflow.add_edge("chat", END)
|
||||
@@ -334,13 +327,15 @@ def get_db_agent_reply(aiId: str, userInput: str, tenant_id: str, sql: str = "")
|
||||
json = pgdb.get_ai_personality(aiId)
|
||||
ai_service = json["业务"]
|
||||
ai_role = json["性格"]
|
||||
final_state = graph.invoke({
|
||||
"ai_service": ai_service,
|
||||
"ai_role": ai_role,
|
||||
"table_info": pgdb.get_available_tables_str(aiId),
|
||||
"tenant_id": tenant_id,
|
||||
"userInput": userInput,
|
||||
"sql": sql,
|
||||
"isFirstGenSQL": sql == "",
|
||||
})
|
||||
final_state = graph.invoke(
|
||||
{
|
||||
"ai_service": ai_service,
|
||||
"ai_role": ai_role,
|
||||
"table_info": pgdb.get_available_tables_str(aiId),
|
||||
"tenant_id": tenant_id,
|
||||
"userInput": userInput,
|
||||
"sql": sql,
|
||||
"isFirstGenSQL": sql == "",
|
||||
}
|
||||
)
|
||||
return final_state
|
||||
|
||||
@@ -1,59 +1,39 @@
|
||||
|
||||
from typing import Literal
|
||||
from langchain_core.messages import AIMessage
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
from langgraph.graph import END, START, MessagesState, StateGraph
|
||||
from langgraph.prebuilt import ToolNode
|
||||
from langchain_community.agent_toolkits import SQLDatabaseToolkit
|
||||
from config.llm import llm,llmThink
|
||||
from langgraph.graph import StateGraph, END
|
||||
from langchain.prompts import PromptTemplate
|
||||
from config.llm import llm
|
||||
from typing import Annotated
|
||||
from typing_extensions import TypedDict
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
from langchain_community.agent_toolkits import create_sql_agent
|
||||
from langchain.prompts import PromptTemplate
|
||||
from config.llm import llm
|
||||
from typing import Annotated
|
||||
from langgraph.graph.message import add_messages
|
||||
import os
|
||||
from langchain_tavily import TavilySearch
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
from llm.chatLLM import get_chat_response
|
||||
from typing import List
|
||||
from typing import TypedDict
|
||||
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
from langgraph.graph import StateGraph, END
|
||||
from llm.summarizeLLM import getSummary
|
||||
import db.postgres as pgdb
|
||||
import db.sqlserver as sqlserver
|
||||
from typing import List, Dict
|
||||
|
||||
import db.milvus as milvus
|
||||
import db.postgres as pgdb
|
||||
from config.llm import llm
|
||||
from config.llm import llmThink
|
||||
|
||||
|
||||
# -------- 定义状态 --------
|
||||
class State(TypedDict):
|
||||
path: str # 开始聊天选择的路径
|
||||
path: str # 开始聊天选择的路径
|
||||
|
||||
memory:str # 记忆
|
||||
knowledge: str # 知识库内容
|
||||
history: str # 聊天历史
|
||||
memory: str # 记忆
|
||||
knowledge: str # 知识库内容
|
||||
history: str # 聊天历史
|
||||
|
||||
ai_id : str # AI id
|
||||
ai_name:str # AI 名称
|
||||
ai_service: str # AI 角色 业务
|
||||
ai_role: str # AI 角色 性格特点
|
||||
kn_bases: List[str] # AI 所使用的知识库
|
||||
ai_id: str # AI id
|
||||
ai_name: str # AI 名称
|
||||
ai_service: str # AI 角色 业务
|
||||
ai_role: str # AI 角色 性格特点
|
||||
kn_bases: List[str] # AI 所使用的知识库
|
||||
|
||||
userInput: str # 用户输入
|
||||
reply: str # 最终回复
|
||||
|
||||
userInput: str # 用户输入
|
||||
reply: str # 最终回复
|
||||
|
||||
# -------- 定义节点 --------
|
||||
# ------------------------------------------------------------------------ 向量数据库查询 --------
|
||||
|
||||
gen_sql_prompt = PromptTemplate(
|
||||
input_variables=["userInput"],
|
||||
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。
|
||||
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。
|
||||
以下是用户的输入：
|
||||
<用户输入>
|
||||
{userInput}
|
||||
@@ -65,28 +45,33 @@ gen_sql_prompt = PromptTemplate(
|
||||
4. 确保关键词之间相互独立，不包含其他关键词。
|
||||
关键词之间用空格分隔。
|
||||
你的回答是：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
sqlChain = gen_sql_prompt | llm
|
||||
|
||||
|
||||
def db_search(state: State):
|
||||
key_words = sqlChain.invoke({
|
||||
"userInput": state['userInput'],
|
||||
}).content
|
||||
key_words = sqlChain.invoke(
|
||||
{
|
||||
"userInput": state["userInput"],
|
||||
}
|
||||
).content
|
||||
print("关键词是：", key_words)
|
||||
knowledge = milvus.get_knowledge_by_key_words(key_words, state['kn_bases'])
|
||||
knowledge = milvus.get_knowledge_by_key_words(key_words, state["kn_bases"])
|
||||
print("知识库内容是：", knowledge)
|
||||
state["knowledge"] = knowledge
|
||||
ai_ids = [state['ai_id']]
|
||||
ai_ids = [state["ai_id"]]
|
||||
memory = milvus.get_memory_by_key_words(key_words, ai_ids)
|
||||
print("记忆是：", memory)
|
||||
state["memory"] = memory
|
||||
return state
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------ 意图分析 --------
|
||||
|
||||
pathSelectPrompt = PromptTemplate(
|
||||
input_variables=[ "userInput","ai_service","history"],
|
||||
template = """
|
||||
input_variables=["userInput", "ai_service", "history"],
|
||||
template="""
|
||||
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。
|
||||
以下是你负责的工作内容：
|
||||
<ai_service>
|
||||
@@ -103,17 +88,25 @@ pathSelectPrompt = PromptTemplate(
|
||||
判断规则如下：
|
||||
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。
|
||||
你生成的结果：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
pathSelectChain = pathSelectPrompt | llmThink
|
||||
|
||||
|
||||
def decide_source(state: State, max_retry=3):
|
||||
"""根据用户输入选择数据来源"""
|
||||
for _ in range(max_retry):
|
||||
choice = pathSelectChain.invoke({
|
||||
"userInput": state["userInput"],
|
||||
"ai_service": state["ai_service"],
|
||||
"history": state["history"],
|
||||
}).content.strip().lower()
|
||||
choice = (
|
||||
pathSelectChain.invoke(
|
||||
{
|
||||
"userInput": state["userInput"],
|
||||
"ai_service": state["ai_service"],
|
||||
"history": state["history"],
|
||||
}
|
||||
)
|
||||
.content.strip()
|
||||
.lower()
|
||||
)
|
||||
print("根据用户输入选择数据来源，路径是：", choice)
|
||||
if choice in ["kn", "chat"]:
|
||||
state["path"] = choice
|
||||
@@ -123,10 +116,11 @@ def decide_source(state: State, max_retry=3):
|
||||
state["path"] = "chat"
|
||||
return state
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------ !普通聊天 --------
|
||||
noChatPrompt = PromptTemplate(
|
||||
input_variables=[ "ai_name", "ai_service", "ai_role", "history"],
|
||||
template = """
|
||||
input_variables=["ai_name", "ai_service", "ai_role", "history"],
|
||||
template="""
|
||||
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。
|
||||
|
||||
这是你和用户的对话历史
|
||||
@@ -140,26 +134,31 @@ noChatPrompt = PromptTemplate(
|
||||
4. 回复要简洁明了，避免冗长和复杂的表述。
|
||||
|
||||
你的回答：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
|
||||
noChatChain = noChatPrompt | llm
|
||||
|
||||
|
||||
def chat(state: State):
|
||||
state["reply"] = noChatChain.invoke({
|
||||
"ai_name": state["ai_name"],
|
||||
"ai_service": state["ai_service"],
|
||||
"ai_role": state["ai_role"],
|
||||
"history": state["history"],
|
||||
"userStr": state["userInput"]
|
||||
}).content
|
||||
state["reply"] = noChatChain.invoke(
|
||||
{
|
||||
"ai_name": state["ai_name"],
|
||||
"ai_service": state["ai_service"],
|
||||
"ai_role": state["ai_role"],
|
||||
"history": state["history"],
|
||||
"userStr": state["userInput"],
|
||||
}
|
||||
).content
|
||||
print("直接回复")
|
||||
return state
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------ 整理结果 --------
|
||||
|
||||
summarizePrompt = PromptTemplate(
|
||||
input_variables=["ai_name", "ai_service", "ai_role", "history", "knowledge"],
|
||||
template = """
|
||||
template="""
|
||||
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。
|
||||
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。
|
||||
|
||||
@@ -177,32 +176,40 @@ summarizePrompt = PromptTemplate(
|
||||
2. 回复的语气要结合你的性格特点。
|
||||
3. 确保回复内容清晰、简洁、有针对性。
|
||||
请生成你的回复：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
summarizeChain = summarizePrompt | llm
|
||||
|
||||
|
||||
def summarize_ai(state: State):
|
||||
"""AI 总结输出"""
|
||||
mem = state['memory']
|
||||
mem = state["memory"]
|
||||
if mem != "":
|
||||
memStr = """
|
||||
memStr = (
|
||||
"""
|
||||
这是给你参考的相关历史记忆：
|
||||
<memory>
|
||||
%s
|
||||
</memory>
|
||||
""" % mem # 这里用 % 把 mem 填进去
|
||||
"""
|
||||
% mem
|
||||
) # 这里用 % 把 mem 填进去
|
||||
else:
|
||||
memStr = "没有记忆内容"
|
||||
print("历史记录是：" ,state["history"])
|
||||
state["reply"] = summarizeChain.invoke({
|
||||
"ai_role":state["ai_role"],
|
||||
"ai_name":state["ai_name"],
|
||||
"history":state["history"],
|
||||
"ai_service":state['ai_service'],
|
||||
"knowledge": state["knowledge"],
|
||||
"memory": memStr,
|
||||
}).content
|
||||
print("历史记录是：", state["history"])
|
||||
state["reply"] = summarizeChain.invoke(
|
||||
{
|
||||
"ai_role": state["ai_role"],
|
||||
"ai_name": state["ai_name"],
|
||||
"history": state["history"],
|
||||
"ai_service": state["ai_service"],
|
||||
"knowledge": state["knowledge"],
|
||||
"memory": memStr,
|
||||
}
|
||||
).content
|
||||
return state
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------ 构建有向图 --------
|
||||
workflow = StateGraph(State)
|
||||
workflow.add_node("decide", decide_source)
|
||||
@@ -217,30 +224,35 @@ workflow.add_conditional_edges(
|
||||
{
|
||||
"kn": "db_search",
|
||||
"chat": "chat",
|
||||
}
|
||||
},
|
||||
)
|
||||
workflow.add_edge("db_search", "summarize")
|
||||
workflow.add_edge("summarize", END)
|
||||
workflow.add_edge("chat", END)
|
||||
graph = workflow.compile()
|
||||
|
||||
|
||||
# 执行函数
|
||||
def get_service_agent_reply(aiId:str, userInput: str,history:str, kn_bases:List[str]) :
|
||||
def get_service_agent_reply(
|
||||
aiId: str, userInput: str, history: str, kn_bases: List[str]
|
||||
):
|
||||
json = pgdb.get_ai_personality(aiId)
|
||||
ai_service = json["业务"]
|
||||
ai_role = json["性格"]
|
||||
ai_role = json["性格"]
|
||||
ai_name = json["名字"]
|
||||
print("AI Name:", ai_name)
|
||||
print("AI Service:", ai_service)
|
||||
|
||||
final_state = graph.invoke({
|
||||
"ai_service":ai_service,
|
||||
"ai_role":ai_role,
|
||||
"ai_name":ai_name,
|
||||
"history":history,
|
||||
"kn_bases":kn_bases,
|
||||
"table_info": pgdb.get_available_tables_str(aiId),
|
||||
"userInput": userInput,
|
||||
"ai_id": aiId,
|
||||
})
|
||||
return final_state["reply"]
|
||||
|
||||
final_state = graph.invoke(
|
||||
{
|
||||
"ai_service": ai_service,
|
||||
"ai_role": ai_role,
|
||||
"ai_name": ai_name,
|
||||
"history": history,
|
||||
"kn_bases": kn_bases,
|
||||
"table_info": pgdb.get_available_tables_str(aiId),
|
||||
"userInput": userInput,
|
||||
"ai_id": aiId,
|
||||
}
|
||||
)
|
||||
return final_state["reply"]
|
||||
|
||||
@@ -40,17 +40,14 @@ if sys.platform.lower() == "win32" or os.name.lower() == "nt":
|
||||
|
||||
|
||||
def get_device_id_simple():
|
||||
try:
|
||||
with open("/etc/machine-id") as f:
|
||||
mid = f.read().strip()
|
||||
if mid:
|
||||
return mid
|
||||
except Exception:
|
||||
pass
|
||||
hostname = socket.gethostname()
|
||||
mac = uuid.getnode()
|
||||
mac_str = ":".join(f"{(mac >> ele) & 0xff:02x}" for ele in range(40, -1, -8))
|
||||
return f"{hostname}|{mac_str}"
|
||||
hostname = os.getenv("HOST_NAME")
|
||||
if not hostname:
|
||||
hostname = socket.gethostname()
|
||||
mac = uuid.getnode()
|
||||
mac_str = ":".join(f"{(mac >> ele) & 0xff:02x}" for ele in range(40, -1, -8))
|
||||
return f"{hostname}|{mac_str}"
|
||||
else:
|
||||
return hostname
|
||||
|
||||
|
||||
# todo 这里需要订阅状态信息 设备发送信息 这里回复 vue前端发送指令 后端发送指令 设备接收指令
|
||||
|
||||
+22
-23
@@ -1,9 +1,10 @@
|
||||
from config.milvus import knVectorstore,memVectorstore
|
||||
from langchain.schema import Document
|
||||
from datetime import datetime
|
||||
from typing import List
|
||||
|
||||
from typing import List, Dict, Any
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from config.milvus import knVectorstore, memVectorstore
|
||||
|
||||
|
||||
def get_knowledge_by_key_words(key_words: str, kn_ids: List[str]) -> str:
|
||||
"""
|
||||
@@ -15,13 +16,11 @@ def get_knowledge_by_key_words(key_words: str, kn_ids: List[str]) -> str:
|
||||
expr = f"({ids_expr})"
|
||||
else:
|
||||
return "未找到相关的知识。"
|
||||
|
||||
|
||||
result = knVectorstore.similarity_search(
|
||||
query=key_words,
|
||||
k=3, # 可调节返回条数
|
||||
expr=expr
|
||||
query=key_words, k=3, expr=expr # 可调节返回条数
|
||||
)
|
||||
|
||||
|
||||
# 整理成字符串
|
||||
doc_texts = []
|
||||
for idx, doc in enumerate(result, start=1):
|
||||
@@ -29,14 +28,14 @@ def get_knowledge_by_key_words(key_words: str, kn_ids: List[str]) -> str:
|
||||
if text:
|
||||
# 可以加个编号，便于LLM区分
|
||||
doc_texts.append(f"[文档{idx}]: {text}")
|
||||
|
||||
|
||||
# 拼成一个大字符串，用换行隔开
|
||||
combined_text = "\n\n".join(doc_texts)
|
||||
return combined_text
|
||||
|
||||
|
||||
def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:
|
||||
print("ai_id是：" , ai_ids)
|
||||
print("ai_id是：", ai_ids)
|
||||
"""
|
||||
根据关键词和 ai_ids 列表，在知识库中检索相关内容，并返回整理后的文本字符串
|
||||
"""
|
||||
@@ -46,13 +45,11 @@ def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:
|
||||
expr = f"({ids_expr})"
|
||||
else:
|
||||
expr = "" # 不限制 kn_id todo 实际上应该不反悔任何内容
|
||||
|
||||
|
||||
result = memVectorstore.similarity_search(
|
||||
query=key_words,
|
||||
k=5, # 可调节返回条数
|
||||
expr=expr
|
||||
query=key_words, k=5, expr=expr # 可调节返回条数
|
||||
)
|
||||
|
||||
|
||||
# 整理成字符串
|
||||
doc_texts = []
|
||||
for idx, doc in enumerate(result, start=1):
|
||||
@@ -60,16 +57,16 @@ def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:
|
||||
if text:
|
||||
# 可以加个编号，便于LLM区分
|
||||
doc_texts.append(f"[记忆{idx}]: {text}")
|
||||
|
||||
|
||||
# 拼成一个大字符串，用换行隔开
|
||||
combined_text = "\n\n".join(doc_texts)
|
||||
return combined_text
|
||||
|
||||
|
||||
def get_knowledge_by_base_id(base_id: str):
|
||||
expr = f'kn_id == "{base_id}"' # base_id 会被替换
|
||||
result = knVectorstore.similarity_search(
|
||||
query="", # 如果只想用过滤条件，可以传空字符串
|
||||
k=100,
|
||||
expr=expr
|
||||
result = knVectorstore.similarity_search(
|
||||
query="", k=100, expr=expr # 如果只想用过滤条件，可以传空字符串
|
||||
)
|
||||
return [
|
||||
{
|
||||
@@ -80,6 +77,7 @@ def get_knowledge_by_base_id(base_id: str):
|
||||
for doc in result
|
||||
]
|
||||
|
||||
|
||||
def add_knowledge(text: str, is_active: bool, base_id: str, user_id: str):
|
||||
docs = [
|
||||
Document(
|
||||
@@ -89,12 +87,13 @@ def add_knowledge(text: str, is_active: bool, base_id: str, user_id: str):
|
||||
"created_by": str(user_id),
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"is_active": is_active,
|
||||
}
|
||||
},
|
||||
)
|
||||
]
|
||||
return knVectorstore.add_documents(docs)
|
||||
|
||||
def add_memory(ai_id:str,mem: str, user_id: str,is_active: bool):
|
||||
|
||||
def add_memory(ai_id: str, mem: str, user_id: str, is_active: bool):
|
||||
docs = [
|
||||
Document(
|
||||
page_content=mem,
|
||||
@@ -103,7 +102,7 @@ def add_memory(ai_id:str,mem: str, user_id: str,is_active: bool):
|
||||
"created_by": str(user_id),
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"is_active": is_active,
|
||||
}
|
||||
},
|
||||
)
|
||||
]
|
||||
return memVectorstore.add_documents(docs)
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from config.llm import llm
|
||||
from langchain.prompts import PromptTemplate
|
||||
|
||||
chatPrompt = PromptTemplate(
|
||||
input_variables=["aiRole", "history", "userInput"],
|
||||
template = """
|
||||
template="""
|
||||
你的用户画像为：{aiRole}。
|
||||
你需要基于你的角色性格，使用中文回答用户。
|
||||
|
||||
@@ -15,13 +15,12 @@ chatPrompt = PromptTemplate(
|
||||
{userInput}
|
||||
|
||||
最后，请注意，不要编造数据，不知道就说不知道，现在，请生成你的回复：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
chatChain = chatPrompt | llm
|
||||
|
||||
def get_chat_response(aiRole: str,history: str, userInput: str) -> str:
|
||||
return chatChain.invoke({
|
||||
"aiRole": aiRole,
|
||||
"history": history,
|
||||
"userInput": userInput
|
||||
})
|
||||
|
||||
def get_chat_response(aiRole: str, history: str, userInput: str) -> str:
|
||||
return chatChain.invoke(
|
||||
{"aiRole": aiRole, "history": history, "userInput": userInput}
|
||||
)
|
||||
|
||||
+34
-20
@@ -1,12 +1,12 @@
|
||||
from langchain.prompts import PromptTemplate
|
||||
from config.llm import llm,llmThink
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
import db.milvus as milvus
|
||||
import db.postgres as pg
|
||||
import json
|
||||
from config.llm import llmThink
|
||||
|
||||
memPathPrompt = PromptTemplate(
|
||||
input_variables=["ai_role", "CHAT_RECORD"],
|
||||
template = """
|
||||
template="""
|
||||
你是一个记忆筛选器，负责判断最近对话的信息中，用户的回复内容是否对业务具有长期价值或潜在价值，或者可以帮助形成用户画像。
|
||||
首先，请仔细阅读以下关于你业务的描述：
|
||||
<ai_role>
|
||||
@@ -31,12 +31,12 @@ no：用户最新回复价值有限或几乎不会在未来业务中使用。
|
||||
|
||||
回复不要带任何标点符号以及空格、换行符。
|
||||
请给出你的判断结果：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
memPathChain = memPathPrompt | llmThink
|
||||
memPrompt = PromptTemplate(
|
||||
input_variables=["CHAT_RECORD"],
|
||||
template = """
|
||||
template="""
|
||||
你的任务是对给定的聊天记录进行关键信息的记忆总结。请仔细阅读以下聊天记录，并按照要求进行总结：
|
||||
<聊天记录>
|
||||
{CHAT_RECORD}
|
||||
@@ -48,13 +48,15 @@ memPrompt = PromptTemplate(
|
||||
4. 总结内容应包含时间，并确保时间是准确的。
|
||||
5. 你需要针对你的业务场景{ai_role}，展开对用户最后回复的总结。
|
||||
请生成你的总结，以用户、时间开头：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
memChain = memPrompt | llmThink
|
||||
def take_memory(ai_id:str,sessionId: str,user_id:str, max_retry=3):
|
||||
|
||||
|
||||
def take_memory(ai_id: str, sessionId: str, user_id: str, max_retry=3):
|
||||
"""根据用户输入选择数据来源"""
|
||||
history = pg.get_history_with_time(sessionId,10)
|
||||
print("获取的历史记录:",history)
|
||||
history = pg.get_history_with_time(sessionId, 10)
|
||||
print("获取的历史记录:", history)
|
||||
ai_service = pg.get_description(ai_id)
|
||||
if ai_service == "":
|
||||
# AI描述没有描述，则取业务字段
|
||||
@@ -66,17 +68,29 @@ def take_memory(ai_id:str,sessionId: str,user_id:str, max_retry=3):
|
||||
else:
|
||||
ai_service = json["业务"]
|
||||
print("获取的描述是：", ai_service)
|
||||
choice = memPathChain.invoke({
|
||||
"ai_role": ai_service,
|
||||
"CHAT_RECORD": history,
|
||||
}).content.strip().lower()
|
||||
choice = (
|
||||
memPathChain.invoke(
|
||||
{
|
||||
"ai_role": ai_service,
|
||||
"CHAT_RECORD": history,
|
||||
}
|
||||
)
|
||||
.content.strip()
|
||||
.lower()
|
||||
)
|
||||
print("记忆判断器判断的结果是：", choice)
|
||||
if choice == "yes":
|
||||
# 对对话进行总结
|
||||
memory = memChain.invoke({
|
||||
"CHAT_RECORD": history,
|
||||
"ai_role": ai_service,
|
||||
}).content.strip().lower()
|
||||
memory = (
|
||||
memChain.invoke(
|
||||
{
|
||||
"CHAT_RECORD": history,
|
||||
"ai_role": ai_service,
|
||||
}
|
||||
)
|
||||
.content.strip()
|
||||
.lower()
|
||||
)
|
||||
print("记忆生成结果是：", memory)
|
||||
milvus.add_memory(mem = memory,user_id = user_id, is_active = True, ai_id = ai_id)
|
||||
return
|
||||
milvus.add_memory(mem=memory, user_id=user_id, is_active=True, ai_id=ai_id)
|
||||
return
|
||||
|
||||
+24
-27
@@ -1,13 +1,13 @@
|
||||
from langchain_community.agent_toolkits import create_sql_agent
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from config.llm import llm
|
||||
from langchain.prompts import PromptTemplate
|
||||
from config.ssDb import ssDBLC
|
||||
from langchain_community.agent_toolkits import create_sql_agent
|
||||
from config.ssDb import ssDBLC
|
||||
|
||||
#______________________________________________________________SQL描述_____________________________________________________________________
|
||||
# ______________________________________________________________SQL描述_____________________________________________________________________
|
||||
sqlDescriptionPrompt = PromptTemplate(
|
||||
input_variables=["sql"],
|
||||
template = """
|
||||
template="""
|
||||
你是一个SQL专家，精通SQLServer数据库。请把一下SQL查询语句用通俗易懂的中文进行总结。
|
||||
SQL语句：{sql}
|
||||
有以下要求：
|
||||
@@ -16,54 +16,51 @@ sqlDescriptionPrompt = PromptTemplate(
|
||||
3. 不能有markdown语法
|
||||
4. 要用业务语言描述，不能有专业语句例如SQL表名等
|
||||
请生成你认为合适的标题，：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
sqlDescriptionChain = sqlDescriptionPrompt | llm
|
||||
|
||||
def get_sql_description_response( sql: str) -> str:
|
||||
return sqlDescriptionChain.invoke({
|
||||
"sql": sql
|
||||
})
|
||||
|
||||
#______________________________________________________________第一次生成SQL_____________________________________________________________________
|
||||
def get_sql_description_response(sql: str) -> str:
|
||||
return sqlDescriptionChain.invoke({"sql": sql})
|
||||
|
||||
|
||||
# ______________________________________________________________第一次生成SQL_____________________________________________________________________
|
||||
sqlPrompt = PromptTemplate(
|
||||
input_variables=["userInput"],
|
||||
template = """
|
||||
template="""
|
||||
你是一个SQL专家，精通SQLServer数据库。
|
||||
请根据用户的需求，生成相应的SQL查询语句。
|
||||
只需要返回SQL语句，不要任何解释。
|
||||
用户需求：{userInput}
|
||||
请生成SQL语句：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
sqlChain = sqlPrompt | llm
|
||||
agent = create_sql_agent(
|
||||
llm=llm,
|
||||
db=ssDBLC,
|
||||
agent_type="tool-calling",
|
||||
verbose=True
|
||||
)
|
||||
agent = create_sql_agent(llm=llm, db=ssDBLC, agent_type="tool-calling", verbose=True)
|
||||
|
||||
|
||||
# def get_chat_sql_response2( userInput: str) -> str:
|
||||
# return sqlChain.invoke({
|
||||
# "userInput": userInput
|
||||
# })
|
||||
def get_chat_sql_response( userInput: str) -> str:
|
||||
def get_chat_sql_response(userInput: str) -> str:
|
||||
return agent.invoke({"input": userInput})["output"]
|
||||
|
||||
#______________________________________________________________改进SQL_____________________________________________________________________
|
||||
|
||||
# ______________________________________________________________改进SQL_____________________________________________________________________
|
||||
sqlImprovePrompt = PromptTemplate(
|
||||
input_variables=["userInput", "sql"],
|
||||
template = """
|
||||
template="""
|
||||
你是一个SQL专家，精通SQLServer数据库。
|
||||
请根据用户的需求，改进已有的SQL查询语句。
|
||||
只需要返回改进后的SQL语句，不要任何解释。
|
||||
已有SQL：{sql}
|
||||
用户需求：{userInput}
|
||||
"""
|
||||
""",
|
||||
)
|
||||
sqlImproveChain = sqlImprovePrompt | llm
|
||||
|
||||
def get_chat_sql_improve_response( userInput: str) -> str:
|
||||
return sqlImproveChain.invoke({
|
||||
"userInput": userInput
|
||||
})
|
||||
|
||||
def get_chat_sql_improve_response(userInput: str) -> str:
|
||||
return sqlImproveChain.invoke({"userInput": userInput})
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from langchain.prompts import PromptTemplate
|
||||
from config.llm import llm
|
||||
|
||||
summarizePrompt = PromptTemplate(
|
||||
input_variables=["aiRole", "history", "userStr", "infomation"],
|
||||
template = """
|
||||
template="""
|
||||
你是一个主干信息研发的 AI 助手，用户画像为：{aiRole}。
|
||||
请基于你的角色性格，保持中文简洁回答的，根据下方提示回答用户。
|
||||
|
||||
@@ -21,14 +21,17 @@ summarizePrompt = PromptTemplate(
|
||||
{infomation}
|
||||
···
|
||||
如果参考内容明显有问题，你要请用户重新描述问题，现在请生成你的回复：
|
||||
"""
|
||||
""",
|
||||
)
|
||||
summarizeChain = summarizePrompt | llm
|
||||
|
||||
|
||||
def getSummary(aiRole: str, history: str, userInput: str, infomation: str) -> str:
|
||||
return summarizeChain.invoke({
|
||||
"aiRole":aiRole,
|
||||
"history": history,
|
||||
"userStr": userInput,
|
||||
"infomation": infomation
|
||||
}).content
|
||||
return summarizeChain.invoke(
|
||||
{
|
||||
"aiRole": aiRole,
|
||||
"history": history,
|
||||
"userStr": userInput,
|
||||
"infomation": infomation,
|
||||
}
|
||||
).content
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import json
|
||||
import re
|
||||
|
||||
from langchain.schema import HumanMessage
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from config.llm import *
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import json
|
||||
import re
|
||||
|
||||
from langchain.schema import HumanMessage
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from config.llm import *
|
||||
from llm.ticketLLM import decode_barcode
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from langchain.prompts import PromptTemplate
|
||||
from config.llm import llm
|
||||
|
||||
titlePrompt = PromptTemplate(
|
||||
input_variables=["userStr"],
|
||||
template = """
|
||||
template="""
|
||||
请将用户的这句话总结成一个简短、精准的对话标题，要求：
|
||||
1. 不超过10个字（可根据需要调整长度）。
|
||||
2. 直接概括本次对话的核心内容。
|
||||
@@ -12,9 +12,10 @@ titlePrompt = PromptTemplate(
|
||||
4. 保持自然、易懂、专业或有趣（可根据场景调整风格）。
|
||||
5. 不能出现标点符号。
|
||||
用户原话："{userStr}"
|
||||
"""
|
||||
""",
|
||||
)
|
||||
titleChain = titlePrompt | llm
|
||||
|
||||
|
||||
def get_title(userInput: str):
|
||||
return titleChain.invoke({"userStr": userInput}).content
|
||||
return titleChain.invoke({"userStr": userInput}).content
|
||||
|
||||
@@ -23,6 +23,7 @@ python-multipart==0.0.20
|
||||
aio_pika==9.5.7
|
||||
ultralytics==8.3.227
|
||||
redis==7.1.0
|
||||
aiomqtt==2.4.0
|
||||
# MCP服务
|
||||
python-dotenv>=1.0.0
|
||||
websockets>=11.0.3
|
||||
|
||||
Reference in New Issue
Block a user