【知识图谱】4、LLM大模型结合neo4j图数据库实现AI问答的功能

昨天写了一篇文章，使用fastapi直接操作neo4j图数据库插入数据的例子，本文实现LLM大模型结合neo4j图数据库实现AI问答功能。

废话不多说，先上代码

import gradio as gr
from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel
from langchain_openai import ChatOpenAI
from langchain.prompts import (ChatPromptTemplate,MessagesPlaceholder,SystemMessagePromptTemplate,HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.graphs import Neo4jGraph
from langchain.chains import GraphCypherQAChain
import asyncio
from typing import List
import json# Initialize FastAPI
app = FastAPI()# Initialize Neo4j with timeout
try:graph = Neo4jGraph(url="bolt://localhost:7687",username="neo4j",password="password",database="neo4j",timeout=60  # 60 seconds timeout)
except Exception as e:print(f"Failed to connect to Neo4j: {e}")graph = None# Fallback in-memory storage
job_seekers = []
job_positions = []# Initialize LangChain components
llm = ChatOpenAI(temperature=0.95,model="glm-4-flash",openai_api_key="xxxxxx",openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)prompt = ChatPromptTemplate(messages=[SystemMessagePromptTemplate.from_template("You are a helpful AI assistant for a recruitment company. You can answer questions about job seekers and available positions."),MessagesPlaceholder(variable_name="chat_history"),HumanMessagePromptTemplate.from_template("{question}")]
)memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation = LLMChain(llm=llm,prompt=prompt,verbose=True,memory=memory
)# Initialize GraphCypherQAChain if Neo4j is available
if graph:graph_qa = GraphCypherQAChain.from_llm(llm,graph=graph,verbose=True)# Define chat function with timeout
async def chat_with_timeout(message, history):try:if graph:neo4j_response = await asyncio.wait_for(asyncio.to_thread(graph_qa.run, message),timeout=10.0  # 10 seconds timeout)return f"Based on our database: {neo4j_response}"else:# Fallback to in-memory dataif "job seekers" in message.lower():return f"Based on our records: We have {len(job_seekers)} job seekers."elif "job positions" in message.lower():return f"Based on our records: We have {len(job_positions)} job positions."else:response = conversation.invoke({"question": message})return response['text']except asyncio.TimeoutError:return "I'm sorry, but the database query took too long. Please try a simpler question or try again later."except Exception as e:print(f"Error in chat function: {e}")response = conversation.invoke({"question": message})return response['text']# # Create Gradio interface
iface = gr.ChatInterface(chat_with_timeout)
#
# # Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, iface, path="/")# Run the app
if __name__ == "__main__":import uvicornuvicorn.run(app, host="0.0.0.0", port=8000)