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【知识图谱】4、LLM大模型结合neo4j图数据库实现AI问答的功能

article 2025/2/1 20:49:18

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 data
            if "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 uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)