如何将用户输入映射到图数据库：提高图查询的准确性-优快云博客

如何将用户输入映射到图数据库：提高图查询的准确性

引言

图数据库因其处理复杂关系的能力而越来越受到关注。然而，在处理自然语言查询时，准确地将用户输入映射到数据库中的值可能会带来挑战。在本文中，我们将探讨如何通过补充步骤来改善图数据库查询生成，以精确映射从用户输入到数据库的值。

主要内容

设置环境

首先，我们需要安装必要的包并设置环境变量：

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

然后，定义Neo4j数据库的凭据（别忘了在本地安装Neo4j）：

import os

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

数据库连接和数据导入

接下来，创建一个连接，并将示例数据导入Neo4j数据库：

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)

实体检测和映射

使用自然语言处理工具从用户输入中检测实体：

from typing import List
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

class Entities(BaseModel):
    names: List[str] = Field(
        ...,
        description="All the person or movies appearing in the text",
    )

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are extracting person and movies from the text."),
        ("human", "Use the given format to extract information from the following input: {question}"),
    ]
)

entity_chain = prompt | llm.with_structured_output(Entities)

entities = entity_chain.invoke({"question": "Who played in Casino movie?"})

然后，将实体映射到数据库：

match_query = """MATCH (p:Person|Movie)
WHERE p.name CONTAINS $value OR p.title CONTAINS $value
RETURN coalesce(p.name, p.title) AS result, labels(p)[0] AS type
LIMIT 1
"""

def map_to_database(entities: Entities) -> Optional[str]:
    result = ""
    for entity in entities.names:
        response = graph.query(match_query, {"value": entity})
        try:
            result += f"{entity} maps to {response[0]['result']} {response[0]['type']} in database\n"
        except IndexError:
            pass
    return result

map_to_database(entities)

代码示例

下面是如何生成Cypher查询的完整示例：

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
{schema}
Entities in the question map to the following database values:
{entities_list}
Question: {question}
Cypher query:"""

cypher_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "Given an input question, convert it to a Cypher query. No pre-amble."),
        ("human", cypher_template),
    ]
)

cypher_response = (
    RunnablePassthrough.assign(names=entity_chain)
    | RunnablePassthrough.assign(
        entities_list=lambda x: map_to_database(x["names"]),
        schema=lambda _: graph.get_schema,
    )
    | cypher_prompt
    | llm.bind(stop=["\nCypherResult:"])
    | StrOutputParser()
)

cypher = cypher_response.invoke({"question": "Who played in Casino movie?"})