Apache AGE

Apache AGE 是一个提供图数据库功能的 PostgreSQL 扩展。AGE 是 A Graph Extension 的缩写，灵感来源于 Bitnine 公司对 PostgreSQL 10 的分支 AgensGraph，它是一个多模型数据库。该项目的目标是创建一个单一存储，既能处理关系型数据又能处理图模型数据，以便用户可以使用标准 ANSI SQL 和开放 Cypher（图查询语言）。Apache AGE 存储的数据元素是节点、连接它们的边以及节点和边的属性。

本笔记本展示了如何使用 LLM 为您可以使用 Cypher 查询语言查询的图数据库提供自然语言界面。

Cypher 是一种声明式图查询语言，可以在属性图中进行富有表达力且高效的数据查询。

设置

您需要运行一个安装了 AGE 扩展的 Postgre 实例。一种测试选项是使用官方 AGE docker 镜像运行 docker 容器。您可以通过执行以下脚本来运行本地 docker 容器。

docker run \
    --name age  \
    -p 5432:5432 \
    -e POSTGRES_USER=postgresUser \
    -e POSTGRES_PASSWORD=postgresPW \
    -e POSTGRES_DB=postgresDB \
    -d \
    apache/age

有关在 docker 中运行的其他说明可以在此处找到。

from langchain_community.graphs.age_graph import AGEGraph
from langchain_neo4j import GraphCypherQAChain
from langchain_openai import ChatOpenAI

conf = {
    "database": "postgresDB",
    "user": "postgresUser",
    "password": "postgresPW",
    "host": "localhost",
    "port": 5432,
}

graph = AGEGraph(graph_name="age_test", conf=conf)

数据库初始化

假设您的数据库是空的，您可以使用 Cypher 查询语言填充它。以下 Cypher 语句是幂等的，这意味着如果您运行一次或多次，数据库信息将保持不变。

graph.query(
    """
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
MERGE (a:Actor {name:actor})
MERGE (a)-[:ACTED_IN]->(m)
"""
)

[]

刷新图模式信息

如果数据库的模式发生变化，您可以刷新生成 Cypher 语句所需的模式信息。

graph.refresh_schema()

print(graph.schema)

        Node properties are the following:
        [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}, {'properties': [{'property': 'property_a', 'type': 'STRING'}], 'labels': 'LabelA'}, {'properties': [], 'labels': 'LabelB'}, {'properties': [], 'labels': 'LabelC'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}]
        Relationship properties are the following:
        [{'properties': [], 'type': 'ACTED_IN'}, {'properties': [{'property': 'rel_prop', 'type': 'STRING'}], 'type': 'REL_TYPE'}]
        The relationships are the following:
        ['(:`Actor`)-[:`ACTED_IN`]->(:`Movie`)', '(:`LabelA`)-[:`REL_TYPE`]->(:`LabelB`)', '(:`LabelA`)-[:`REL_TYPE`]->(:`LabelC`)']

查询图

我们现在可以使用图 Cypher QA 链来询问图问题。

chain = GraphCypherQAChain.from_llm(
    ChatOpenAI(temperature=0), graph=graph, verbose=True, allow_dangerous_requests=True
)

chain.invoke("Who played in Top Gun?")

> Entering new GraphCypherQAChain chain...

Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.'}

限制结果数量

您可以使用 top_k 参数限制 Cypher QA 链的结果数量。默认值为 10。

chain = GraphCypherQAChain.from_llm(
        ChatOpenAI(temperature=0),
        graph=graph,
        verbose=True,
        top_k=2,
        allow_dangerous_requests=True,
)

chain.invoke("Who played in Top Gun?")

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})
RETURN a.name
Full Context:
[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer played in Top Gun.'}

返回中间结果

您可以使用 return_intermediate_steps 参数返回 Cypher QA 链的中间步骤。

chain = GraphCypherQAChain.from_llm(
        ChatOpenAI(temperature=0),
        graph=graph,
        verbose=True,
        return_intermediate_steps=True,
        allow_dangerous_requests=True,
)

result = chain("Who played in Top Gun?")
print(f"Intermediate steps: {result['intermediate_steps']}")
print(f"Final answer: {result['result']}")

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]

> Finished chain.
Intermediate steps: [{'query': "MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\nWHERE m.name = 'Top Gun'\nRETURN a.name"}, {'context': [{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]}]
Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.

返回直接结果

您可以使用 return_direct 参数返回 Cypher QA 链的直接结果。

chain = GraphCypherQAChain.from_llm(
        ChatOpenAI(temperature=0),
        graph=graph,
        verbose=True,
        return_direct=True,
        allow_dangerous_requests=True,
)

chain.invoke("Who played in Top Gun?")

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})
RETURN a.name

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': [{'name': 'Tom Cruise'},
  {'name': 'Val Kilmer'},
  {'name': 'Anthony Edwards'},
  {'name': 'Meg Ryan'}]}

在 Cypher 生成提示中添加示例

您可以定义希望 LLM 为特定问题生成的 Cypher 语句。

from langchain_core.prompts.prompt import PromptTemplate

CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.
Examples: Here are a few examples of generated Cypher statements for particular questions:
# How many people played in Top Gun?
MATCH (m:Movie {{title:"Top Gun"}})<-[:ACTED_IN]-()
RETURN count(*) AS numberOfActors

The question is:
{question}"""

CYPHER_GENERATION_PROMPT = PromptTemplate(
        input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
)

chain = GraphCypherQAChain.from_llm(
        ChatOpenAI(temperature=0),
        graph=graph,
        verbose=True,
        cypher_prompt=CYPHER_GENERATION_PROMPT,
        allow_dangerous_requests=True,
)

chain.invoke("How many people played in Top Gun?")

> Entering new GraphCypherQAChain chain...

Generated Cypher:
MATCH (:Movie {name:"Top Gun"})<-[:ACTED_IN]-(:Actor)
RETURN count(*) AS numberOfActors
Full Context:
[{'numberofactors': 4}]

> Finished chain.

{'query': 'How many people played in Top Gun?',
 'result': "I don't know the answer."}

对 Cypher 和答案生成使用单独的 LLM

您可以使用 cypher_llm 和 qa_llm 参数定义不同的 LLM。

chain = GraphCypherQAChain.from_llm(
    graph=graph,
    cypher_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
    qa_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k"),
    verbose=True,
    allow_dangerous_requests=True,
)

chain.invoke("Who played in Top Gun?")

> Entering new GraphCypherQAChain chain...

Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}

忽略指定的节点和关系类型

您可以使用 include_types 或 exclude_types 在生成 Cypher 语句时忽略图模式的某些部分。

chain = GraphCypherQAChain.from_llm(
        graph=graph,
        cypher_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
        qa_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k"),
        verbose=True,
        exclude_types=["Movie"],
        allow_dangerous_requests=True,
)

# Inspect graph schema
print(chain.graph_schema)

Node properties are the following:
Actor {name: STRING},LabelA {property_a: STRING},LabelB {},LabelC {}
Relationship properties are the following:
ACTED_IN {},REL_TYPE {rel_prop: STRING}
The relationships are the following:
(:LabelA)-[:REL_TYPE]->(:LabelB),(:LabelA)-[:REL_TYPE]->(:LabelC)

验证生成的 Cypher 语句

您可以使用 validate_cypher 参数验证并更正生成的 Cypher 语句中的关系方向。

chain = GraphCypherQAChain.from_llm(
        llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
        graph=graph,
        verbose=True,
        validate_cypher=True,
        allow_dangerous_requests=True,
)

chain.invoke("Who played in Top Gun?")

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name
Full Context:
[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]

> Finished chain.

{'query': 'Who played in Top Gun?',
 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.'}

在 GitHub 上编辑此页面源文件。

以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。

热门提供商

按组件划分的集成

设置

数据库初始化

刷新图模式信息

查询图

限制结果数量

返回中间结果

返回直接结果

在 Cypher 生成提示中添加示例

对 Cypher 和答案生成使用单独的 LLM

忽略指定的节点和关系类型

验证生成的 Cypher 语句

热门提供商

按组件划分的集成

​设置

​数据库初始化

​刷新图模式信息

​查询图

​限制结果数量

​返回中间结果

​返回直接结果

​在 Cypher 生成提示中添加示例

​对 Cypher 和答案生成使用单独的 LLM

​忽略指定的节点和关系类型

​验证生成的 Cypher 语句

设置

数据库初始化

刷新图模式信息

查询图

限制结果数量

返回中间结果

返回直接结果

在 Cypher 生成提示中添加示例

对 Cypher 和答案生成使用单独的 LLM

忽略指定的节点和关系类型

验证生成的 Cypher 语句