Apache AGE

Apache AGE 是一个 PostgreSQL 扩展，提供了图数据库功能。AGE 是 A Graph Extension 的缩写，灵感来源于 Bitnine 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，能够同时处理关系型和图模型数据，以便用户可以使用标准 ANSI SQL 以及图查询语言 openCypher。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 为图数据库提供自然语言接口，您可以使用 Cypher 查询语言查询该数据库。

Cypher 是一种声明式图查询语言，允许在属性图中进行富有表现力和高效的数据查询。

设置

您需要有一个运行中的 PostgreSQL 实例，并安装了 AGE 扩展。一种测试方法是使用官方 AGE Docker 镜像运行 Docker 容器。您可以通过运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 中运行的更多说明，请参阅此处。

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

API 参考：AGEGraph | GraphCypherQAChain | ChatOpenAI

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

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

填充数据库

假设您的数据库为空，您可以使用 Cypher 查询语言填充它。以下 Cypher 语句是幂等的，这意味着无论您运行一次还是多次，数据库信息都将保持不变。

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

如果数据库架构发生更改，您可以刷新生成 Cypher 语句所需的架构信息。

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

现在我们可以使用图 Cypher QA 链来向图提出问题。

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

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

[1m> Entering new GraphCypherQAChain chain...[0m
``````output
Generated Cypher:
[32;1m[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name[0m
Full Context:
[32;1m[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}][0m

[1m> Finished chain.[0m

{'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?")

[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})
RETURN a.name[0m
Full Context:
[32;1m[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}][0m

[1m> Finished chain.[0m

{'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 参数从 Cypher QA 链返回中间步骤

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']}")

[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name[0m
Full Context:
[32;1m[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}][0m

[1m> Finished chain.[0m
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 参数从 Cypher QA 链返回直接结果

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?")

[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})
RETURN a.name[0m

[1m> Finished chain.[0m

{'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,
)

API 参考：PromptTemplate

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

[1m> Entering new GraphCypherQAChain chain...[0m
``````output
Generated Cypher:
[32;1m[1;3mMATCH (:Movie {name:"Top Gun"})<-[:ACTED_IN]-(:Actor)
RETURN count(*) AS numberOfActors[0m
Full Context:
[32;1m[1;3m[{'numberofactors': 4}][0m

[1m> Finished chain.[0m

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

为 Cypher 和答案生成使用不同的 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?")

[1m> Entering new GraphCypherQAChain chain...[0m
``````output
Generated Cypher:
[32;1m[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name[0m
Full Context:
[32;1m[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}][0m

[1m> Finished chain.[0m

{'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 语句时忽略图架构的部分内容。

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?")

[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
WHERE m.name = 'Top Gun'
RETURN a.name[0m
Full Context:
[32;1m[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}][0m

[1m> Finished chain.[0m

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

设置​

填充数据库​

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​

为 Cypher 和答案生成使用不同的 LLM​

忽略指定的节点和关系类型​

验证生成的 Cypher 语句​

设置