Apache AGE

Apache AGE 是一个 PostgreSQL 扩展，提供图数据库功能。AGE 是 A Graph Extension 的缩写，灵感来自 Bitnine 的 PostgreSQL 10 分支 AgensGraph，这是一个多模型数据库。该项目的目标是创建一个可以同时处理关系模型和图模型数据的单一存储，以便用户可以使用标准的 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LLMs为图数据库提供一个自然语言界面，您可以使用Cypher查询语言进行查询。

Cypher 是一种声明式图查询语言，允许在属性图中进行表达性强且高效的数据查询。

设置

你需要有一个正在运行的Postgre实例，并且安装了AGE扩展。一个测试的选择是使用官方的AGE docker镜像来运行一个docker容器。你可以通过执行以下脚本来运行一个本地的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这里找到。

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

API Reference:AGEGraph | GraphCypherQAChain | ChatOpenAI

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

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

数据库种子

假设您的数据库为空，您可以使用Cypher查询语言来填充它。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链来询问图的问题

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

你可以使用top_k参数来限制Cypher QA Chain返回的结果数量。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

你可以使用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

你可以使用return_direct参数直接从Cypher QA链返回结果

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

你可以定义Cypher语句，让LLM为特定问题生成

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 Reference: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."}

使用单独的LLMs进行Cypher和答案生成

你可以使用cypher_llm和qa_llm参数来定义不同的llms

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语句时忽略图模式的部分内容。

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

你可以使用validate_cypher参数来验证和纠正生成的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​

数据库种子​

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​

使用单独的LLMs进行Cypher和答案生成​

忽略指定的节点和关系类型​

验证生成的Cypher语句​

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设置