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Azure Cosmos DB for Apache Gremlin

Azure Cosmos DB for Apache Gremlin 是一个图数据库服务，可用于存储具有数十亿顶点和边的大规模图。您可以以毫秒级的延迟查询图，并轻松地演化图结构。

Gremlin 是一种图遍历语言和虚拟机，由Apache TinkerPop的Apache Software Foundation开发。

本笔记本展示了如何使用LLMs为图数据库提供自然语言界面，您可以使用Gremlin查询语言进行查询。

设置​

安装一个库：

!pip3 install gremlinpython

您将需要一个Azure CosmosDB图数据库实例。一个选择是在Azure中创建一个免费的CosmosDB图数据库实例。

当您创建Cosmos DB账户和图时，请使用/type作为分区键。

cosmosdb_name = "mycosmosdb"
cosmosdb_db_id = "graphtesting"
cosmosdb_db_graph_id = "mygraph"
cosmosdb_access_Key = "longstring=="
import nest_asyncio
from langchain_community.chains.graph_qa.gremlin import GremlinQAChain
from langchain_community.graphs import GremlinGraph
from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
from langchain_core.documents import Document
from langchain_openai import AzureChatOpenAI
graph = GremlinGraph(
    url=f"wss://{cosmosdb_name}.gremlin.cosmos.azure.com:443/",
    username=f"/dbs/{cosmosdb_db_id}/colls/{cosmosdb_db_graph_id}",
    password=cosmosdb_access_Key,
)

数据库种子​

假设您的数据库为空，您可以使用GraphDocuments来填充它

对于Gremlin，始终为每个节点添加名为'label'的属性。 如果未设置标签，则使用Node.type作为标签。 对于Cosmos，使用自然ID是有意义的，因为它们在图浏览器中可见。

source_doc = Document(
    page_content="Matrix is a movie where Keanu Reeves, Laurence Fishburne and Carrie-Anne Moss acted."
)
movie = Node(id="The Matrix", properties={"label": "movie", "title": "The Matrix"})
actor1 = Node(id="Keanu Reeves", properties={"label": "actor", "name": "Keanu Reeves"})
actor2 = Node(
    id="Laurence Fishburne", properties={"label": "actor", "name": "Laurence Fishburne"}
)
actor3 = Node(
    id="Carrie-Anne Moss", properties={"label": "actor", "name": "Carrie-Anne Moss"}
)
rel1 = Relationship(
    id=5, type="ActedIn", source=actor1, target=movie, properties={"label": "ActedIn"}
)
rel2 = Relationship(
    id=6, type="ActedIn", source=actor2, target=movie, properties={"label": "ActedIn"}
)
rel3 = Relationship(
    id=7, type="ActedIn", source=actor3, target=movie, properties={"label": "ActedIn"}
)
rel4 = Relationship(
    id=8,
    type="Starring",
    source=movie,
    target=actor1,
    properties={"label": "Strarring"},
)
rel5 = Relationship(
    id=9,
    type="Starring",
    source=movie,
    target=actor2,
    properties={"label": "Strarring"},
)
rel6 = Relationship(
    id=10,
    type="Straring",
    source=movie,
    target=actor3,
    properties={"label": "Strarring"},
)
graph_doc = GraphDocument(
    nodes=[movie, actor1, actor2, actor3],
    relationships=[rel1, rel2, rel3, rel4, rel5, rel6],
    source=source_doc,
)
# The underlying python-gremlin has a problem when running in notebook
# The following line is a workaround to fix the problem
nest_asyncio.apply()

# Add the document to the CosmosDB graph.
graph.add_graph_documents([graph_doc])

刷新图模式信息​

如果数据库的模式发生变化（更新后），您可以刷新模式信息。

graph.refresh_schema()
print(graph.schema)

查询图​

我们现在可以使用gremlin QA链来对图提出问题

chain = GremlinQAChain.from_llm(
    AzureChatOpenAI(
        temperature=0,
        azure_deployment="gpt-4-turbo",
    ),
    graph=graph,
    verbose=True,
)
chain.invoke("Who played in The Matrix?")
chain.run("How many people played in The Matrix?")

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