Azure Cosmos DB for Apache Gremlin 集成

使用 LangChain Python 与 Azure Cosmos DB for Apache Gremlin 图进行集成。

Azure Cosmos DB for Apache Gremlin 是一个图数据库服务，可用于存储数十亿顶点和边的海量图。您可以以毫秒级延迟查询图，并轻松演进图结构。

Gremlin 是由 Apache TinkerPop 的 Apache Software Foundation 开发的图遍历语言和虚拟机。

本笔记本展示了如何使用大语言模型（LLM）为可以使用 Gremlin 查询语言查询的图数据库提供自然语言接口。

设置

安装一个库：

!pip3 install gremlinpython

您需要一个 Azure CosmosDB 图数据库实例。一种选项是在 Azure 中创建一个 免费的 CosmosDB 图数据库实例。

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,
)

填充数据库

假设您的数据库为空，您可以使用 GraphDocuments 对其进行填充

对于 Gremlin，请始终为每个节点添加名为 ‘label’ 的属性。 如果未设置 label，则使用 Node.type 作为 label。 对于 Cosmos，使用自然 ID 是合理的，因为它们可以在图资源管理器中看到。

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])

刷新图架构信息

如果数据库的架构发生变化（更新后），您可以刷新架构信息。

graph.refresh_schema()

print(graph.schema)

查询图

我们现在可以使用 Gremlin QA 链来询问关于图的问题

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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