> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure Cosmos DB for Apache Gremlin 集成

> 使用 LangChain Python 集成 Azure Cosmos DB for Apache Gremlin。

> [Azure Cosmos DB for Apache Gremlin](https://learn.microsoft.com/en-us/azure/cosmos-db/gremlin/introduction) is a graph database service that can be used to store massive graphs with billions of vertices and edges. You can query the graphs with millisecond latency and evolve the graph structure easily.
>
> [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(query_language\)) 是由 `Apache TinkerPop` of the `Apache Software Foundation`.

本 notebook 展示如何使用 LLMs to provide a 自然语言 interface to a graph database you can query with the `Gremlin` query language.

## 设置

Install a library:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
!pip3 install gremlinpython
```

You will need an Azure CosmosDB Graph database instance. One option is to create a [free CosmosDB Graph database instance in Azure](https://learn.microsoft.com/en-us/azure/cosmos-db/free-tier).

When you create your Cosmos DB account and Graph, use `/type` as a partition key.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
cosmosdb_name = "mycosmosdb"
cosmosdb_db_id = "graphtesting"
cosmosdb_db_graph_id = "mygraph"
cosmosdb_access_Key = "longstring=="
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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

Assuming your database is empty, you can populate it using the GraphDocuments

For Gremlin, always add property called 'label' for each Node.
If no label is set, Node.type is used as a label.
For cosmos using natural id's make sense, as they are visible in the graph explorer.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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,
)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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 Schema 信息

If the schema of database changes (after updates), you can refresh the schema information.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
graph.refresh_schema()
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
print(graph.schema)
```

## 查询图

我们现在可以使用 gremlin QA chain 来查询图

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
chain = GremlinQAChain.from_llm(
    AzureChatOpenAI(
        temperature=0,
        azure_deployment="gpt-4-turbo",
    ),
    graph=graph,
    verbose=True,
)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
chain.invoke("Who played in The Matrix?")
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
chain.run("How many people played in The Matrix?")
```

***

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