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

# Azure Cosmos DB MongoDB vCore 集成

## 代码

```python cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/cosmos_mongodb_vcore.py theme={null}
import urllib.parse
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.mongodb import MongoDb

# Azure Cosmos DB MongoDB connection string
"""
Example connection strings:
"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"
"""
mdb_connection_string = f"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=MongoDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
        cosmos_compatibility=True,
    ),
)

# Comment out after first run
knowledge_base.load(recreate=True)

# Create and use the agent
agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Thai curry?", markdown=True)
```

## 用法

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="安装库">
    ```bash theme={null}
    pip install -U pymongo pypdf openai agno
    ```
  </Step>

  <Step title="运行 Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/cosmos_mongodb_vcore.py
      ```

      ```bash Windows theme={null}
      python cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/cosmos_mongodb_vcore.py
      ```
    </CodeGroup>
  </Step>
</Steps>