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

# Azure Cosmos DB MongoDB vCore Vector Database

> Use Azure Cosmos DB MongoDB vCore as a vector database for your Knowledge Base.

## Setup

Follow the instructions in the [Azure Cosmos DB Setup Guide](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore) to get the connection string.

Install MongoDB packages:

```shell theme={null}
uv pip install -U agno pymongo openai pypdf
```

The example uses OpenAI for embeddings and the agent model, so set your API key:

```shell theme={null}
export OPENAI_API_KEY=xxx
```

## Example

```python agent_with_knowledge.py theme={null}
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.mongodb import MongoVectorDb

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

knowledge_base = Knowledge(
    vector_db=MongoVectorDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
        cosmos_compatibility=True,
    ),
)

knowledge_base.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

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

## Azure Cosmos DB MongoDB vCore Params

<Snippet file="vectordb_mongodb_params.mdx" />