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

# LanceDB Vector Database

> Use LanceDB as a vector database for your Knowledge Base.

## Setup

```shell theme={null}
uv pip install lancedb
```

## Example

```python agent_with_knowledge.py theme={null}
import typer
from typing import Optional
from rich.prompt import Prompt

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb
from agno.vectordb.search import SearchType

# LanceDB Vector DB
vector_db = LanceDb(
    table_name="recipes",
    uri="/tmp/lancedb",
    search_type=SearchType.keyword,
)

# Knowledge Base
knowledge_base = Knowledge(
    vector_db=vector_db,
)

def lancedb_agent(user: str = "user"):
    agent = Agent(
        knowledge=knowledge_base,
        debug_mode=True,
    )

    while True:
        message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
        if message in ("exit", "bye"):
            break
        agent.print_response(message, session_id=f"{user}_session")

if __name__ == "__main__":
    # Comment out after first run
    knowledge_base.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )

    typer.run(lancedb_agent)
```

<Card title="Async Support ⚡">
  <div className="mt-2">
    <p>
      LanceDB also supports asynchronous operations, enabling concurrency and leading to better performance.
    </p>

    ```python async_lance_db.py theme={null}
    # install lancedb - `pip install lancedb`
    import asyncio

    from agno.agent import Agent
    from agno.knowledge.knowledge import Knowledge
    from agno.vectordb.lancedb import LanceDb

    # Initialize LanceDB
    vector_db = LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",  # You can change this path to store data elsewhere
    )

    # Create knowledge base
    knowledge_base = Knowledge(
        vector_db=vector_db,
    )
    agent = Agent(knowledge=knowledge_base, debug_mode=True)

    if __name__ == "__main__":
        # Load knowledge base asynchronously
        asyncio.run(knowledge_base.ainsert(
                url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
            )
        )

        # Create and use the agent asynchronously
        asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))
    ```

    <Tip className="mt-4">
      Use <code>aload()</code> and <code>aprint\_response()</code> methods with <code>asyncio.run()</code> for non-blocking operations in high-throughput applications.
    </Tip>
  </div>
</Card>

## LanceDb Params

<Snippet file="vectordb_lancedb_params.mdx" />