> ## 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.

# LanceDB Database

> Demonstrates LanceDB-backed knowledge with sync and async-batching flows.

```python theme={null}
"""
LanceDB Database
================

Demonstrates LanceDB-backed knowledge with sync and async-batching flows.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.lancedb import LanceDb


# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
def create_sync_knowledge() -> tuple[Knowledge, LanceDb]:
    vector_db = LanceDb(table_name="vectors", uri="tmp/lancedb")
    knowledge = Knowledge(
        name="Basic SDK Knowledge Base",
        description="Agno 2.0 Knowledge Implementation with LanceDB",
        vector_db=vector_db,
    )
    return knowledge, vector_db


def create_async_batch_knowledge() -> Knowledge:
    return Knowledge(
        vector_db=LanceDb(
            uri="/tmp/lancedb",
            table_name="recipe_documents",
            embedder=OpenAIEmbedder(enable_batch=True),
        )
    )


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
def create_sync_agent(knowledge: Knowledge) -> Agent:
    return Agent(knowledge=knowledge)


def create_async_batch_agent(knowledge: Knowledge) -> Agent:
    return Agent(
        model=OpenAIChat(id="gpt-5.2"),
        knowledge=knowledge,
        search_knowledge=True,
        read_chat_history=True,
    )


# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
def run_sync() -> None:
    knowledge, vector_db = create_sync_knowledge()
    knowledge.insert(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"doc_type": "recipe_book"},
    )

    agent = create_sync_agent(knowledge)
    agent.print_response(
        "List down the ingredients to make Massaman Gai", markdown=True
    )

    vector_db.delete_by_name("Recipes")
    vector_db.delete_by_metadata({"doc_type": "recipe_book"})


async def run_async_batch() -> None:
    knowledge = create_async_batch_knowledge()
    agent = create_async_batch_agent(knowledge)

    await knowledge.ainsert(path="cookbook/07_knowledge/testing_resources/cv_1.pdf")
    await agent.aprint_response(
        "What can you tell me about the candidate and what are his skills?",
        markdown=True,
    )


if __name__ == "__main__":
    run_sync()
    asyncio.run(run_async_batch())
```

## Run the Example

```bash theme={null}
# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/vector_db/lance_db

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python lance_db.py
```