LanceDB

LanceDb supports vector, full-text, and hybrid search. A filesystem path creates a local database. A db:// URI connects to LanceDB Cloud and requires api_key= or LANCEDB_API_KEY.

In Agno v2.7.2, dictionary filters are applied after LanceDB returns the limited candidates, and filter-expression lists are ignored. Filtered searches can return fewer documents than requested. Search results also omit the stored LanceDB row ID.

agent_with_knowledge.py

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

knowledge = Knowledge(
    vector_db=LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",
    )
)
agent = Agent(knowledge=knowledge, search_knowledge=True)

if __name__ == "__main__":
    knowledge.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )
    agent.print_response("How do I make pad thai?", markdown=True)

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

uv venv --python 3.12
.venv\Scripts\activate

Install dependencies

uv pip install -U agno lancedb openai pypdf

Export the API key

export OPENAI_API_KEY=your_openai_api_key_here

Run the example

python agent_with_knowledge.py

Search Modes

`search_type`	Behavior
`SearchType.vector`	Searches by embedding similarity
`SearchType.keyword`	Uses LanceDB native full-text search
`SearchType.hybrid`	Runs LanceDB hybrid vector and full-text search

Parameters

Parameter	Type	Default	Description
`uri`	`str`	`/tmp/lancedb`	The URI to connect to.
`table`	`LanceTable`	-	The Lance table to use.
`table_name`	`str`	-	The name of the table to use.
`connection`	`DBConnection`	-	The database connection to use.
`api_key`	`str`	-	The API key to use.
`embedder`	`Embedder`	-	The embedder to use.
`search_type`	`SearchType`	vector	The search type to use.
`distance`	`Distance`	cosine	The distance to use.
`nprobes`	`int`	-	The number of probes to use. More Info
`reranker`	`Reranker`	-	The reranker to use. More Info
`use_tantivy`	`bool`	`False`	Deprecated. LanceDB now uses native FTS; this parameter is ignored.
`on_bad_vectors`	`str`	-	What to do if the vector is bad. One of `"error"`, `"drop"`, `"fill"`, `"null"`.
`fill_value`	`float`	-	The value to fill the vector with if `on_bad_vectors` is `"fill"`.

Next Steps

Task	Guide
Insert, search, and delete content	LanceDB usage
Call async Agno methods	Async LanceDB usage
Configure hybrid search	LanceDB hybrid search

Developer Resources

LanceDB Python SDK

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Advanced

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Additional Resources

Run the Example

Search Modes

Parameters

Next Steps

Developer Resources

​Run the Example

​Search Modes

​Parameters

​Next Steps

​Developer Resources

Run the Example

Search Modes

Parameters

Next Steps

Developer Resources