lance_db.py
delete_by_name() and delete_by_metadata() load matching rows and delete their IDs from the LanceDB table.
Run the Example
1
Set up your virtual environment
2
Install dependencies
3
Export the API key
4
Run the example
Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Insert a PDF, search it with an agent, and delete LanceDB rows by name or metadata.
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb
vector_db = LanceDb(table_name="recipes", uri="tmp/lancedb")
knowledge = Knowledge(vector_db=vector_db)
agent = Agent(knowledge=knowledge, search_knowledge=True)
if __name__ == "__main__":
knowledge.insert(
name="Recipes",
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
metadata={"doc_type": "recipe_book"},
)
agent.print_response(
"List the ingredients for Massaman Gai.",
markdown=True,
)
vector_db.delete_by_name("Recipes")
# To delete by metadata instead:
# vector_db.delete_by_metadata({"doc_type": "recipe_book"})
delete_by_name() and delete_by_metadata() load matching rows and delete their IDs from the LanceDB table.
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 lance_db.py
| Task | Guide |
|---|---|
| Call async Agno methods | Async LanceDB usage |
| Configure hybrid search | LanceDB hybrid search |
| Configure search behavior | LanceDB overview |
Was this page helpful?