​
Code

cookbook/agent_concepts/vector_dbs/lance_db.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb

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

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

knowledge_base.load(recreate=False)  # Comment out after first run

agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Tom Kha Gai", markdown=True)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.

python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U lancedb pypdf openai agno
3

Run Agent

python cookbook/agent_concepts/vector_dbs/lance_db.py

​
Code

cookbook/agent_concepts/vector_dbs/lance_db.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb

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

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

knowledge_base.load(recreate=False)  # Comment out after first run

agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Tom Kha Gai", markdown=True)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.

python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U lancedb pypdf openai agno
3

Run Agent

python cookbook/agent_concepts/vector_dbs/lance_db.py