​
Code

cookbook/models/meta/llama/knowledge.py
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.meta import Llama
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)
# Add content to the knowledge
knowledge.add_content(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=Llama(id="Llama-4-Maverick-17B-128E-Instruct-FP8"), knowledge=knowledge
)
agent.print_response("How to make Thai curry?", 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

Set your LLAMA API key

export LLAMA_API_KEY=YOUR_API_KEY
3

Install libraries

pip install ddgs sqlalchemy pgvector pypdf llama-api-client
4

Run Agent

python python cookbook/models/meta/llama/knowledge.py