async_knowledge.py
"""Run `uv pip install ddgs sqlalchemy pgvector pypdf llama-api-client` to install dependencies."""
import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.meta import Llama
from agno.vectordb.pgvector import PgVector
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
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.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")
agent = Agent(
model=Llama(id="Llama-4-Maverick-17B-128E-Instruct-FP8"), knowledge=knowledge
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Create and use the agent
asyncio.run(agent.aprint_response("How to make Thai curry?", markdown=True))
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno llama-api-client openai pgvector psycopg-binary pypdf sqlalchemy
3
Export your API keys
export LLAMA_API_KEY="your_llama_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:LLAMA_API_KEY="your_llama_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
async_knowledge.py, then run:python async_knowledge.py