Agent with Knowledge

Answer questions from a PDF using Groq, OpenAI embeddings, and PgVector.

Add a PDF to a Knowledge base and let the agent retrieve relevant chunks to answer questions.

Code

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.groq import Groq
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.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")

agent = Agent(
    model=Groq(id="openai/gpt-oss-120b"),
    knowledge=knowledge,
)
agent.print_response("How to make Thai curry?", markdown=True)

Usage

Set up your virtual environment

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

Set your API key

export GROQ_API_KEY=xxx
export OPENAI_API_KEY=xxx

Install dependencies

uv pip install -U sqlalchemy psycopg pgvector pypdf openai groq agno

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

Run Agent

Save the code above as knowledge.py, then run:

python knowledge.py

Developer Resources