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Code

cookbook/models/ollama/knowledge.py
from agno.agent import Agent
from agno.embedder.ollama import OllamaEmbedder
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.models.ollama import Ollama
from agno.vectordb.pgvector import PgVector

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

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        embedder=OllamaEmbedder(id="llama3.2", dimensions=3072),
    ),
)
knowledge_base.load(recreate=True)  # Comment out after first run

agent = Agent(
    model=Ollama(id="llama3.2"),
    knowledge=knowledge_base,
    show_tool_calls=True,
)
agent.print_response("How to make Thai curry?", markdown=True)

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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 Ollama

Follow the installation guide and run:
ollama pull llama3.2
3

Install libraries

pip install -U ollama sqlalchemy pgvector pypdf agno
4

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:16
5

Run Agent

python cookbook/models/ollama/knowledge.py