Knowledge

Query a PDF knowledge base stored in PgVector with Gemini embeddings.

knowledge.py
"""Run `uv pip install ddgs sqlalchemy pgvector pypdf openai google.genai` to install dependencies."""

from agno.agent import Agent
from agno.knowledge.embedder.google import GeminiEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.google import Gemini
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,
        embedder=GeminiEmbedder(),
    ),
)
# Add content to the knowledge
knowledge.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")

agent = Agent(model=Gemini(id="gemini-3.7-flash"), knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno "psycopg[binary]" beautifulsoup4 google-genai pgvector pypdf sqlalchemy

Export your Google API key

export GOOGLE_API_KEY="your_google_api_key_here"

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 the example

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

python knowledge.py

Full source: cookbook/90_models/google/gemini/knowledge.py