Agent with Knowledge

Code

cookbook/11_models/google/gemini/knowledge.py

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

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-2.0-flash-001"), knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)

Usage

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   
2. Set your API key

   export GOOGLE_API_KEY=xxx
   
3. Install dependencies

   uv pip install -U google-genai sqlalchemy pgvector pypdf openai 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/11_models/google/gemini/knowledge.py