Knowledge

Search a PgVector knowledge base loaded from a PDF with a watsonx agent.

knowledge.py
"""Run `uv pip install ddgs sqlalchemy pgvector pypdf openai ibm-watsonx-ai` to install dependencies."""

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.ibm import WatsonX
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=WatsonX(id="mistralai/mistral-small-3-1-24b-instruct-2503"),
    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 ibm-watsonx-ai openai pgvector pypdf sqlalchemy

Configure your watsonx project and region

Use a project associated with a watsonx.ai Runtime service and an IBM Cloud API key authorized to access it. Copy the project ID and service URL from your watsonx environment. Check regional model availability before selecting a model.

The example URL below is Frankfurt, which is also the adapter default. Change it to match your project and service region.

export IBM_WATSONX_API_KEY="your_ibm_cloud_api_key"
export IBM_WATSONX_PROJECT_ID="your_project_id"
export IBM_WATSONX_URL="https://eu-de.ml.cloud.ibm.com"

Export the embedding API key

The default PgVector embedder makes separate OpenAI requests.

export OPENAI_API_KEY="your_openai_api_key"

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/ibm/watsonx/knowledge.py