RAG Agent

Code

cookbook/11_models/ibm/watsonx/knowledge.py

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.ibm import WatsonX
from agno.vectordb.pgvector import PgVector

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

knowledge_base = Knowledge(
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)
knowledge_base.insert(
  url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=WatsonX(id="ibm/granite-20b-code-instruct"),
    knowledge=knowledge_base,
    )
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

uv venv --python 3.12
.venv\Scripts\activate

Set your API key

export IBM_WATSONX_API_KEY=xxx
export IBM_WATSONX_PROJECT_ID=xxx

Install dependencies

uv pip install -U ibm-watsonx-ai sqlalchemy pgvector psycopg pypdf openai agno

Set up PostgreSQL with pgvector

You need a PostgreSQL database with the pgvector extension installed. Adjust the db_url in the code to match your database configuration.

Run Agent

python cookbook/11_models/ibm/watsonx/knowledge.py

For subsequent runs

After the first run, comment out the knowledge_base.load(recreate=True) line to avoid reloading the PDF.

This example shows how to integrate a knowledge base with IBM WatsonX. It loads a PDF from a URL, processes it into a vector database (PostgreSQL with pgvector in this case), and then creates an agent that can query this knowledge base. Note: You need to install several packages (pgvector, pypdf, etc.) and have a PostgreSQL database with the pgvector extension available.

Agent with Storage Image Agent

⌘I

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

Code

Usage

​Code

​Usage

Code

Usage