Agent with Storage

Code

cookbook/models/ibm/watsonx/db.py

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.ibm import WatsonX
from agno.tools.duckduckgo import DuckDuckGoTools

# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)

agent = Agent(
    model=WatsonX(id="mistralai/mistral-small-3-1-24b-instruct-2503"),
    db=db,
    tools=[DuckDuckGoTools()],
    add_history_to_context=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Set your API key

   export IBM_WATSONX_API_KEY=xxx
   export IBM_WATSONX_PROJECT_ID=xxx
   
3. Install libraries

   pip install -U ddgs psycopg sqlalchemy ibm-watsonx-ai agno
   
4. Set up PostgreSQL

   Make sure you have a PostgreSQL database running. You can adjust the db_url in the code to match your database configuration.

5. Run Agent

   Mac

   python cookbook/models/ibm/watsonx/db.py
   

This example shows how to use PostgreSQL storage with IBM WatsonX to maintain conversation state across multiple interactions. It creates an agent with a PostgreSQL storage backend and sends multiple messages, with the conversation history being preserved between them.

Note: You need to install the sqlalchemy package and have a PostgreSQL database available.