Agent with Knowledge Base

Code

cookbook/11_models/cerebras/basic_knowledge.py

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

agent = Agent(model=Cerebras(id="llama-4-scout-17b-16e-instruct"), 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
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Set your API key

   export CEREBRAS_API_KEY=xxx
   
3. Install dependencies

   uv pip install -U agno sqlalchemy pgvector pypdf cerebras_cloud_sdk
   
4. Start your Postgres server

   Ensure your Postgres server is running and accessible at the connection string used in db_url.

5. Run Agent (first time)

   The first run will load and index the PDF. This may take a while.

   Mac

   python cookbook/11_models/cerebras/basic_knowledge.py
   

   python cookbook/11_models/cerebras/basic_knowledge.py
   
6. Subsequent Runs

   After the first run, comment out or remove knowledge_base.load(recreate=True) to avoid reloading the PDF each time.