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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

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Code

cookbook/models/cerebras/basic_knowledge.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.models.cerebras import Cerebras
from agno.vectordb.pgvector import PgVector

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

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)
knowledge_base.load(recreate=True)  # Comment out after first run

agent = Agent(
    model=Cerebras(id="llama-4-scout-17b-16e-instruct"),
    knowledge=knowledge_base,
)
agent.print_response("How to make Thai curry?", markdown=True)

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Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set your API key

export CEREBRAS_API_KEY=xxx
3

Install libraries

pip install -U agno 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.
python cookbook/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.