Agent with Knowledge

Code

knowledge.py

import os

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.nebius import Nebius
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=Nebius(id=os.environ["NEBIUS_MODEL_ID"]), knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)

Select a text-generation model with function-calling support for this example.

Usage

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

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

Export environment variables

Select an available text-generation model ID from the Nebius model-list API. PgVector uses OpenAIEmbedder by default, so this example also requires an OpenAI API key.

export NEBIUS_API_KEY="your_nebius_api_key"
export NEBIUS_MODEL_ID="your_current_text_model_id"
export OPENAI_API_KEY="your_openai_api_key"

$Env:NEBIUS_API_KEY="your_nebius_api_key"
$Env:NEBIUS_MODEL_ID="your_current_text_model_id"
$Env:OPENAI_API_KEY="your_openai_api_key"

Install dependencies

uv pip install -U openai agno sqlalchemy psycopg pgvector pypdf

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run Agent

Save the code above as knowledge.py, then run:

python knowledge.py

Get Started

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Agent with Knowledge

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Usage