OpenAI Knowledge

knowledge.py

"""Run `uv pip install ddgs sqlalchemy pgvector pypdf openai` to install dependencies."""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.azure_openai import AzureOpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.azure import AzureOpenAI
from agno.vectordb.pgvector import PgVector

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

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

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        embedder=AzureOpenAIEmbedder(),
    ),
)
# Add content to the knowledge
asyncio.run(
    knowledge.ainsert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )
)

agent = Agent(
    model=AzureOpenAI(id="gpt-5.2"),
    knowledge=knowledge,
)
agent.print_response("How to make Thai curry?", markdown=True)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

Run the Example

Set up your virtual environment

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

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

Install dependencies

uv pip install -U agno "psycopg[binary]" beautifulsoup4 openai pgvector pypdf sqlalchemy

Export environment variables

export AZURE_EMBEDDER_DEPLOYMENT="your_azure_embedder_deployment_here"
export AZURE_EMBEDDER_OPENAI_API_KEY="your_azure_embedder_openai_api_key_here"
export AZURE_EMBEDDER_OPENAI_ENDPOINT="your_azure_embedder_openai_endpoint_here"
export AZURE_OPENAI_API_KEY="your_azure_openai_api_key_here"
export AZURE_OPENAI_DEPLOYMENT="your_azure_openai_deployment_here"
export AZURE_OPENAI_ENDPOINT="your_azure_openai_endpoint_here"

$Env:AZURE_EMBEDDER_DEPLOYMENT="your_azure_embedder_deployment_here"
$Env:AZURE_EMBEDDER_OPENAI_API_KEY="your_azure_embedder_openai_api_key_here"
$Env:AZURE_EMBEDDER_OPENAI_ENDPOINT="your_azure_embedder_openai_endpoint_here"
$Env:AZURE_OPENAI_API_KEY="your_azure_openai_api_key_here"
$Env:AZURE_OPENAI_DEPLOYMENT="your_azure_openai_deployment_here"
$Env:AZURE_OPENAI_ENDPOINT="your_azure_openai_endpoint_here"

Run PgVector

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

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

Configure Azure deployments

Confirm that AZURE_OPENAI_DEPLOYMENT and AZURE_EMBEDDER_DEPLOYMENT refer to deployed gpt-5.2 and text-embedding-3-small resources. Azure API calls use deployment names rather than model names. See Azure OpenAI deployment setup.

Run the example

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

python knowledge.py

Full source: cookbook/90_models/azure/openai/knowledge.py

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OpenAI Knowledge

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Run the Example