Knowledge

Give Claude a PgVector knowledge base built from a recipe PDF with Azure OpenAI embeddings.

Anthropic retired the source's dated Sonnet 4 and Opus 4 models on June 15, 2026. Before running your saved copy, replace claude-sonnet-4-20250514 or claude-opus-4-20250514 with the active claude-sonnet-4-6 model. See model lifecycle status.

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

from agno.agent import Agent
from agno.knowledge.embedder.azure_openai import AzureOpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.anthropic import Claude
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
knowledge.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    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

Install dependencies

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

Export environment variables

export ANTHROPIC_API_KEY="your_anthropic_api_key_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"

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

Update the source model

In your saved copy, replace claude-sonnet-4-20250514 or claude-opus-4-20250514 with claude-sonnet-4-6. Keep the other model settings.

Select the Azure embedding deployment

Your Azure resource must already have a text-embedding-3-small deployment. If its deployment name is not text-embedding-3-small, export its actual name before starting Python:

export AZURE_EMBEDDER_DEPLOYMENT="your_embedding_deployment_name"

The endpoint and key must belong to that resource. These variables select an existing deployment; they do not create one.

Run the example

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

python knowledge.py

Full source: cookbook/90_models/anthropic/knowledge.py