> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-docs-scavio-google-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Foundry Knowledge

> Query a PgVector knowledge base from an Azure AI Foundry model with Azure OpenAI embeddings.

```python knowledge.py theme={null}
"""Run `uv pip install ddgs sqlalchemy pgvector pypdf 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.azure import AzureAIFoundry
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=AzureAIFoundry(id="Cohere-command-r-08-2024"),
    knowledge=knowledge,
)
agent.print_response("How to make Thai curry?", markdown=True)

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

if __name__ == "__main__":
    pass
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" aiohttp azure-ai-inference beautifulsoup4 openai pgvector pypdf sqlalchemy
    ```
  </Step>

  <Step title="Export environment variables">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export AZURE_API_KEY="your_azure_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"
      export AZURE_ENDPOINT="your_azure_endpoint_here"
      ```

      ```bash Windows theme={null}
      $Env:AZURE_API_KEY="your_azure_api_key_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_ENDPOINT="your_azure_endpoint_here"
      ```
    </CodeGroup>
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run the example">
    Save the code above as `knowledge.py`, then run:

    ```bash theme={null}
    python knowledge.py
    ```
  </Step>
</Steps>

Full source: [cookbook/90\_models/azure/ai\_foundry/knowledge.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/azure/ai_foundry/knowledge.py)