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

# Agent with Knowledge Base

> Search a PgVector knowledge base from an Azure AI Foundry agent.

## Code

```python knowledge.py theme={null}
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

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)
```

## Usage

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

**1. Set your API keys**

```bash theme={null}
export AZURE_API_KEY=xxx
export AZURE_ENDPOINT=xxx
# For the AzureOpenAIEmbedder
export AZURE_EMBEDDER_OPENAI_API_KEY=xxx
export AZURE_EMBEDDER_OPENAI_ENDPOINT=xxx
```

**2. Install dependencies**

```bash theme={null}
uv pip install -U azure-ai-inference aiohttp openai sqlalchemy pgvector pypdf psycopg agno
```

**3. Run PgVector**

```bash theme={null}
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
```

**4. Run Agent**

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

```bash theme={null}
python knowledge.py
```


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