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

# Agent with Knowledge

> Answer questions from a PDF using a Nebius agent and PgVector.

## Code

```python knowledge.py theme={null}
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](https://docs.tokenfactory.nebius.com/ai-models-inference/function-calling) for this example.

## Usage

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

  <Step title="Export environment variables">
    Select an available text-generation model ID from the [Nebius model-list API](https://docs.tokenfactory.nebius.com/api-reference/examples/list-of-models). `PgVector` uses `OpenAIEmbedder` by default, so this example also requires an OpenAI API key.

    <CodeGroup>
      ```bash macOS / Linux theme={null}
      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"
      ```

      ```powershell Windows theme={null}
      $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"
      ```
    </CodeGroup>
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U openai agno sqlalchemy psycopg pgvector pypdf
    ```
  </Step>

  <Step title="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
    ```
  </Step>

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

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