> ## 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

> 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

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

**1. 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.

```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"
```

**2. Install dependencies**

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

**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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