> ## 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.

# Nebius Embedder

> Generate embeddings with an active Nebius model through its OpenAI-compatible API.

Select an active embedding model from the [Nebius List Models API](https://docs.tokenfactory.nebius.com/api-reference/models/list-models) and set its output dimensions.

```python nebius_embedder.py theme={null}
import os

from agno.knowledge.embedder.nebius import NebiusEmbedder

embedder = NebiusEmbedder(
    id=os.environ["NEBIUS_EMBEDDING_MODEL"],
    dimensions=int(os.environ["NEBIUS_EMBEDDING_DIMENSIONS"]),
)
embedding = embedder.get_embedding(
    "The quick brown fox jumps over the lazy dog."
)

print(f"First values: {embedding[:5]}")
print(f"Dimensions: {len(embedding)}")
```

> [!WARNING]
>
> The SDK default, `BAAI/bge-en-icl`, is retired. Override both `id` and `dimensions` with values for an active Nebius embedding model.

## Run the Example

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

**1. Export the API key and model settings**

```bash theme={null}
export NEBIUS_API_KEY=your_nebius_api_key_here
export NEBIUS_EMBEDDING_MODEL=your_active_embedding_model
export NEBIUS_EMBEDDING_DIMENSIONS=your_model_output_dimensions
```

**2. Install dependencies**

```bash theme={null}
uv pip install -U agno openai
```

**3. Run the example**

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

## Developer Resources

* [NebiusEmbedder reference](https://agno-v2.mintlify.site/reference/knowledge/embedder/nebius.md)
* [Embedders overview](https://agno-v2.mintlify.site/knowledge/concepts/embedder/overview.md)
* [Nebius embeddings API](https://docs.tokenfactory.nebius.com/api-reference/inference/create-embeddings)
* [Nebius List Models API](https://docs.tokenfactory.nebius.com/api-reference/models/list-models)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.