> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Nebius Embedder

The `NebiusEmbedder` is used to embed text data into vectors using the Nebius API. Nebius uses the OpenAI API specification, so the `NebiusEmbedder` class is similar to the `OpenAIEmbedder` class, incorporating adjustments to ensure compatibility with the Nebius platform.

Get your key from [here](https://studio.nebius.com/?modals=create-api-key)

## Usage

```python theme={null}
from agno.agent import AgentKnowledge
from agno.embedder.nebius import NebiusEmbedder
from agno.vectordb.pgvector import PgVector

embeddings = NebiusEmbedder().get_embedding(
    "The quick brown fox jumps over the lazy dog."
)

# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Example usage:
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="nebius_embeddings",
        embedder=NebiusEmbedder(),
    ),
    num_documents=2,
)
```

## Params

| Parameter    | Type  | Default                               | Description                                                  |
| ------------ | ----- | ------------------------------------- | ------------------------------------------------------------ |
| `id`         | `str` | `"BAAI/bge-en-icl"`                   | The name of the model used for generating embeddings.        |
| `dimensions` | `int` | `1024`                                | The dimensionality of the embeddings generated by the model. |
| `api_key`    | `str` |                                       | The API key used for authenticating requests.                |
| `base_url`   | `str` | `"https://api.studio.nebius.com/v1/"` | The base URL for the API endpoint.                           |

## Developer Resources

* View [Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/embedders/nebius_embedder.py)