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

# Nebius Embedder

The `NebiusEmbedder` can be used to embed text data into vectors using the Nebius Token Factory 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://tokenfactory.nebius.com/).

## Usage

```python nebius_embedder.py theme={null}
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.nebius import NebiusEmbedder

# Embed sentence in database
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)}")

# Use an embedder in a knowledge base
knowledge = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="nebius_embeddings",
        embedder=NebiusEmbedder(),
    ),
    max_results=2,
)
```

## Params

For a full list of parameters, see the [Nebius Embedder reference](/reference/knowledge/embedder/nebius).

## Developer Resources

* View [Cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/08_knowledge/embedders/nebius_embedder.py)