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

# Sentence Transformers

Embed text using any HuggingFace Sentence Transformer model locally — no API key needed.
See the [API reference](https://lancedb.github.io/geneva/api/embeddings/#geneva.udfs.text.embeddings.sentence_transformer_udf) for all parameters.

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
pip install 'geneva[udf-text-sentence-transformers]'
```

<Tip>
  Sentence Transformer models run **locally** on your workers — there are no API calls and no
  per-token costs. This makes them a good fit for large-scale embedding jobs where cost is a
  concern.
</Tip>

## Embeddings

**Compare a lightweight and a high-quality model side by side:**

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from geneva.udfs import sentence_transformer_udf

table.add_columns({
    # Lightweight default model — fast, CPU-friendly
    "embedding_mini": sentence_transformer_udf(
        column="body",
        model="sentence-transformers/all-MiniLM-L6-v2",
    ),
    # Larger model with GPU acceleration
    "embedding_bge": sentence_transformer_udf(
        column="body",
        model="BAAI/bge-large-en-v1.5",
        num_gpus=1.0,
    ),
})
```

## GPU acceleration

Sentence Transformer models can run on CPU or GPU. Smaller models like `all-MiniLM-L6-v2`
work well on CPU, but larger models like `bge-large-en-v1.5` benefit significantly from GPU
acceleration. Use the `num_gpus` parameter to request GPU resources for a worker:

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
# CPU-only (default) — suitable for lightweight models
sentence_transformer_udf(column="body", model="sentence-transformers/all-MiniLM-L6-v2")

# GPU-accelerated — recommended for larger models
sentence_transformer_udf(column="body", model="BAAI/bge-large-en-v1.5", num_gpus=1.0)
```

Setting `num_gpus` to a fractional value (e.g., `0.5`) tells the
[Ray scheduler](https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html)
to co-locate multiple workers on the same physical GPU. For example, two UDFs with
`num_gpus=0.5` will be scheduled on a single GPU. Note that Ray does not enforce GPU memory
limits — it is your responsibility to ensure the combined models fit in GPU memory.

## API Reference

* [Embeddings](https://lancedb.github.io/geneva/api/embeddings/) — `sentence_transformer_udf()` — all parameters including `column`, `model`, `num_gpus`, `normalize`, and `batch_size`