# embedding.EmbeddingSentenceTransformers


Creates an embedding function provider backed by sentence-transformers models.


Usage

``` python
embedding.EmbeddingSentenceTransformers(
    model="all-MiniLM-L6-v2", device=None, batch_size=64, prompts=None
)
```


Implements the [EmbeddingProvider](%60raghilda.EmbeddingProvider%60) interface.

This provider runs models locally using the `sentence-transformers` library, enabling offline/private embedding without external API calls.


## Parameters


`model: str = ``"all-MiniLM-L6-v2"`  
The sentence-transformers model to use. Default is "all-MiniLM-L6-v2". Any model from the Hugging Face Hub that is compatible with sentence-transformers can be used.

`device: str | None = None`  
The device to run the model on (e.g., "cpu", "cuda", "mps"). If None, sentence-transformers will auto-detect the best available device.

`batch_size: int = ``64`  
The number of texts to process in each batch.

`prompts: dict[EmbedInputType, str] | None = None`  
Optional mapping from [EmbedInputType](embedding.EmbedInputType.md#raghilda.embedding.EmbedInputType) to a prefix string to prepend to each text before encoding. This is useful for models that require task-specific prefixes (e.g., nomic-embed-text uses "search_query:" and "search_document:").


## Examples

Install raghilda with sentence-transformers support:

``` bash
pip install raghilda[sentence-transformers]
```


``` python
from raghilda.embedding import EmbeddingSentenceTransformers

provider = EmbeddingSentenceTransformers(model="all-MiniLM-L6-v2")
embeddings = provider.embed(["hello world", "testing embeddings"])
print(len(embeddings))
print(len(embeddings[0]))  # Dimension of the embedding
```


For models that use task-specific prefixes:


``` python
from raghilda.embedding import EmbeddingSentenceTransformers, EmbedInputType

provider = EmbeddingSentenceTransformers(
    model="nomic-ai/nomic-embed-text-v1.5",
    prompts={
        EmbedInputType.QUERY: "search_query: ",
        EmbedInputType.DOCUMENT: "search_document: ",
    },
)
# Queries get "search_query: " prepended automatically
query_emb = provider.embed(["Who is Laurens van Der Maaten?"], EmbedInputType.QUERY)
# Documents get "search_document: " prepended automatically
doc_emb = provider.embed(["TSNE is a dimensionality reduction algorithm"])
```