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

# Sentence Transformer Embedder

> Generate local embeddings with the sentence-transformers library.

`SentenceTransformerEmbedder` defaults to `sentence-transformers/all-MiniLM-L6-v2` with 384 dimensions. Models download from Hugging Face on first use and run locally.

```python sentence_transformer_embedder.py theme={null}
from agno.knowledge.embedder.sentence_transformer import SentenceTransformerEmbedder

embedder = SentenceTransformerEmbedder()
embedding = embedder.get_embedding("The quick brown fox jumps over the lazy dog.")

print(embedding[:5])
print(len(embedding))
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno sentence-transformers
    ```
  </Step>

  <Step title="Run the example">
    ```bash theme={null}
    python sentence_transformer_embedder.py
    ```
  </Step>
</Steps>

## Developer Resources

* [SentenceTransformerEmbedder reference](/reference/knowledge/embedder/sentence-transformer)
* [sentence-transformers documentation](https://www.sbert.net/)
* [Embedders overview](/knowledge/concepts/embedder/overview)