> ## Documentation Index
> Fetch the complete documentation index at: https://docs.phidata.com/llms.txt
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# SentenceTransformers Embedder

The `SentenceTransformerEmbedder` class is used to embed text data into vectors using the [SentenceTransformers](https://www.sbert.net/) library.

## Usage

```python cookbook/embedders/sentence_transformer_embedder.py theme={null}
from phi.agent import AgentKnowledge
from phi.vectordb.pgvector import PgVector
from phi.embedder.sentence_transformer import SentenceTransformerEmbedder

embeddings = SentenceTransformerEmbedder().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="sentence_transformer_embeddings",
        embedder=SentenceTransformerEmbedder(),
    ),
    num_documents=2,
)
```

## Params

| Parameter                     | Type               | Default             | Description                                                  |
| ----------------------------- | ------------------ | ------------------- | ------------------------------------------------------------ |
| `dimensions`                  | `int`              | -                   | The dimensionality of the generated embeddings               |
| `model`                       | `str`              | `all-mpnet-base-v2` | The name of the SentenceTransformers model to use            |
| `sentence_transformer_client` | `Optional[Client]` | -                   | Optional pre-configured SentenceTransformers client instance |