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

# Embeddings Refinery

> Embed Chunked Texts

The `EmbeddingsRefinery` allows you to add more more information
to your chunks by adding embeddings to them. This is useful for
downstream tasks like semantic search, clustering, or vector database insertions.

## API Reference

To use the `EmbeddingsRefinery` via the API, check out the [API reference documentation](../../api/refineries/embeddings).

## Initialization

To use the `EmbeddingsRefinery`, you need to initialize it with an embedding model.

```python theme={"system"}
from chonkie import EmbeddingsRefinery

# Initialize with string model identifier
# or an embedding model instance
em_refinery = EmbeddingsRefinery(
    embedding_model="minishlab/potion-base-32M",  # Required
)
```

## Usage

Use the `EmbeddingsRefinery` object as a callable or the
`refine` method to add embeddings to your chunks.

```python theme={"system"}
from chonkie import TokenChunker, EmbeddingsRefinery

test_string = "This is a test string. It will be chunked and embedded."
chunker = TokenChunker()
chunks = chunker(test_string)

# Add embeddings to the chunks
em_refinery = EmbeddingsRefinery(
    embedding_model="minishlab/potion-base-32M",  # Model string or BaseEmbeddings instance
)

chunks_with_embeddings = em_refinery(chunks)
```

## Parameters

<ParamField path="embedding_model" type="Union[str, BaseEmbeddings]">
  Model identifier or embedding model instance
</ParamField>