## Feature Extraction

Feature extraction is the task of converting a text into a vector (often called "embedding").

Example applications:
* Retrieving the most relevant documents for a query (for RAG applications).
* Reranking a list of documents based on their similarity to a query.
* Calculating the similarity between two sentences.

> [!TIP]
> For more details about the `feature-extraction` task, check out its [dedicated page](https://huggingface.co/tasks/feature-extraction)! You will find examples and related materials.

### Recommended models

- [thenlper/gte-large](https://huggingface.co/thenlper/gte-large): A powerful feature extraction model for natural language processing tasks.

Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=feature-extraction&sort=trending).

### Using the API

<InferenceSnippet
    pipeline=feature-extraction
    providersMapping={ {"hf-inference":{"modelId":"ibm-granite/granite-embedding-97m-multilingual-r2","providerModelId":"ibm-granite/granite-embedding-97m-multilingual-r2"},"scaleway":{"modelId":"Qwen/Qwen3-Embedding-8B","providerModelId":"qwen3-embedding-8b"}} }
/>

### API specification

#### Request

| Headers |   |    |
| :--- | :--- | :--- |
| **authorization** | _string_ | Authentication header in the form `'Bearer: hf_****'` when `hf_****` is a personal user access token with "Inference Providers" permission. You can generate one from [your settings page](https://huggingface.co/settings/tokens/new?ownUserPermissions=inference.serverless.write&tokenType=fineGrained). |

| Payload |  |  |
| :--- | :--- | :--- |
| **inputs*** | _unknown_ | One of the following: |
| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(#1)** | _string_ |  |
| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(#2)** | _string[]_ |  |
| **normalize** | _boolean_ |  |
| **prompt_name** | _string_ | The name of the prompt that should be used by for encoding. If not set, no prompt will be applied.  Must be a key in the `sentence-transformers` configuration `prompts` dictionary.  For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text to encode. |
| **truncate** | _boolean_ |  |
| **truncation_direction** | _enum_ | Possible values: left, right. |

#### Response

| Body |  |
| :--- | :--- | :--- |
| **(array)** | _array[]_ | Output is an array of arrays. |