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.
For more details about the feature-extraction task, check out its dedicated page! You will find examples and related materials.
Explore all available models and find the one that suits you best here.
<InferenceSnippet pipeline=feature-extraction providersMapping={ {"hf-inference":{"modelId":"intfloat/multilingual-e5-large-instruct","providerModelId":"intfloat/multilingual-e5-large-instruct"},"sambanova":{"modelId":"intfloat/e5-mistral-7b-instruct","providerModelId":"E5-Mistral-7B-Instruct"}} } />
| 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. |
| Payload | ||
|---|---|---|
| inputs* | unknown | One of the following: |
| (#1) | string | |
| (#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. |
| Body | | | :--- | :--- | :--- | | (array) | array[] | Output is an array of arrays. |
