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.
For more details about the feature-extraction task, check out its dedicated page! You will find examples and related materials.
Recommended models
- 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.
Using the API
Python
JavaScript
cURL
import requests
API_URL = "https://api-inference.huggingface.co/models/thenlper/gte-large"
headers = {"Authorization": "Bearer hf_***"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Today is a sunny day and I will get some ice cream.",
})To use the Python client, see huggingface_hub’s package reference.
API specification
Request
| Payload | ||
|---|---|---|
| inputs* | string | The text to embed. |
| 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. |
Some options can be configured by passing headers to the Inference API. Here are the available headers:
| Headers | ||
|---|---|---|
| authorization | string | Authentication header in the form 'Bearer: hf_****' when hf_**** is a personal user access token with Inference API permission. You can generate one from your settings page. |
| x-use-cache | boolean, default to true | There is a cache layer on the inference API to speed up requests we have already seen. Most models can use those results as they are deterministic (meaning the outputs will be the same anyway). However, if you use a nondeterministic model, you can set this parameter to prevent the caching mechanism from being used, resulting in a real new query. Read more about caching here. |
| x-wait-for-model | boolean, default to false | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error, as it will limit hanging in your application to known places. Read more about model availability here. |
For more information about Inference API headers, check out the parameters guide.
Response
| Body | ||
|---|---|---|
| (array) | array[] | Output is an array of arrays. |