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Update README to reflect changes in embedding response handling and adjust example usage
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| title: Vector Endpoint | |
| emoji: 📉 | |
| colorFrom: red | |
| colorTo: indigo | |
| sdk: docker | |
| pinned: false | |
| # Vector Endpoint | |
| A simple API that converts text into vector embeddings using the [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) sentence transformer model. | |
| ## API Reference | |
| ### Endpoint | |
| ``` | |
| POST /vectorize | |
| ``` | |
| ### Request Format | |
| ```json | |
| { | |
| "text": "Your text to be vectorized" | |
| } | |
| ``` | |
| ### Response Format | |
| ```json | |
| { | |
| "embedding": [0.123, 0.456, ...] // Vector representation of your text | |
| } | |
| ``` | |
| ## Usage Examples | |
| ### cURL | |
| ```bash | |
| curl -X 'POST' \ | |
| 'https://placingholocaust-vector-endpoint.hf.space/vectorize' \ | |
| -H 'accept: application/json' \ | |
| -H 'Content-Type: application/json' \ | |
| -d '{ | |
| "text": "This is a text" | |
| }' | |
| ``` | |
| ### Python | |
| ```python | |
| import requests | |
| import json | |
| url = "https://placingholocaust-vector-endpoint.hf.space/vectorize" | |
| headers = { | |
| "accept": "application/json", | |
| "Content-Type": "application/json" | |
| } | |
| data = { | |
| "text": "This is a text" | |
| } | |
| response = requests.post(url, headers=headers, json=data) | |
| result = response.json() | |
| print(f"Embedding length: {len(result)}") | |
| print(f"First few values: {result[:5]}") | |
| ``` | |
| ### JavaScript | |
| ```javascript | |
| // Using fetch | |
| async function getEmbedding(text) { | |
| const response = await fetch( | |
| "https://placingholocaust-vector-endpoint.hf.space/vectorize", | |
| { | |
| method: "POST", | |
| headers: { | |
| "accept": "application/json", | |
| "Content-Type": "application/json" | |
| }, | |
| body: JSON.stringify({ text }) | |
| } | |
| ); | |
| const data = await response.json(); | |
| return data | |
| } | |
| // Example usage | |
| getEmbedding("This is a text") | |
| .then(embedding => { | |
| console.log(`Embedding length: ${embedding.length}`); | |
| console.log(`First few values: ${embedding.slice(0, 5)}`); | |
| }) | |
| .catch(error => console.error("Error:", error)); | |
| ``` | |
| ## Model Information | |
| This endpoint uses the [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) model, which produces 768-dimensional embeddings that capture semantic meaning of text across multiple languages. | |