--- 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.