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app.py

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+from sentence_transformers import SentenceTransformer
+from flask import Flask, request, jsonify
+from flask_cors import CORS # Import CORS
+
+app = Flask(__name__)
+CORS(app) # Enable CORS for all routes
+
+# Load the model once when the application starts
+# This is efficient as it avoids reloading on every request.
+print("Loading sentence-transformer model...")
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+print("Model loaded successfully.")
+
+@app.route('/', methods=['GET'])
+def health_check():
+    """A simple endpoint to check if the service is running."""
+    return jsonify({
+        'status': 'ok',
+        'model': 'sentence-transformers/all-MiniLM-L6-v2'
+    })
+
+@app.route('/embed', methods=['POST'])
+def embed_text():
+    """The main endpoint to generate embeddings."""
+    data = request.json
+    if not data or 'text' not in data:
+        return jsonify({'error': 'No text provided in JSON body'}), 400
+    
+    text = data.get('text')
+    
+    if not isinstance(text, str) or not text.strip():
+        return jsonify({'error': 'Text must be a non-empty string'}), 400
+    
+    try:
+        # Generate embedding
+        embedding = model.encode(text) # No need to wrap in a list for a single string
+        
+        return jsonify({
+            'embedding': embedding.tolist(),
+            'model': 'all-MiniLM-L6-v2',
+            'dimension': len(embedding)
+        })
+    except Exception as e:
+        print(f"Error during embedding: {e}")
+        return jsonify({'error': 'Failed to generate embedding'}), 500

requirements.txt

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+flask
+sentence-transformers
+torch
+flask-cors