lexguard-backend / src /models /PredatoryTrap.js
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const mongoose = require('mongoose');
const predatoryTrapSchema = new mongoose.Schema({
trap_type: {
type: String,
required: true,
index: true
},
severity: {
type: String,
enum: ['high', 'critical'],
required: true
},
text: {
type: String,
required: true
},
embedding: {
type: [Number], // 1024-d BGE-m3 vector
required: true
}
}, { timestamps: true });
// Create a 2dsphere or vector index in MongoDB Atlas for the embedding field
// Note: Actual Vector Search index configuration requires Atlas Search UI/API setup
// using {"type": "knnVector", "dimensions": 1024, "similarity": "cosine"}
const PredatoryTrap = mongoose.model('PredatoryTrap', predatoryTrapSchema);
module.exports = PredatoryTrap;