Doppel Feature Implementation Guide

Face matching and liveness verification in a Flutter app are implemented using 128-dimensional embeddings with a pgvector IVFFlat index and AWS Rekognition for random liveness missions. This combination ensures biometric security and validates active user presence.

Real-time chat with translation in FastAPI is built using WebSocket connections to deliver emotion-preserving translation. This architecture handles concurrent message streaming while maintaining the original tone and context of user conversations across languages.

The pgvector IVFFlat index works for face matching by partitioning 128-dimensional embedding vectors into clusters for fast approximate nearest neighbor searches. It enables rapid similarity comparisons against stored facial profiles within the PostgreSQL database.

AWS Rekognition handles liveness checks by assigning random verification missions to users, eliminating the need for custom models. It validates active presence through API-driven analysis, securing the signup and profile lifecycle directly.

Yes, the Doppel feature guide includes data privacy and security requirements for FastAPI implementations, specifically covering encryption, rate limits, and audits. These non-functional requirements are baked into the modular architecture and API contracts.

Structuring an end-to-end user lifecycle from signup to push notifications involves coordinating data models and API contracts across Flutter and FastAPI layers. The process covers profile creation, liveness, matching, chat, and notification delivery.