prasad-vennam Android Local RAG & Vector Databases 🔎
Retrieval-Augmented Generation (RAG) on Edge devices provides privacy-first, low-latency contextual intelligence. Instead of using cloud Pinecone/Weaviate, this skill enforces local vector embeddings via Room or SQLite.
⚡ When to Use
- When tasked with "RAG", "semantic search", or "vector embeddings".
- Building highly private AI features.
🏗️ Core Rules / Pillars
1. Vector Search in Room
- Pattern: Standard Room does not have native vector similarity out of the box in stable. Use L2 distance extensions or
sqlite-vss. - Implementation: Avoid pulling unmaintained third-party vector DBs. Use SQLite virtual tables with FTS or simple cosine similarity functions if embeddings are small.
🚧 Critical Anti-Hallucination Guards
- Trap: Do NOT hallucinate that Room natively supports
SELECT * FROM tbl ORDER BY VECTOR_DISTANCE()securely out of the box without specific extensions.