When Your Firestore × Gemini Embeddings RAG Quietly Degrades — Designing for Re-Embedding
A RAG built on Firestore native vector search and Gemini Embeddings drifts when the embedding model changes generations, and retrieval quality drops with no errors. Here is how to detect the drift, re-embed without downtime, and keep retrieval cost in check.
Putting Gemini Embeddings in the Browser — Building a Serverless FAQ Search with IndexedDB
How I shipped semantic search for a few hundred FAQ entries without standing up a vector database. Gemini Embedding runs once at build time, the index sits in IndexedDB, and searches happen in the browser.
Building a Production RAG System with Gemma 4: Local LLM + Vector Search Architecture
A complete guide to building production RAG systems with Gemma 4, ChromaDB, and pgvector. Covers architecture design, chunking strategies, Long-Context RAG using the 256K window, hybrid search, and performance optimization.
Applying TurboQuant to RAG and Vector Search — New Uses for KV Cache Compression
Google's TurboQuant compression technology extends beyond LLM inference to RAG pipeline vector databases. Learn how embedding vector compression can improve memory efficiency, search speed, and scalability for large-scale RAG systems.
Building RAG Agents with Gemini × LlamaIndex — From Document Search to Multi-Step Reasoning
A hands-on guide to building high-accuracy RAG agents with Gemini API and LlamaIndex — measured chunk-size comparisons, full hybrid-search and reranking implementations, an evaluation loop, and incremental sync that follows edits and deletions.