Define What Counts as a Duplicate Before Adding Embedding Search to Your Image Pipeline
My dedup check flagged two prints with identical composition but different colors as one image. Here is how I split duplicate detection into three layers, measured where perceptual hashing ends, and decided what embedding search is actually for.
Deleting the Source Isn't Enough — A Ledger Design for Propagating Deletes Through Gemini-Derived Data
When a user deletes their data, the embeddings, caches, and File Search documents you generated from it live on. A provenance ledger written at generation time, per-sink propagation workers, and a verification sweep make deletion actually reach your derived data.
Catch Near-Duplicate Images Before You Publish with gemini-embedding-2
This is about removing near-duplicates, not image search. Use gemini-embedding-2 multimodal embeddings to vectorize images, cluster them, and build a pre-publish gate — with working code and threshold guidance.
When Your pgvector Search Quietly Gets Worse — Field Notes on Protecting Recall with Gemini Embeddings
A semantic search built on Gemini Embeddings and PostgreSQL pgvector tends to lose precision over months without throwing a single error. These are field notes on the real causes — model pinning, operator/index mismatch, HNSW reindexing, and recall collapse under filters — with working code.
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.
Choosing the Right Gemini RAG Pattern in 2026 — Simple vs Advanced vs Agentic, Compared with Real Code
Compare three RAG implementation patterns with the Gemini API — Simple, Advanced, and Agentic — using real code examples. Learn which pattern fits your use case and where to start.
Dynamic Few-Shot for Gemini API — A Self-Improving Prompt That Picks Examples by Vector Search
Hand-picked, hard-coded few-shot examples stop scaling once your inputs drift. This guide builds a Gemini Embeddings + vector search pipeline that selects the best 3-5 examples per request and grows them from production feedback, with copy-paste code.
Beyond Embeddings: Production Reranking with Vertex AI Ranking and Gemini-as-Judge
When pure embedding search nails the top-3 but buries the right answer at rank 4, you need a reranker. This guide walks through a production-grade two-stage architecture using Vertex AI Ranking API and Gemini-as-judge — with cost, latency, and evaluation patterns that hold up under load.
Gemini API Embeddings vs Vector Databases: Pinecone, Qdrant, pgvector, and Cloud Spanner Compared for Production
Benchmark Pinecone, Qdrant, pgvector, and Cloud Spanner Vector using Gemini text-embedding-004 with real latency, cost, and code. The definitive production selection guide.
Building a Production RAG System with Gemini Embedding API and Pinecone
A step-by-step guide to building a production-ready RAG system using Gemini Embedding API and Pinecone. Covers index design, query optimization, chunking strategies, and cost management with practical Python code.
Multimodal RAG with Gemini API — Cross-Format Search over Images, PDFs, and Video
Build a production-grade multimodal RAG pipeline with Gemini 2.5 Pro: unified vector search across text, images, PDFs, and video with cost optimization and scaling patterns.
Building Production Semantic Search with Gemini Embeddings API — Design, Implementation, and Operations
A comprehensive guide to building production-grade semantic search with Gemini Embeddings API. Covers vector DB selection, reranking, recommendation engines, and cost optimization with practical code.