A Japanese query won't surface its English twin — when embeddings notice language before meaning
Embed a translation pair with gemini-embedding-2 and the two halves won't be nearest neighbours, because language itself inflates similarity. Here is how I measured cross-lingual recall using translation pairs as ground truth, and what happened when I subtracted the language centroid.
A near-miss label won't fix itself on retry — a normalization layer for closed-vocabulary classification
When responseSchema enum returns an out-of-set label, retrying tends to return the same near-miss. From a wallpaper app's 30-category batch, here is the distribution of how labels miss, plus a normalization layer built on an alias table and gemini-embedding-2 nearest-neighbor, with measured results.
The Day We Went From 30 Categories to 34 — Reclassifying 1,180 Assets Instead of 8,142
Adding categories to a taxonomy does not require reclassifying everything. Here is how embeddings and confidence margins narrowed a backfill from 8,142 assets to 1,180, with the numbers.
Your File Search Store Goes Stale in Production — Catalog Sync and Drift Detection That Actually Hold
Load a catalog into File Search once and forget it, and within weeks it starts confidently pointing users at assets you already pulled. Here is the sync pipeline I run: hash-based incremental import, a blue/green rebuild that swallows deletions, and a nightly drift audit.
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.
Put Help Docs and Screenshots in One File Search Store and Return Answers That Cite the Image Too
Your text help docs and your screenshots live in separate stores, so a single question can never return both the steps and the matching screen. With gemini-embedding-2 going multimodal in File Search, here is how I merged them and returned the cited screenshot alongside the answer.
Rebuilding a Three-Layer RAG Cache After Migrating to Gemini 3.5 Flash
When Gemini 2.0 Flash was retired, I rebuilt my RAG caching stack around 3.5 Flash. Here are the working implementations for response, semantic, and embedding caches, measured hit rates from production, and how self-managed caching divides the work with the API's Context Caching.