Your next search feature may not need a cloud API
Small retrieval models can change where we build search. The first gain is control over local data.
Google has released EmbeddingGemma 2. It maps text, code, images, audio and video into one shared search space.
The full model has 740 million parameters. Text-only use needs 270 million. Google releases it under Apache 2.0.
The weights are available now. This is something builders can test, not a promise to wait for.
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2
My read: small retrieval models can change where we build search. The first gain is control over local data.
Your pick: test local code or document search on one real dataset. Compare useful results, speed and memory with your current search.
Start with text. Add audio or images only when your users need them. Local search still needs access controls.
12 months: I expect more apps to offer local search.
24 months: I expect search across media to become common.
36 months: I expect buyers to demand clear choices about where indexing happens.
Why I may be wrong: Google's benchmarks may not predict results on messy files or low-cost devices.
What would you search locally first: code, documents or voice notes?