EmbeddingGemma 2: a new approach to local search across text, images and audio

The Google announcement presents a 740 million parameter model, with components for text, image, and audio, released under the Apache 2.0 license. It produces numerical representations that can be used to compare content. An assistant who writes an answer requires even more components.

In a fictitious maintenance example, a person is looking for a photo resembling a part, a report excerpt, or an audio segment describing an incident. The three searches require different file preparation. The result must also point to a document or passage that the user can verify.

The model can therefore contribute to a document search system. File ingestion, their segmentation, and the references returned still need to be organized. Quality must be measured based on searches that employees actually perform.

Local execution can meet certain infrastructure control needs. Check the available memory, indexing duration, and search delay on your hardware. A test on a few files does not necessarily describe the behavior of a larger document catalog.

Keep a set of questions and expected results. For each query, note whether the top results include the useful document and if the user can locate the relevant passage. Add difficult files, duplicates, and old documents. A precise search must be able to indicate the absence of relevant results.

Document protection requires access controls, backups, and update management. The model’s presence on a local server is not enough to handle these responsibilities. Also check any external services used elsewhere in the workflow.

Start with a limited corpus useful to a specific team. A maintenance service can select validated manuals and authorized intervention sheets. Define which documents to exclude and how to remove a file from the index when it is no longer up to date.

Permissions should follow users and sources. The RAG access controls documented by AWS story describes a case where permissions change after indexing. This case also deserves a test in a local system.

The RAG and knowledge base department can scope the corpus, the material and the search criteria. The choice of model comes with these constraints and the catalog maintenance procedure.