Technical guide
Text and images can participate in one vector search when their encoders are jointly designed or aligned and the application follows one model version's input and output contract. Truncating two unrelated vectors to the same length matches a storage shape; it does not establish semantic comparability.
What the current model documentation establishes
Google introduced EmbeddingGemma 2 on October 6, 2026. Its official model card lists text, image, audio and video inputs. This guide explains integration conditions for text and image retrieval and reports no local deployment results. The model was listed in the Hugging Face Trending list on October 18, 2026. That is a platform attention signal, not search volume or enterprise adoption.
Fields for a retrieval input contract
Swipe the table horizontally to view all columns.
| Contract item | What both text and image processing must establish | Handling a mismatch |
|---|---|---|
| Model and processor | Model identifier, revision, encoders and processor versions | Use separate indexes or re-encode; do not mix outputs directly |
| Input rules | Text length and splitting; image decoding, orientation, resizing and cropping | Retain the original source and actual transformation record |
| Task mode | Query-specific and document-specific prompts or API parameters | Follow the model card rather than treating every input as the same task |
| Vector output | Dimensions, normalization, numeric type and similarity calculation | Check a small consistency sample before indexing |
| Source and access | Document or image identifier, page, version, owner and visibility | Enforce authorization in the retrieval API and provide source navigation |
This table defines an interface; it is not a model benchmark. Vector similarity expresses similarity under the configured model. Business relevance still needs representative questions and independently checkable answers.
A small input-pipeline check
Prepare a description and a clear image of the same object, an unrelated image and restricted material. Record their expected relationships. Check encoder outputs, retrieval ordering, source navigation and unauthorized requests separately. Reuse the same inputs after model, text-splitting or image-cropping changes so that index maintenance problems are distinguishable from changes in model behavior.
Can an existing knowledge base switch directly to multimodal retrieval?
The Chinese-language knowledge-base project report documents material processing, local OCR, source retrieval and member permissions. The published record does not show that the project uses EmbeddingGemma 2. Image retrieval requires additional image processing, shared encoding configuration and authorization scenarios. Provide sample materials, questions, access groups and deployment conditions through the Chinese-language knowledge-base development service to define a verifiable first retrieval work package.
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