EmbeddingGemma 2

EmbeddingGemma 2 puts text, code, images, video and audio (or a mix) into one 768-dimension vector, from a 740M open model under Apache 2.0. Image, video and audio tokens are spliced into the text sequence and run through one encoder, so a text query can find a video moment or a scanned PDF page without captioning or transcribing first.

• It’s not the quality leader. Text is flat against v1; the gain is code (MTEB Code 68.8 to 78.7). Audio (49) and video (51) scores are modest. All numbers are Google’s own.
• Every major vendor ships one-space multimodal embeddings now: Amazon Nova, Gemini Embedding 2, Voyage, Cohere, and the open Qwen3-VL-Embedding, which reports higher image and document scores but has no audio. Pick by constraint. EmbeddingGemma 2’s niche is open weights plus audio.
• Matryoshka truncation isn’t free: its MMEB score falls from 59.0 at 768 dimensions to 45.7 at 128.
• The interesting part is the system around the model. DigUp is local file search on it: one vector per photo, PDF page, video shot and 30 s of audio, each pointing back to its page or second, next to an FTS5 keyword index, because no embedding holds an invoice number exactly.
• DigUp fuses the two by turning cosine into a per-query z-score (how far a match stands out for this query), gating on it, and adding a word-coverage bonus. It beat reciprocal-rank fusion on their evals (R@1 0.88 vs 0.73) and can honestly say “nothing found”. Small, author-run eval.
• It also pins llama.cpp, records the model version in the index, and checks every upgrade against stored reference vectors. Vectors from two runtimes don’t mix, and nothing errors when they do.