Holy crap, Google just launched Gemini Embedding 2! Multimodal search is moving from 'stitched solutions' to 'native solutions.' No more tedious image-text recognition, no more audio transcription. Text, images, audio, video, PDFs—all thrown into the same vector space. Gemini Embedding 2 is rewriting the rules of RAG. The most badass part of Embedding 2 is its 'native' approach. It directly maps over 100 languages and all media types into a single dimension. Plus, you can mix inputs in a single request (e.g., an image + a question), and it can understand the subtle connections between them. How much can you pack into a single API call? Text: Up to 8K tokens of context ️ Images: Up to 6 images at once (PNG/JPEG) Video: Up to 120 seconds! (MP4/MOV) ️ Audio: Native direct reading, no middleware transcription needed Documents: Toss in up to 6 pages of PDFs directly The team has already achieved some insane results: 1️⃣ Creator Economy (Sparkonomy): By removing the LLM inference step, latency dropped by 70%! Image/video matching scores doubled. 2️⃣ Personal Health Assistant (Mindlid): Combining text conversations with audio-visual embeddings boosted Top-1 recall rate by 20%. 3️⃣ Legal Tech (Everlaw): Enabled precise evidence retrieval across millions of highly confidential case materials (including videos and images).