Tag: Semantic Search
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Gemini Embedding 2: One Model to Index Them All
Imagine building a search system that can handle text, images, audio recordings, video clips, and PDFs—all within the same search query. Traditionally, this would require a complex pipeline: multiple vector stores, various specialized embedding models (like CLIP for images or Whisper for audio transcription), and a messy fusion layer to combine the results. [01:32] With…