Multimodal Embeddings Reshape RAG: Ditch Lossy Text Conversion for Native Retrieval
CShorten30 · x · 2026-08-07
Traditional RAG pipelines rely on transcribing or OCR-ing audio, PDFs, and videos into plain text, which inherently loses critical information like tone, table structures, or visual context.
With new multimodal embedding models like Gemini Embedding 2, text, images, audio, and video can be mapped into a shared embedding space. Using Weaviate, developers can configure multimodal fields via multi2vecgooglegemini() to enable cross-modal native retrieval.
This approach allows a text query to directly retrieve an intact PDF page, a specific video segment, or an audio chunk preserving its original tone. The retrieved media can then be passed directly to a multimodal generation model, significantly enhancing RAG fidelity.
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