VoiceVision RAG - Integrating Visual Document Intelligence with Voice Response
summary
Claims that Colpali generates multi-vector embeddings directly from document pages to bypass OCR and that late-interaction patch embeddings (token×patch dot products with a max-sum) return precise relevant pages; demo loads Colpali v1.3 from Hugging Face, stores 128‑dim vectors in a local Quadrant vector DB collection with multi-vector max comparator, retrieves top pages, and feeds those images plus the query to multimodal LLMs (Olama or Bedrock Claude Sonnet 3.7) wrapped as a tool in a strands agent pipeline that adds synthesized output via a speak voice tool.
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chapters (9)
- 0:00Introduction and setup
- 7:25Multimodal RAG overview
- 15:39Vision-based retrieval with PaLI
- 22:30Vision-based language models
- 31:21PaLI embedding process
- 46:33Demo setup and data processing
- 53:00Strands agent framework
- 1:01:48Voice integration demo
- 1:12:13Q&A and production insights
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