World's Fair 2025
VoiceVision RAG - Integrating Visual Document Intelligence with Voice Response — Suman Debnath, AWS
Overview
This talk introduces VoiceVision RAG, a system that integrates visual document intelligence with voice response capabilities. It explores multimodal RAG techniques, focusing on vision-based retrieval using the Pali model. The presentation demonstrates how to build agentic applications with the Strands framework, enabling voice-enabled retrieval and response generation from visual documents.
Who should watch
- AI engineers and builders interested in multimodal RAG.
- Product Managers exploring new capabilities for document intelligence.
- Developers looking to integrate visual understanding into agentic workflows.
- Those facing challenges with traditional OCR or text-based extraction from image-heavy documents.
Key takeaways
- Traditional multimodal RAG involves extracting text, images, and tables separately, embedding them, and then using a multimodal LLM.
- Vision-based retrieval, exemplified by the Pali model, treats each page of a document as an image, creating patches and generating embeddings for each patch.
- The Pali model uses a vision-based encoder and a linear projection to create embeddings that are compatible with text queries, enabling semantic search across visual document elements.
- The Strands framework simplifies building agentic applications by combining models and tools, allowing for custom retrieval tools and voice output.
- For complex or image-dense documents where traditional OCR fails, vision-based retrieval offers a more robust solution by directly processing visual information.
- The effectiveness of vision-based retrieval depends on the nature of the data; it is particularly useful for documents where visual context is crucial for understanding.
- Integrating voice output can be achieved by adding a speak tool to the agentic workflow, allowing for spoken responses to queries.
- While Pali is effective, it can be computationally heavy during the embedding creation phase, though query time remains fast.
Notable quotes
*The premise is this. If you use if if your data with your data any of these three options works you just go with this what we're going to discuss in next one hour you it's not relevant for you but this you know what we are going to discuss is an option number four which is a smarter technique which is based on a vision based model to perform the retrieval.*
*The core idea of strand is just pip install and use it with the by default tools and your model of your choice. That's all.*
Unofficial community note. Prefer the recording for nuance.