Hands-On Multimodal RAG Images, Tables & Text
AI Summary
Summary of Video: Building a Multimodal Retrieval System
- Introduction
- Most current systems for retrieval augmented generation are text-based.
- Real-world documents contain multimodal data (text, images, tables).
- The video demonstrates building a multimodal rack system to process text, images, and tables.
- Traditional Approach
- In classical systems, documents are chunked into text, and images/tables are parsed.
- Captions are generated using vision language models (VLMs), leading to potential data loss.
- Proposed Workflow
- Introduces Poly, which allows for direct processing of images as part of the indexing process.
- Discusses Cohair’s Embed V4, a multimodal embedding approach that produces state-of-the-art results without losing context.
- Cost and Implementation
- Embedding sizes range from 512 to 1536 dimensions and can be quantized to reduce costs and storage requirements.
- Quantization of embeddings preserves performance while lowering resource consumption.
- Embedding Process
- Images are converted to embeddings and stored in a vector store.
- User queries are embedded, and results retrieved using cosine similarity.
- Highlights the need for a strong vision language model like Gemini for responding to queries based on visual content.
- Examples of Questions
- Demonstrates how the model processes complex queries using infographics (e.g., showcasing Nike’s profits).
- Shows the model’s reasoning capabilities over visual data.
- Local Solutions
- For users preferring local solutions, mentions the Call Pony for setting up a vision-based retrieval system without proprietary APIs.
- Promotes using Local GPT for a fully local solution, combining open-source and proprietary models where preferred.
- Conclusion
- Emphasizes the importance of multimodal retrieval systems in enterprise search.
- Suggests the potential for significant advancements in retrieval techniques.
Key Takeaways
- Multimodal systems can enhance the understanding and processing of complex data such as images and tables.
- Explore advanced techniques for embedding and retrieval to improve system performance.