Multimodal Lesson Reflection

Key Points:
1. Multimodal Retrieval on Images: Traditional retrieval systems often rely on text. However, multimodal retrieval systems, especially for images, offer enhanced performance by using image encoders and advanced vector databases.
2. Quantization: This process reduces the memory footprint of embedding vectors without significantly impacting performance, making it feasible to run complex models even on limited hardware.
3. Image-Based Document Indexing: We convert PDFs to image data to avoid missing visual cues. Use advanced models (like KolPali) to create dense embeddings for images, enhancing search and retrieval precision.
4. Vector Database Enhancements: Use multi-vector configurations and quantization to optimize storage and retrieval in vector databases like Qdrant.

Reflection Questions:
1. What are the limitations of traditional text-only retrieval systems when dealing with multimodal documents?
2. How does quantization help reduce computational resources, and how does it impact performance?
3. Why is it important to consider the underlying structure of documents, including visual components, during retrieval?
4. How do vector databases benefit from configurations like multi-vector setups and scalar quantization?
5. In what scenarios would using an image encoder over a text encoder significantly impact performance?

Challenge Exercises:
1. Convert a PDF document with images into a set of images and use an image encoder to create embeddings.
2. Implement quantization on a language model and observe performance and memory usage changes.
3. Set up a vector database using multi-vector configuration and insert multimodal data. Perform a search query and compare results with a single vector configuration.
4. Develop a function to integrate image retrieval results with a text-based querying system for a more robust RAG system.
5. Experiment with different vector database configurations to optimize retrieval speed and accuracy, and document the outcomes.