Multimodal RAPTOR for Disaster Documents
What it is
A hierarchical retrieval system that combines RAPTOR (Recursive Approach to Prompting Over Time) with multimodal embeddings. It uses BLIP for image captioning and ColVBERT for text embeddings to create a structured tree of summaries for large-scale disaster documents.
The problem it solves
Standard RAG systems often struggle with long-context documents and multi-modal data (text + images), leading to lost context and low-quality summaries.
What you could use it for
- Retrieving information from large volumes of disaster response PDFs (e.g., tsunami lessons)
- Generating high-quality summaries of multi-page documents with images
- Building a multimodal RAG system that preserves complex disaster knowledge
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.