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LlamaIndex Multimodal Vector Index Setup

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Linked challenge: Geospatial Burn Severity & Reinsurance RAG using LlamaIndex and Multimodal Embeddings

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Linked challenge

Geospatial Burn Severity & Reinsurance RAG using LlamaIndex and Multimodal Embeddings

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Set up a LlamaIndex `MultiModalVectorIndex` combining PDF node documents and satellite image patch nodes with metadata indexing.

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Linked challenge

Geospatial Burn Severity & Reinsurance RAG using LlamaIndex and Multimodal Embeddings

Reinsurance underwriters processing catastrophe claims after El Niño-driven wildfires must integrate complex multi-modal evidence: high-resolution Sentinel SAR radar imagery, post-fire drone burn severity index maps, and thousands of pages of field adjuster reports. Querying this heterogenous corpus requires specialized geospatial RAG architecture. In this challenge, you will implement a multi-modal spatial Retrieval-Augmented Generation (RAG) system using LlamaIndex. The system will index satellite imagery embeddings alongside unstructured PDF claims documents into a unified vector store. Your solution will execute sub-question query engines to retrieve relevant document sections and raster patches, providing transparent, evidence-backed reinsurance claim risk answers.

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