Tools and Frameworks: LlamaIndex Multimodal, ColPali, and an End-to-End Example
~14 min read
LlamaIndex provides ready-made multimodal RAG building blocks; ColPali takes a genuinely different approach, embedding whole page IMAGES directly and skipping text extraction entirely.
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Key points
- •LlamaIndex provides ready-made multimodal RAG components: dual-track document parsers, multimodal vector store integrations, and query engines that route images to a VLM for generation
- •ColPali takes a genuinely different approach: embed each whole page as a single image directly, skipping text extraction, chunking, and OCR entirely
- •ColPali's bet is that a capable VLM can represent a page's combined text+layout+visual content more faithfully than a pipeline that first tries to imperfectly separate 'text' from 'image'
- •This trades away sub-page retrieval granularity for avoiding the extraction failure modes (mangled tables, dropped charts) covered earlier in this topic
- •The end-to-end shape is still fundamentally standard RAG (retrieve, then generate grounded in what was retrieved) — extended at every stage to keep visual content in play instead of discarding it