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rag-multimodalemerging
Multi-modal RAG
Text + image + PDF vision. Understands documents humans see.
Overview
Extends standard RAG to handle images, tables, charts, and mixed PDF layouts using vision models. Embeds both text and visual content, enabling queries that require reading diagrams, interpreting charts, or understanding complex document layouts.
Architecture
Interactive walkthrough01
Ingestion
PDF Parserpypdf extraction
Image Extractorfigures + charts
Vision CaptionerGPT-4o describes images
Dual Vector Storestext + image indexes
02
Retrieval
Text Retrievaldense top-k
Image Retrievalcaption matching
Modality Mergerunified ranking
03
Generation
Multimodal Prompttext + image captions
LLM Callvision-grounded
Cited Answerpage + figure refs
01
Ingestion
PDF Parserpypdf extraction
Image Extractorfigures + charts
Vision CaptionerGPT-4o describes images
Dual Vector Storestext + image indexes
02
Retrieval
Text Retrievaldense top-k
Image Retrievalcaption matching
Modality Mergerunified ranking
03
Generation
Multimodal Prompttext + image captions
LLM Callvision-grounded
Cited Answerpage + figure refs
Summarized pipeline view. For the full interactive, scroll-driven walkthrough with clickable stages → Pipeline detail
When to use
Use when
- corpus contains PDFs with images/charts/tables
- queries reference visual content
- document layout carries meaning
- financial reports, scientific papers, slide decks
Avoid when
- corpus is pure text
- cost sensitivity is high (vision model calls)
- latency < 500ms required
- simple text search suffices
Compatible vector databases
QdrantPineconeWeaviate
Compatible frameworks
llamaindexraw pythonlangchain
#multimodal#vision#pdf#images#tables#gpt-4v#claude-vision
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