Multi-Modal RAG with Images and Documents

Text-only RAG misses visual evidence present in charts, diagrams, screenshots, scanned forms, and tables. Multi-modal RAG extends retrieval and reasoning across text and image modalities.

Core concept: use shared or aligned embedding spaces so text queries can retrieve image evidence and image queries can retrieve related text context.

Reference architecture:

1. Extraction: parse documents into text blocks, tables, and images (layout-aware extraction strongly preferred).
2. Embedding: text embeddings for textual chunks, CLIP-like embeddings for image assets.
3. Indexing: store vectors with modality tags and rich metadata (type, page, region, source, tenant, timestamp).
4. Retrieval: run cross-modal search with modality-aware filtering and ranking.
5. Generation: send selected text+images to a vision-capable LLM with citation constraints.

Design decisions that matter:

• OCR vs visual embeddings: OCR alone loses chart geometry and visual relationships; image embeddings preserve visual semantics.
• Chunk-image alignment: connect nearby text and image regions so answers can combine both reliably.
• Storage pressure: image vectors and thumbnails increase index size; lifecycle/retention policies are essential.

Failure modes: retrieving decorative images with high similarity, missing small chart text due to weak extraction, and answer generation that ignores modality citations. Production systems need modality-aware eval sets, not text-only eval.

Interview-Ready Deepening

Source-backed reinforcement: these points add detail beyond short-duration UI hints and emphasize production tradeoffs.

• Embedding and retrieving images alongside text using unified vector spaces.
• Failure modes: retrieving decorative images with high similarity, missing small chart text due to weak extraction, and answer generation that ignores modality citations.
• Extraction : parse documents into text blocks, tables, and images (layout-aware extraction strongly preferred).
• Multi-modal RAG extends retrieval and reasoning across text and image modalities.
• We will have to convert it into B 64 because that is how images are transferred in the internet.
• Embedding : text embeddings for textual chunks, CLIP-like embeddings for image assets.
• Generation : send selected text+images to a vision-capable LLM with citation constraints.
• But we have to somehow embed it, convert it into embeddings and store it in the vector database in order for the retriever to retrieve those chunks.

Tradeoffs You Should Be Able to Explain

• Higher recall often increases context noise; reranking and filtering are required to keep precision high.
• Smaller chunks improve semantic precision but can break cross-sentence context needed for accurate answers.
• Aggressive grounding reduces hallucinations but can increase abstentions when retrieval coverage is weak.

First-time learner note: Master one stage at a time: ingestion, retrieval, then grounded generation. Validate each stage with small test questions before tuning everything together.

Production note: Treat quality as measurable system behavior. Track retrieval relevance, groundedness, and abstention quality with repeatable eval sets.