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Most RAG failures originate at retrieval, not generation. Text-first pipelines lose layout semantics, table structure, and figure grounding during PDF→text conversion, degrading recall and precision before an LLM ever runs. Vision-RAG—retrieving rendered pages with vision-language embeddings—directly targets this bottleneck and shows material end-to-end gains on visually rich corpora.

Pipelines (and where they fail)

Text-RAG. PDF → (parser/OCR) → text chunks → text embeddings → ANN index → retrieve → LLM. Typical failure modes: OCR noise, multi-column flow breakage, table cell structure loss, and missing figure/chart semantics—documented by table- and doc-VQA benchmarks created to measure exactly these gaps.

Vision-RAG. PDF → page raster(s) → VLM embeddings (often multi-vector with late-interaction scoring) → ANN index → retrieve → VLM/LLM consumes high-fidelity crops or full pages. This preserves layout and figure-text grounding; recent systems (ColPali, VisRAG, VDocRAG) validate the approach.

What current evidence supports

Document-image retrieval works and is simpler. ColPali embeds page images and uses late-interaction matching; on the ViDoRe benchmark it outperforms modern text pipelines while remaining end-to-end trainable.
End-to-end lift is measurable. VisRAG reports 25–39% end-to-end improvement over text-RAG on multimodal documents when both retrieval and generation use a VLM.
Unified image format for real-world docs. VDocRAG shows that keeping documents in a unified image format (tables, charts, PPT/PDF) avoids parser loss and improves generalization; it also introduces OpenDocVQA for evaluation.
Resolution drives reasoning quality. High-resolution support in VLMs (e.g., Qwen2-VL/Qwen2.5-VL) is explicitly tied to SoTA results on DocVQA/MathVista/MTVQA; fidelity matters for ticks, superscripts, stamps, and small fonts.

Costs: vision context is (often) order-of-magnitude heavier—because of tokens

Vision inputs inflate token counts via tiling, not necessarily per-token price. For GPT-4o-class models, total tokens ≈ base + (tile_tokens × tiles), so 1–2 MP pages can be ~10× cost of a small text chunk. Anthropic recommends ~1.15 MP caps (~1.6k tokens) for responsiveness. By contrast, Google Gemini 2.5 Flash-Lite prices text/image/video at the same per-token rate, but large images still consume many more tokens. Engineering implication: adopt selective fidelity (crop > downsample > full page).

Design rules for production Vision-RAG

Align modalities across embeddings. Use encoders trained for textimage alignment (CLIP-family or VLM retrievers) and, in practice, dual-index: cheap text recall for coverage + vision rerank for precision. ColPali’s late-interaction (MaxSim-style) is a strong default for page images.
Feed high-fidelity inputs selectively. Coarse-to-fine: run BM25/DPR, take top-k pages to a vision reranker, then send only ROI crops (tables, charts, stamps) to the generator. This preserves crucial pixels without exploding tokens under tile-based accounting.
Engineer for real documents.
• Tables: if you must parse, use table-structure models (e.g., PubTables-1M/TATR); otherwise prefer image-native retrieval.
• Charts/diagrams: expect tick- and legend-level cues; resolution must retain these. Evaluate on chart-focused VQA sets.
• Whiteboards/rotations/multilingual: page rendering avoids many OCR failure modes; multilingual scripts and rotated scans survive the pipeline.
• Provenance: store page hashes and crop coordinates alongside embeddings to reproduce exact visual evidence used in answers.

When Text-RAG is still the right default?

Clean, text-dominant corpora (contracts with fixed templates, wikis, code)
Strict latency/cost constraints for short answers
Data already normalized (CSV/Parquet)—skip pixels and query the table store

Evaluation: measure retrieval + generation jointly

Add multimodal RAG benchmarks to your harness—e.g., M²RAG (multi-modal QA, captioning, fact-verification, reranking), REAL-MM-RAG (real-world multi-modal retrieval), and RAG-Check (relevance + correctness metrics for multi-modal context). These catch failure cases (irrelevant crops, figure-text mismatch) that text-only metrics miss.

Summary

Text-RAG remains efficient for clean, text-only data. Vision-RAG is the practical default for enterprise documents with layout, tables, charts, stamps, scans, and multilingual typography. Teams that (1) align modalities, (2) deliver selective high-fidelity visual evidence, and (3) evaluate with multimodal benchmarks consistently get higher retrieval precision and better downstream answers—now backed by ColPali (ICLR 2025), VisRAG’s 25–39% E2E lift, and VDocRAG’s unified image-format results.

References:

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“}]] [[{“value”:”Most RAG failures originate at retrieval, not generation. Text-first pipelines lose layout semantics, table structure, and figure grounding during PDF→text conversion, degrading recall and precision before an LLM ever runs. Vision-RAG—retrieving rendered pages with vision-language embeddings—directly targets this bottleneck and shows material end-to-end gains on visually rich corpora. Pipelines (and where they fail) Text-RAG. PDF
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Standard	Text-RAG	Vision-RAG
Ingest pipeline	PDF → parser/OCR → text chunks → text embeddings → ANN	PDF → page render(s) → VLM page/crop embeddings (often multi-vector, late interaction) → ANN. ColPali is a canonical implementation.
Primary failure modes	Parser drift, OCR noise, multi-column flow breakage, table structure loss, missing figure/chart semantics. Benchmarks exist because these errors are common.	Preserves layout/figures; failures shift to resolution/tiling choices and cross-modal alignment. VDocRAG formalizes “unified image” processing to avoid parsing loss.
Retriever representation	Single-vector text embeddings; rerank via lexical or cross-encoders	Page-image embeddings with late interaction (MaxSim-style) capture local regions; improves page-level retrieval on ViDoRe.
End-to-end gains (vs Text-RAG)	Baseline	+25–39% E2E on multimodal docs when both retrieval and generation are VLM-based (VisRAG).
Where it excels	Clean, text-dominant corpora; low latency/cost	Visually rich/structured docs: tables, charts, stamps, rotated scans, multilingual typography; unified page context helps QA.
Resolution sensitivity	Not applicable beyond OCR settings	Reasoning quality tracks input fidelity (ticks, small fonts). High-res document VLMs (e.g., Qwen2-VL family) emphasize this.
Cost model (inputs)	Tokens ≈ characters; cheap retrieval contexts	Image tokens grow with tiling: e.g., OpenAI base+tiles formula; Anthropic guidance ~1.15 MP ≈ ~1.6k tokens. Even when per-token price is equal (Gemini 2.5 Flash-Lite), high-res pages consume far more tokens.
Cross-modal alignment need	Not required	Critical: textimage encoders must share geometry for mixed queries; ColPali/ViDoRe demonstrate effective page-image retrieval aligned to language tasks.
Benchmarks to track	DocVQA (doc QA), PubTables-1M (table structure) for parsing-loss diagnostics.	ViDoRe (page retrieval), VisRAG (pipeline), VDocRAG (unified-image RAG).
Evaluation approach	IR metrics plus text QA; may miss figure-text grounding issues	Joint retrieval+gen on visually rich suites (e.g., OpenDocVQA under VDocRAG) to capture crop relevance and layout grounding.
Operational pattern	One-stage retrieval; cheap to scale	Coarse-to-fine: text recall → vision rerank → ROI crops to generator; keeps token costs bounded while preserving fidelity. (Tiling math/pricing inform budgets.)
When to prefer	Contracts/templates, code/wikis, normalized tabular data (CSV/Parquet)	Real-world enterprise docs with heavy layout/graphics; compliance workflows needing pixel-exact provenance (page hash + crop coords).
Representative systems	DPR/BM25 + cross-encoder rerank	ColPali (ICLR’25) vision retriever; VisRAG pipeline; VDocRAG unified image framework.

Vision-RAG vs Text-RAG: A Technical Comparison for Enterprise Search Michal Sutter Artificial Intelligence Category – MarkTechPost