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Vision-RAG vs Text-RAG: A Technical Comparison for Enterprise Search

ByRicardo

Most RAG failures originate at retrieval, not era. Text-first pipelines lose format semantics, desk construction, and determine grounding throughout PDF→textual content conversion, degrading recall and precision earlier than an LLM ever runs. Vision-RAG—retrieving rendered pages with vision-language embeddings—straight targets this bottleneck and reveals materials end-to-end features on visually wealthy corpora.

Pipelines (and the place they fail)

Text-RAG. PDF → (parser/OCR) → textual content chunks → textual content embeddings → ANN index → retrieve → LLM. Typical failure modes: OCR noise, multi-column stream breakage, desk cell construction loss, and lacking determine/chart semantics—documented by table- and doc-VQA benchmarks created to measure precisely these gaps.

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

What present proof helps

  • Document-image retrieval works and is less complicated. ColPali embeds web page photos and makes use of late-interaction matching; on the ViDoRe benchmark it outperforms trendy textual content pipelines whereas remaining end-to-end trainable.
  • End-to-end elevate is measurable. VisRAG experiences 25–39% end-to-end enchancment over text-RAG on multimodal paperwork when each retrieval and era use a VLM.
  • Unified picture format for real-world docs. VDocRAG reveals that holding paperwork in a unified picture format (tables, charts, PPT/PDF) avoids parser loss and improves generalization; it additionally introduces OpenDocVQA for analysis.
  • Resolution drives reasoning high quality. High-resolution assist in VLMs (e.g., Qwen2-VL/Qwen2.5-VL) is explicitly tied to SoTA outcomes on DocVQA/MathVista/MTVQA; constancy issues for ticks, superscripts, stamps, and small fonts.

Costs: imaginative and prescient context is (usually) order-of-magnitude heavier—due to tokens

Vision inputs inflate token counts through tiling, not essentially per-token value. For GPT-4o-class fashions, whole tokens ≈ base + (tile_tokens × tiles), so 1–2 MP pages could be ~10× value of a small textual content chunk. Anthropic recommends ~1.15 MP caps (~1.6k tokens) for responsiveness. By distinction, Google Gemini 2.5 Flash-Lite costs textual content/picture/video on the similar per-token price, however massive photos nonetheless devour many extra tokens. Engineering implication: undertake selective constancy (crop > downsample > full web page).

Design guidelines for manufacturing Vision-RAG

  1. Align modalities throughout embeddings. Use encoders skilled for textual content↔picture alignment (CLIP-family or VLM retrievers) and, in follow, dual-index: low-cost textual content recall for protection + imaginative and prescient rerank for precision. ColPali’s late-interaction (MaxSim-style) is a robust default for web page photos.
  2. Feed high-fidelity inputs selectively. Coarse-to-fine: run BM25/DPR, take top-k pages to a imaginative and prescient reranker, then ship solely ROI crops (tables, charts, stamps) to the generator. This preserves essential pixels with out exploding tokens beneath tile-based accounting.
  3. Engineer for actual paperwork.
    • Tables: for those who should parse, use table-structure fashions (e.g., PubTables-1M/TATR); in any other case desire image-native retrieval.
    • Charts/diagrams: count on tick- and legend-level cues; decision should retain these. Evaluate on chart-focused VQA units.
    • Whiteboards/rotations/multilingual: web page rendering avoids many OCR failure modes; multilingual scripts and rotated scans survive the pipeline.
    • Provenance: retailer web page hashes and crop coordinates alongside embeddings to breed precise visible proof utilized in solutions.
Standard Text-RAG Vision-RAG
Ingest pipeline PDF → parser/OCR → textual content chunks → textual content embeddings → ANN PDF → web page render(s) → VLM web page/crop embeddings (usually multi-vector, late interplay) → ANN. ColPali is a canonical implementation.
Primary failure modes Parser drift, OCR noise, multi-column stream breakage, desk construction loss, lacking determine/chart semantics. Benchmarks exist as a result of these errors are widespread. Preserves format/figures; failures shift to decision/tiling decisions and cross-modal alignment. VDocRAG formalizes “unified picture” processing to keep away from parsing loss.
Retriever illustration Single-vector textual content embeddings; rerank through lexical or cross-encoders Page-image embeddings with late interplay (MaxSim-style) seize native areas; improves page-level retrieval on ViDoRe.
End-to-end features (vs Text-RAG) Baseline +25–39% E2E on multimodal docs when each retrieval and era are VLM-based (VisRAG).
Where it excels Clean, text-dominant corpora; low latency/value Visually wealthy/structured docs: tables, charts, stamps, rotated scans, multilingual typography; unified web page context helps QA.
Resolution sensitivity Not relevant past OCR settings Reasoning high quality tracks enter constancy (ticks, small fonts). High-res doc VLMs (e.g., Qwen2-VL household) emphasize this.
Cost mannequin (inputs) Tokens ≈ characters; low-cost retrieval contexts Image tokens develop with tiling: e.g., OpenAI base+tiles system; Anthropic steerage ~1.15 MP ≈ ~1.6k tokens. Even when per-token value is equal (Gemini 2.5 Flash-Lite), high-res pages devour much more tokens.
Cross-modal alignment want Not required Critical: textual content↔picture encoders should share geometry for blended queries; ColPali/ViDoRe reveal efficient page-image retrieval aligned to language duties.
Benchmarks to trace DocVQA (doc QA), PubTables-1M (desk construction) for parsing-loss diagnostics. ViDoRe (web page retrieval), VisRAG (pipeline), VDocRAG (unified-image RAG).
Evaluation strategy IR metrics plus textual content QA; could miss figure-text grounding points Joint retrieval+gen on visually wealthy suites (e.g., OpenDocVQA beneath VDocRAG) to seize crop relevance and format grounding.
Operational sample One-stage retrieval; low-cost to scale Coarse-to-fine: textual content recall → imaginative and prescient rerank → ROI crops to generator; retains token prices bounded whereas preserving constancy. (Tiling math/pricing inform budgets.)
When to desire Contracts/templates, code/wikis, normalized tabular knowledge (CSV/Parquet) Real-world enterprise docs with heavy format/graphics; compliance workflows needing pixel-exact provenance (web page hash + crop coords).
Representative programs DPR/BM25 + cross-encoder rerank ColPali (ICLR’25) imaginative and prescient retriever; VisRAG pipeline; VDocRAG unified picture framework.

When Text-RAG continues to be the best default?

  • Clean, text-dominant corpora (contracts with mounted templates, wikis, code)
  • Strict latency/value constraints for brief solutions
  • Data already normalized (CSV/Parquet)—skip pixels and question the desk retailer

Evaluation: measure retrieval + era collectively

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 instances (irrelevant crops, figure-text mismatch) that text-only metrics miss.

Summary

Text-RAG stays environment friendly for clear, text-only knowledge. Vision-RAG is the sensible default for enterprise paperwork with format, tables, charts, stamps, scans, and multilingual typography. Teams that (1) align modalities, (2) ship selective high-fidelity visible proof, and (3) consider with multimodal benchmarks persistently get increased retrieval precision and higher downstream solutions—now backed by ColPali (ICLR 2025), VisRAG’s 25–39% E2E elevate, and VDocRAG’s unified image-format outcomes.

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