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

Understanding the Target Audience

The target audience for this comparison includes enterprise decision-makers, data scientists, and AI practitioners focused on enhancing document retrieval systems. Their pain points often revolve around inefficiencies in current retrieval methods, particularly in handling visually rich documents. They seek solutions that improve accuracy and reduce operational costs while maintaining high-quality outputs. Their interests lie in the latest advancements in AI technologies, particularly those that enhance enterprise search capabilities. Communication preferences lean towards technical documentation, case studies, and peer-reviewed research that provide clear, actionable insights.

Pipelines and Where They Fail

Text-RAG

Text-RAG follows this pipeline: PDF → (parser/OCR) → text chunks → text embeddings → ANN index → retrieve → LLM. Typical failure modes include:

OCR noise
Multi-column flow breakage
Table cell structure loss
Missing figure/chart semantics

These issues are documented by benchmarks created to measure these gaps.

Vision-RAG

Vision-RAG utilizes the following pipeline: 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 approach preserves layout and figure-text grounding, with recent systems like ColPali, VisRAG, and VDocRAG validating its effectiveness.

Current Evidence Supporting Vision-RAG

Document-image retrieval has proven effective and simpler. For instance, ColPali embeds page images and employs late-interaction matching, outperforming modern text pipelines on the ViDoRe benchmark while remaining end-to-end trainable.

VisRAG reports a 25–39% end-to-end improvement over Text-RAG on multimodal documents when both retrieval and generation utilize a VLM.

VDocRAG demonstrates that maintaining documents in a unified image format avoids parser loss and enhances generalization, introducing OpenDocVQA for evaluation.

High-resolution support in VLMs, such as Qwen2-VL/Qwen2.5-VL, is explicitly linked to state-of-the-art results on benchmarks like DocVQA, MathVista, and MTVQA, emphasizing the importance of fidelity for small details.

Cost Considerations

Vision context is often order-of-magnitude heavier due to token inflation from tiling. For GPT-4o-class models, total tokens are calculated as follows: total tokens ≈ base + (tile_tokens × tiles). This means that 1–2 MP pages can be approximately 10× the cost of a small text chunk. Anthropic recommends a cap of ~1.15 MP (~1.6k tokens) for responsiveness. In contrast, Google Gemini 2.5 Flash-Lite prices text/image/video at the same per-token rate, but large images still consume significantly more tokens. Therefore, selective fidelity (crop > downsample > full page) is advised.

Design Rules for Production Vision-RAG

Align modalities across embeddings using encoders trained for text-image alignment (e.g., CLIP-family or VLM retrievers).
Feed high-fidelity inputs selectively, employing a coarse-to-fine approach: run BM25/DPR, take top-k pages to a vision reranker, then send only ROI crops (tables, charts, stamps) to the generator.
Engineer for real documents, ensuring that tables are parsed using table-structure models (e.g., PubTables-1M/TATR) or prefer image-native retrieval.
For charts and diagrams, ensure resolution retains tick- and legend-level cues, evaluating on chart-focused VQA sets.
Utilize page rendering to avoid many OCR failure modes, accommodating multilingual scripts and rotated scans.
Store page hashes and crop coordinates alongside embeddings to reproduce the exact visual evidence used in answers.

Comparison Summary

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.	Preserves layout/figures; failures shift to resolution/tiling choices and cross-modal alignment.
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).
Cost model (inputs)	Tokens ≈ characters; cheap retrieval contexts.	Image tokens grow with tiling; high-res pages consume far more tokens.
Cross-modal alignment need	Not required.	Critical: text-image encoders must share geometry for mixed queries.
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 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.
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.
Representative systems	DPR/BM25 + cross-encoder rerank.	ColPali (ICLR’25) vision retriever; VisRAG pipeline; VDocRAG unified image framework.

Conclusion

Text-RAG remains efficient for clean, text-only data. Vision-RAG is the preferred choice for enterprise documents with complex layouts, tables, charts, and multilingual typography. Teams that align modalities, deliver selective high-fidelity visual evidence, and evaluate with multimodal benchmarks consistently achieve higher retrieval precision and improved downstream answers.

References

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