Vision-RAG vs Text-RAG: Which Retrieval Wins for Enterprise Documents?

September 25, 2025 · 4 min

Retrieval is the Real Failure Point

Most retrieval-augmented generation failures trace back to retrieval, not to the LLM. When PDFs are converted to plain text, layout, table structure, and figure grounding are often lost. That degradation harms recall and precision long before generation starts.

How Text-RAG Pipelines Break

Text-first pipelines follow PDF → parser/OCR → text chunks → text embeddings → ANN index → retrieve → LLM. Common failure modes include OCR noise, broken multi-column flow, loss of table cell structure, and missing figure or chart semantics. These issues are well documented by table and document VQA benchmarks that were created to expose exactly these gaps.

The Vision-RAG Alternative

Vision-RAG keeps the document as images or page renders: PDF → page raster(s) → vision-language model embeddings (often multi-vector with late-interaction scoring) → ANN index → retrieve → VLM/LLM consumes full pages or high-fidelity crops. This preserves layout, spatial relations, and figure-text grounding, directly addressing the primary bottleneck of text-first approaches.

Evidence and Benchmarks

Document-image retrieval is effective and simpler to train end-to-end. ColPali embeds page images and relies on late-interaction matching; on the ViDoRe benchmark it outperforms modern text-centric pipelines while remaining end-to-end trainable. VisRAG reports 25–39% end-to-end improvement over Text-RAG on multimodal documents when both retrieval and generation use a VLM. VDocRAG advocates a unified image format for real-world documents and introduces OpenDocVQA for evaluation. High-resolution VLMs such as the Qwen2-VL family are explicitly tied to SoTA results on DocVQA and other visual QA tasks, underscoring the role of fidelity for ticks, superscripts, stamps, and small fonts.

Costs and Token Accounting

Vision inputs frequently inflate token counts via tiling. For GPT-4o-class models, total tokens are approximately base + (tile_tokens × tiles), so 1–2 megapixel pages can be roughly 10× the cost of a small text chunk. Anthropic recommends caps near 1.15 MP for responsiveness. Even when providers price text and images at the same per-token rate, large images still consume many more tokens in practice. Engineering implication: selectively send high-fidelity regions rather than entire pages when possible.

Design Rules for Production Vision-RAG

Align modalities across embeddings. Use encoders trained for textimage alignment, typically CLIP-family or dedicated VLM retrievers. A dual-index strategy works well: cheap text recall for broad coverage and vision rerank for precision. ColPali’s late-interaction MaxSim-style matching is a strong default for page images.
Feed high-fidelity inputs selectively. Use a coarse-to-fine flow: run BM25 or DPR, take top-k pages into a vision reranker, then crop regions of interest such as tables, charts, and stamps and send only those to the generator. This preserves critical pixels without exploding token costs under tile-based accounting.
Engineer for real document artifacts. For tables, prefer table-structure models like PubTables-1M or TATR when parsing is required, otherwise lean on image-native retrieval. For charts and diagrams, ensure resolution retains ticks and legends. For rotated scans, whiteboards, and multilingual scripts, page rendering avoids many OCR failure modes. Always store provenance: page hashes and crop coordinates alongside embeddings to reproduce visual evidence used in answers.

When to Choose Text-RAG

Text-RAG still makes sense for clean, text-dominant corpora such as contracts with fixed templates, wikis, or codebases where latency and cost are strict constraints. If data is already normalized in CSV or Parquet, skip pixels and query the table store directly.

When to Choose Vision-RAG

Vision-RAG is the practical default for enterprise documents that are visually rich or structured: tables, charts, slides, stamps, rotated scans, and multilingual typography. Teams that align modalities, deliver selective high-fidelity visual evidence, and evaluate with multimodal benchmarks consistently obtain higher retrieval precision and better downstream answers. Recent systems that validate these gains include ColPali, VisRAG, and VDocRAG.

Evaluation and Benchmarks to Track

Track DocVQA, PubTables-1M, ViDoRe, VisRAG, and VDocRAG results. Use joint retrieval plus generation evaluation on visually rich suites such as OpenDocVQA to capture crop relevance and layout grounding. Add multimodal RAG benchmarks like M2RAG, REAL-MM-RAG, and RAG-Check to catch failure cases that text-only metrics miss.

Practical Summary

Text-RAG remains efficient for clean, text-only sources. Vision-RAG outperforms when documents contain layout, figures, and fine-grained visual cues. The right engineering pattern is to combine cheap text recall for coverage with vision rerank and selective high-fidelity crops for generation, while tracking multimodal benchmarks and storing pixel-level provenance.