Pixel-Native RAG: A Practical Guide to Visual Document Indexing

This article provides a practical guide to Pixel-Native RAG, a new method for document indexing. The system handles documents as images, taking screenshots of web pages and PDFs. It then processes these pictures through a tiling and embedding pipeline to create a searchable visual index.

Key takeaways

• The system builds a RAG pipeline that treats documents as visual screenshots, avoiding traditional text extraction.
• Document images are divided into overlapping tiles for focused processing and indexed using multimodal embeddings (e.g., SigLIP, CLIP).
• Hybrid retrieval combines dense vector similarity with optional OCR-based BM25 scoring, fused via Reciprocal Rank Fusion.
• Retrieved visual evidence can optionally be passed to a Vision-Language Model for grounded answer generation.
• The complete pipeline is deployed as a configurable FastAPI service, supporting domain adaptation and comprehensive evaluation metrics.

Building the Visual Document Pipeline

This retrieval-augmented generation (RAG) pipeline handles documents as images, skipping conventional HTML parsing and text extraction. It captures web pages and PDFs as visual screenshots instead.

For rendering, the pipeline employs Playwright for web pages and can also handle synthetic PDFs. You can adjust key rendering parameters, like the device scale factor and a maximum page height, which defaults to 24,000 pixels.

Tile-Based Processing

Each rendered document image gets split into smaller, overlapping tiles. The default setup makes tiles of 1024 by 1024 pixels with a 128-pixel overlap between them. This tiling method lets the system concentrate on specific document areas.

The pipeline manages data volume with a configurable limit on tiles per document. A deduplication step filters out nearly identical tiles, which guarantees efficient processing and storage.

Generating and Indexing Multimodal Embeddings

The system turns tile images into vector embeddings using multimodal models. Primary backends are SigLIP or CLIP, with Qwen3-VL available as an extra option.

These created embeddings go into a FAISS vector index for fast similarity search during retrieval. The FAISS index uses adjustable IVF (Inverted File) parameters to handle bigger datasets, with settings for an IVF threshold and probe count (nprobe) to trade speed for accuracy.

Hybrid Retrieval Enhancement

Retrieval gets a boost with optional Optical Character Recognition (OCR) on image tiles to pull out text. This text then powers sparse retrieval using BM25 scoring. Dense similarity scores from the FAISS index and sparse BM25 scores merge into one ranked list via Reciprocal Rank Fusion (RRF).

Retrieval, Evaluation, and Optional Answer Generation

For any query, the system fetches the top-k most similar image tiles from its FAISS index. It aggregates this tile-level evidence at the document level and returns the top-n documents as the final result.

Evaluating Retrieval Quality

Retrieval quality is assessed with a predefined set of test queries. Performance metrics like Recall@k and Mean Reciprocal Rank (MRR) are applied to this evaluation set.

Optional Grounded Answer Generation

You can optionally send the strongest evidence tiles to a vision-language model (VLM) for grounded answer generation. The setup supports models like Qwen2.5-VL for this job, letting the system produce answers directly from visual proof.

System Deployment and Customization

The full pipeline becomes a search service through a FastAPI server, enabled and managed by the system’s central settings.

Configuration and Dependencies

The system has full configuration for dependencies and model backends. A setup script installs needed packages, including FAISS for the vector index, Transformers for embedding models, FastAPI and Uvicorn for the server, and Rank-BM25 for hybrid retrieval. It also manages the optional installation of Tesseract OCR for text-based scoring. The configuration lets you pick the multimodal embedding backend, choosing between SigLIP, CLIP, or an optional Qwen3-VL model.

Domain Adaptation and Hybrid Retrieval

For domain adaptation, the system allows training a lightweight residual adapter with contrastive learning. A configuration flag controls this feature. The retrieval pipeline gains strength from hybrid retrieval, merging dense vector similarity with OCR-based BM25 scoring before using reciprocal rank fusion to combine results.

The system also offers visualization of retrieved screenshots, with a configurable option to enable plots. A single, detailed configuration dataclass manages all these parts—the server, adapter training, hybrid retrieval, and visualization—specifying parameters for every operational detail.