RAG Frameworks · OpenBMB

VisRAG

VisRAG is an open-source Python framework that performs document retrieval and question-answering directly on document images using vision-language models (VLMs), avoiding text parsing. It includes EVisRAG (2.0), an end-to-end VLM trained with a novel reward-scoped optimization method, plus VisRAG-Ret, a document embedding model for retrieval.

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Source: GitHub — github.com/OpenBMB/VisRAG

970

GitHub stars

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	OpenBMB/VisRAG
Owner	OpenBMB
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	970
Forks	76
Open issues	0
Latest release	Unknown
Last updated	2025-12-07
Source	https://github.com/OpenBMB/VisRAG

What VisRAG is

VisRAG implements vision-language model (VLM)–based RAG by embedding documents as images rather than parsing to text, preserving layout and visual content. EVisRAG 2.0 employs Reward-Scoped Group Relative Policy Optimization (RS-GRPO) for fine-grained token-level rewards during VLM training; models are based on Qwen2.5-VL (7B, 3B) for generation and MiniCPM-V 2.0 + SigLIP for retrieval.

Quickstart

Get the VisRAG source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/OpenBMB/VisRAG.git2cd VisRAG3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-modal document understanding at scale

Organizations processing PDFs, forms, reports, or technical documents with complex layouts, tables, and figures where text extraction loses critical spatial or visual context. VisRAG preserves document fidelity without parsing overhead.

Enterprise knowledge retrieval with visual documents

Internal QA systems over large document repositories (contracts, technical specs, research papers) where visual understanding is needed. VisRAG-Ret provides VLM-based indexing; EVisRAG enables multi-image reasoning in responses.

Research and academic document analysis

Processing academic PDFs, technical reports, and multi-page datasets. Two arxiv papers referenced; benchmarks and training data published on HuggingFace indicate active research use and reproducibility focus.

Implementation considerations

Setup requires Python 3.10, CUDA 11.8, and conda; custom `timm_modified` library (gradient checkpointing) must be installed separately for training. Significant dependency footprint.
EVisRAG training is two-stage (SFT via LLaMA-Factory, then RS-GRPO via custom Easy-R1 variant). Requires separate repo clones and script orchestration; not a plug-and-play fine-tuning.
VisRAG-Ret training uses OpenMatch framework and 362K Q-D pairs split into in-domain and synthetic; datasets must be manually merged if full dataset used. Metadata.json with length field required for custom data.
Inference via HuggingFace models (EVisRAG-7B, EVisRAG-3B, VisRAG-Ret) or Colab/HF Spaces demo; production deployment path (API, containerization, scaling) not documented.
No release tags yet (latestRelease: none); all functionality on main branch. Evaluate stability and breaking changes by commit history; last push 2025-12-07 (current).

When to avoid it — and what to weigh

High-volume real-time inference with strict latency SLAs — EVisRAG and VisRAG-Ret inference costs (VLM-based embedding and reasoning) are higher than traditional text-based RAG. Multi-image reasoning in EVisRAG 2.0 adds per-image processing overhead.
Already have robust text-extraction pipelines — If you have mature, domain-specific OCR and text-parsing workflows with acceptable quality, adding a VLM-based layer may increase latency and cost without benefit. VisRAG's value is in avoiding parsing—not guaranteed in all scenarios.
Limited GPU resources or on-premise-only deployment — EVisRAG (7B/3B) and VisRAG-Ret require GPU memory. Setup instructions include CUDA 11.8; training requires DeepSpeed. Cloud or GPU-rich environments strongly assumed; on-premise CPU inference Unknown if feasible.
Regulated domains requiring explainability or audit trails — VLM-based reasoning (EVisRAG) is less interpretable than rule-based or structured extraction. No documented audit, traceability, or compliance features. Regulatory fit Unknown; requires review.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution under Apache terms.

Apache 2.0 is permissive and permits commercial use. However, review dependencies (LLaMA-Factory, Easy-R1, OpenMatch) for their own license constraints. Base VLM models (Qwen2.5-VL, MiniCPM-V) require separate license review with their respective owners. No indication of enterprise support, SLA, or commercial indemnity from OpenBMB.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

No explicit security documentation. Consider: (1) VLM-based systems may be vulnerable to adversarial image inputs; VisRAG processing of document images as-is introduces injection risk if documents are untrusted. (2) No encrypted model weights or API key management documented. (3) Training on synthetic data (66% of VisRAG-Ret) via GPT-4o; data provenance and filtering Unknown. (4) GPU memory and inference side-channels not addressed. Requires threat model review before production use.

Alternatives to consider

LlamaIndex / LangChain (text-based RAG)

Established, production-ready RAG frameworks with text parsing, multi-vector indexing, and broad VLM/LLM integrations. Lower inference cost; mature ecosystem. Trade-off: loses visual/layout information if documents are parsed.

Anthropic Claude's document understanding (API)

Closed-source, managed VLM service with native multi-document reasoning and visual comprehension. Higher latency/cost per inference; no training customization. Avoids GPU/infrastructure management.

Multimodal RAG frameworks (e.g., Vespa, Weaviate with VLM embeddings)

Vector DBs with multimodal embedding plugins. More flexible than VisRAG-Ret alone; better for heterogeneous document types. Requires separate VLM selection and training; less opinionated on end-to-end reasoning (EVisRAG equivalent).

Software development agency

Build on VisRAG with DEV.co software developers

VisRAG is suited for enterprises processing complex, visually-rich documents (PDFs, forms, technical specs) where layout and images matter. Assess GPU infrastructure, latency requirements, and integration with existing RAG pipelines before adopting. Start with pre-trained models on HuggingFace; prototype in Colab or HF Spaces.

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VisRAG FAQ

Can I use VisRAG with my own documents in production?

VisRAG Pipeline (Colab, HF Spaces, or local) supports PDF-to-image conversion and QA demos. Production deployment (scaling, API, monitoring) is not documented. GPU resources and model hosting (HF, local, or cloud) must be arranged separately. Evaluate performance and cost in your environment.

Do I need to train EVisRAG or VisRAG-Ret myself?

No. Pre-trained models are available on HuggingFace (EVisRAG-7B, EVisRAG-3B, VisRAG-Ret). Use them directly for inference. Training is optional if you want to fine-tune on domain-specific data or improve performance; requires significant compute (DeepSpeed, multi-GPU setup).

What is the difference between EVisRAG and VisRAG-Ret?

VisRAG-Ret is a retriever (document embedding model, based on MiniCPM-V 2.0). EVisRAG is an end-to-end generator (Qwen2.5-VL-based) that performs evidence-guided multi-image reasoning. Use VisRAG-Ret to index and retrieve documents; use EVisRAG to answer questions over retrieved images.

Is VisRAG compatible with OpenAI GPT-4o or other commercial VLMs?

EVisRAG is trained on Qwen2.5-VL (open-source). VisRAG-Gen (the generative component in papers) used MiniCPM-V, MiniCPM-V 2.6, and GPT-4o in experiments, but the released code uses open models. Custom generator integration is possible but not documented. Commercial VLM compatibility requires engineering effort.

Software development & web development with DEV.co

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