RAG-Anything

1. Project Overview

RAG-Anything try to solve a key shortcoming of traditional RAG: real-life enterprise documents are usually not pure text, but a mixture of paragraphs, pictures, charts, tables, formulas and complex layouts. The traditional "extract text-> cut chunk -> vector retrieval" scheme will lose a lot of structural relations. RAG-Anything, on the other hand, splits documents into different modalities and organizes them using multimodal knowledge graphs and hybrid searches.

Official architecture diagram:

! RAG-Anything Framework

2. What does it mostly do?

3. Applicable Scenario

The best pre-sales entry is: "What is really difficult for customers is not the question and answer, but the reliable transformation of complex documents into searchable, traceable and referable knowledge structures."

4. Not quite the scene

5. Architecture and Workflow

A typical link of RAG-Anything can be summarized:

1. Document analysis: Use MinerU, Docling or PaddleOCR to disassemble PDF/Office/pictures into structured content.
2. Content classification: divide the content into text, image, table, equation, generic content and other types.
3. Modal processing: pictures go visual analysis, tables go structured interpretation, formulas retain LaTeX and semantic description.
4. Map construction: Extract entities and establish relationships between text and charts, chapters and elements, tables and indicators.
5. Hybrid retrieval: When querying, combine vector similarity and graph relationship to return a more complete context.
6. LLM generation: Generate answers with recall context, which can be combined with multimodal information.

The pre-sales highlight of this architecture is that it does not simply OCR images into text, but attempts to preserve "the relationship between elements". This point is critical for complex customer documentation.

How to use #6.

Installation:

pip install raganything
pip install 'raganything[all]'

Office documents require additional installation LibreOffice:

brew install --cask libreoffice

Source installation:

git clone https://github.com/HKUDS/RAG-Anything.git
cd RAG-Anything
uv sync
uv run python examples/raganything_example.py --help

Minimum use of ideas:

from raganything import RAGAnything, RAGAnythingConfig

config = RAGAnythingConfig(
    working_dir="./rag_storage",
    parser="mineru",
    parse_method="auto",
    enable_image_processing=True,
    enable_table_processing=True,
    enable_equation_processing=True,
)

The official example requires configuring the LLM, visual model, and embedding functions, and then calling process_document_complete or directly inserting content_list '. content_list is valuable for system integration, because enterprises can first use their own OCR/analysis services to get structured content, and then hand it over to RAG-Anything for multimodal RAG.

7. What can I say before sales

One-sentence positioning:

"RAG-Anything is a multimodal RAG framework for complex enterprise documents, which can integrate PDF, Office, pictures, tables and formulas into knowledge base retrieval and question-and-answer."

Value Mapping:

8. Demo/PoC Suggestions

It is recommended to use real customer documents instead of public demo documents. PoC is divided into three types of materials:

PoC indicators:

9. Frequently Asked Customer Questions

10. Risks and Considerations

1. Analytical quality determines the upper limit: complex tables, spread tables, scanned copies, and handwritten content will significantly affect the effect.
2. Multi-modal cost is high: VLM analysis of images, charts may bring additional costs and delays.
3. Engineering integration still requires investment: permissions, auditing, tenant isolation, incremental updates, and failure retries are not out-of-the-box enterprise systems.
4. Evaluation is critical: a standard problem set must be built for the customer's business, otherwise it is difficult to judge whether it is really better than ordinary RAG.
5. License friendly: MIT is friendly to business integration, but still needs to confirm the authorization of the model, parser and data to which it is connected.

11. My Pre-Sales Judgment

RAG-Anything is a direction worthy of long-term pre-sales attention in this batch of projects. It goes to the real pain point of the enterprise knowledge base: documents are not clean text, but a mixture of complex typography and multimodal information. Ordinary RAG is easy to "see tables and pictures" in such scenarios. The value of RAG-Anything lies in providing a more complete processing chain.

It is recommended to use it for the program explanation and PoC of "complex document intelligent question and answer", "multimodal knowledge base" and "R & D/financial/manufacturing document assistant. Don't just demonstrate plain text Q & A, that doesn't show the difference. What really impresses customers is to take a real document with charts, formulas and appendices and let the system answer questions that ordinary RAG cannot answer well.

12. REFERENCE

-GitHub: https://github.com/HKUDS/RAG-Anything

-Thesis: https://arxiv.org/abs/2510.12323

-PyPI: https://pypi.org/project/raganything/

-LightRAG: https://github.com/HKUDS/LightRAG

-MinerU: https://github.com/opendatalab/MinerU