RAG-Anything

RAG-Anything — Overview

RAG-Anything is an end-to-end, all-in-one multimodal RAG framework designed to handle modern documents that interleave text, images, tables, equations and other heterogeneous content. Unlike text-only RAG systems, RAG-Anything provides specialized processors and an integrated pipeline to parse, analyze, index, and retrieve multimodal content while preserving document hierarchy and cross-modal relationships.

Core goals and features

• End-to-end multimodal pipeline: document ingestion → high-fidelity parsing (MinerU or Docling) → modality-aware analysis → multimodal knowledge-graph indexing → hybrid retrieval and RAG-style generation.
• Universal document support: PDFs, Office documents (DOCX/PPTX/XLSX), images (JPG/PNG/BMP/TIFF/GIF/WebP), and plain text formats.
• Specialized modality handlers: visual analyzers (VLM-enabled captioning/analysis), table interpreters, equation parsers with LaTeX output, and extensible plugin interfaces for new modalities.
• Multimodal Knowledge Graph: extracts entities across modalities, maps cross-modal relationships, and maintains hierarchical "belongs_to" chains to preserve context.
• Modality-aware retrieval: vector-graph fusion combining dense embeddings and graph traversal, with adaptive ranking that weights modalities according to query intent.
• VLM-Enhanced Query mode: when documents contain images, the framework can automatically include visual context in queries by sending images to a vision-language model for richer multimodal reasoning.

Architecture & Pipeline

RAG-Anything adopts a multi-stage architecture:

1. Document parsing: MinerU (recommended) or Docling for structure-aware extraction, OCR and table/formula detection.
2. Content classification & routing: autonomous pipelines route text, images, tables, formulas to appropriate processors.
3. Modality analysis: vision model for image semantics, structured interpreter for tables, math parser for formulas.
4. Knowledge graph construction: multimodal entities and cross-modal relationships are represented and scored for relevance.
5. Retrieval & RAG: hybrid search (vector similarity + graph traversal) returns coherent multimodal contexts for downstream LLM answering.

Integration & Extensibility

• Built on top of LightRAG and designed for easy integration with external LLMs and VLMs (example code shows OpenAI-style calls). Models can be configured to download automatically or be provided manually.
• Provides an EmbeddingFunc abstraction for pluggable embedding providers and supports large embedding dimensions and long context chunking.
• Plugin-style ModalProcessors let you add custom handlers (e.g., for new file formats or domain-specific analyses).

Quick start & deployment notes

• Installable via PyPI (pip install raganything) or from source. Optional extras (image/text/all) enable extended format support.
• Office parsing requires LibreOffice on the host. MinerU is used for parsing and must be installed/configured; examples include commands and checks to validate MinerU availability.
• The project provides examples for end-to-end processing, multimodal queries, batch processing, and direct insertion of pre-parsed content lists.

Typical use cases

• Research paper analysis: extract figures, tables and equations and query them together with text.
• Enterprise knowledge bases: ingest reports, manuals, and mixed-format documentation for unified multimodal retrieval.
• Technical documentation and compliance: correlate images/diagrams with textual descriptions and structured tables.

Citation & community

The repository links to a technical report on arXiv (arXiv:2510.12323). The authors provide citation details in the README. The project encourages contributions, provides examples, and includes badges for installation status, PyPI, and community channels (Discord, GitHub discussions).

Note: The repository metadata indicates it was created on 2025-06-06 and the README documents feature additions throughout 2025 (VLM features, context configuration module, multimodal query support, and a technical report released on arXiv in October 2025).