RAG-Anything — All-in-One Multimodal RAG Framework

Introduction

RAG-Anything is an open-source framework from HKUDS that unifies retrieval-augmented generation across all content types. Unlike text-only RAG systems, it natively handles PDFs with tables, images, charts, and mixed-media documents in a single pipeline.

What RAG-Anything Does

• Ingests documents containing text, tables, images, and charts
• Parses and indexes multimodal content with type-aware chunking
• Retrieves relevant chunks across content types for a given query
• Generates grounded answers using retrieved multimodal context
• Supports incremental document ingestion and index updates

Architecture Overview

RAG-Anything uses a modular pipeline: a document parser segments input into typed chunks (text, table, image), each processed by a specialized encoder. Chunks are indexed in a unified vector store with metadata tagging. At query time, a hybrid retriever fetches relevant chunks across types, and a multimodal LLM generates the answer with citations back to source content.

Self-Hosting & Configuration

• Install via pip with optional GPU acceleration for embedding models
• Supports local vector stores (FAISS, Chroma) and remote options
• LLM backend is configurable (OpenAI, Anthropic, local models)
• Document parsing uses built-in extractors for PDF, DOCX, and HTML
• Embedding model and chunk size are adjustable via configuration

Key Features

• Native multimodal support for text, tables, images, and charts
• Type-aware chunking that preserves table structure and image context
• Hybrid retrieval combining dense and sparse search
• Source citation with page numbers and bounding boxes
• Pluggable LLM and embedding model backends

Comparison with Similar Tools

• LlamaIndex — general RAG framework; RAG-Anything adds native multimodal handling
• LangChain — broad LLM toolkit; RAG-Anything is focused and optimized for RAG
• Haystack — pipeline-based RAG; RAG-Anything offers simpler multimodal ingestion
• Quivr — opinionated RAG app; RAG-Anything is a lower-level framework for custom pipelines

FAQ

Q: What document formats are supported? A: PDF, DOCX, HTML, Markdown, and plain text are supported out of the box.

Q: Can it handle scanned PDFs? A: Yes, it integrates OCR for scanned documents and extracts embedded images.

Q: What vector stores are compatible? A: FAISS, Chroma, Milvus, and Qdrant are supported as storage backends.

Q: Is GPU required? A: GPU accelerates embedding generation but is not required; CPU mode works for smaller document sets.

Sources

• https://github.com/HKUDS/RAG-Anything
• https://github.com/HKUDS/RAG-Anything/blob/main/README.md