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

> Unified retrieval-augmented generation framework that handles documents, images, tables, and mixed-media content in a single pipeline.

## Install

Save in your project root:

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

## Quick Use
```bash
pip install rag-anything
from rag_anything import RAGAnything
rag = RAGAnything()
rag.ingest("./documents/")
answer = rag.query("Summarize the Q3 report")
```

## 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

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Source: https://tokrepo.com/en/workflows/asset-70802504
Author: AI Open Source