RAG-Anything
ActiveDescription
All-in-one RAG framework supporting text, images, tables, equations and more document formats for retrieval-augmented generation with unified knowledge QA.
Key Features
- End-to-end multimodal pipeline — Complete workflow from document ingestion to intelligent multimodal query answering
- Universal document support — Seamless processing of PDFs, Office documents, images and diverse file formats
- Specialized content analysis — Dedicated processors for images, tables, mathematical equations and heterogeneous content
- Multimodal knowledge graph — Automatic entity extraction and cross-modal relationship discovery
- Adaptive processing modes — Flexible MinerU-based parsing or direct multimodal content injection
- Hybrid intelligent retrieval — Advanced search across textual and multimodal content with contextual understanding
Use Cases
💡 Academic paper Q&A — simultaneously understanding text, figures and equations
💡 Technical document retrieval — processing mixed-format docs with tables and images
💡 Financial report analysis — extracting tabular data alongside textual descriptions
💡 Enterprise knowledge management — unified retrieval across multi-format internal documents
💡 Multimodal research analysis — cross-document association of images and text
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (22.9k stars)
- • Permissive open-source license (MIT)
- • Responsive to issues, low backlog
Categories
Quick Start
```bash
# Install
pip install raganything
# Using uv (recommended)
uv pip install raganything
# Basic usage
from raganything import RAGAnything
rag = RAGAnything()
rag.ingest_document("your_document.pdf")
result = rag.query("What experimental results are mentioned in the document?")
```