RAGFlow
An open-source engine that makes RAG easy.
Overview
RAGFlow is an open-source Retrieval-Augmented Generation engine that is built around the principle of 'quality-in, quality-out'. It specializes in deep document understanding, capable of parsing complex documents like PDFs with tables and images to extract clean, structured information. This focus on high-quality data extraction aims to significantly improve the accuracy and relevance of RAG outputs.
✨ Key Features
- Open source
- Deep document understanding for complex formats (PDFs, tables)
- Automated RAG workflow from ingestion to generation
- Visual interface for managing documents and testing queries
- Template-based chunking for different text structures
- Grounded citations to reduce hallucinations
🎯 Key Differentiators
- Specialized in deep document understanding
- Optimized for parsing complex, visually structured documents
- Easy-to-use, all-in-one workflow
Unique Value: Improves the accuracy of RAG systems by focusing on high-quality information extraction from complex, real-world documents, ensuring the context provided to the LLM is clean and relevant.
🎯 Use Cases (4)
✅ Best For
- Parsing and querying annual financial reports
- Creating chatbots for technical manuals
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Simple RAG applications based on plain text data
🏆 Alternatives
While frameworks like LlamaIndex provide broad data connectors, RAGFlow specializes in the 'ingestion' part for complex files, potentially offering superior parsing quality for those specific formats.
💻 Platforms
✅ Offline Mode Available
🔌 Integrations
🛟 Support Options
- ✓ Live Chat
💰 Pricing
Free tier: Open source is free.
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