Ragflow
RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine developed by InfiniFlow, designed to provide a high-quality context layer for large language models. It combines deep document understanding with RAG and agent capabilities, enabling users to build reliable AI applications that ground answers in authoritative sources. The engine excels at parsing complex documents, including tables, images, and multi-column layouts, using deep learning models to extract structured information. Unlike simple chunking approaches, RAGFlow employs template-based chunking and intelligent document comprehension to preserve context and improve retrieval accuracy. It supports a wide range of data sources such as PDFs, Word documents, web pages, and databases, and integrates with numerous LLMs and embedding models. The platform offers an intuitive web UI for managing knowledge bases, testing chats, and configuring pipelines, making it accessible to both technical and non-technical users. RAGFlow also includes agent capabilities, allowing developers to create autonomous agents that can plan, execute, and use tools to accomplish complex tasks. With features like grounded citations, multimodal support, and a flexible API, RAGFlow serves as a foundation for building production-grade RAG applications.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
RAGFlow addresses the challenge of building accurate and trustworthy RAG applications by providing advanced document parsing, semantic chunking, and grounded generation. It reduces hallucinations and improves answer quality by ensuring that responses are based on relevant document context, making it easier to deploy LLM-powered assistants over enterprise knowledge.
Use cases
- Build a question-answering chatbot over internal company documents
- Create a knowledge base assistant with citation-backed answers
- Develop AI agents that retrieve and act on data from multiple sources
- Automate extraction of insights from complex PDFs and tables
- Deploy a self-hosted RAG system for data privacy and compliance
- Integrate RAG capabilities into existing applications via API
Audience
- Developers
- ML Engineers
- Data Scientists
- Enterprise
Pros
- Advanced document understanding with layout and table parsing
- Template-based chunking improves retrieval relevance
- Grounded citations reduce hallucinations
- Supports multiple LLMs and embedding models
- User-friendly web interface for non-developers
Cons
- Requires significant computational resources (GPU recommended)
- Complex setup for production environments
- Steep learning curve for advanced features
- Agent capabilities are still evolving
- Documentation can be overwhelming for beginners
Official website: https://github.com/infiniflow/ragflow
Canonical LambdaBase page: https://www.lambdabase.com/tools/ragflow