RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine designed to enhance the capabilities of Large Language Models (LLMs) by integrating deep document understanding. It enables the development of AI-driven applications that provide accurate, context-aware responses, supported by well-founded citations from complex, formatted data sources. By leveraging RAGFlow, businesses can build generative AI solutions tailored to their specific needs, ensuring reliable and informed outputs. Key Features and Functionality: - Deep Document Understanding: RAGFlow processes and comprehends complex document structures, facilitating accurate information retrieval. - Integration with LLMs: Seamlessly combines with various LLMs to enhance their performance with retrieval-augmented generation capabilities. - Cross-Language Support: Offers cross-language search functionality, improving search accuracy and user experience in multilingual environments. - Agent Mechanism: Features a no-code workflow editor and a graph-based task orchestration framework, allowing for the creation of complex AI workflows without extensive coding. - Enhanced Image Handling: Displays images directly within responses in chat and search modules, providing a more intuitive user experience. - Code Component: Supports Python and JavaScript scripts, enabling developers to handle complex tasks like dynamic data processing. Primary Value and Problem Solved: RAGFlow addresses the challenge of generating accurate and contextually relevant responses in AI applications by combining the generative power of LLMs with precise information retrieval from structured and unstructured data sources. This integration ensures that AI-driven solutions can provide users with reliable, well-cited information, reducing the risk of misinformation and enhancing trust in AI outputs. By offering a flexible and scalable platform, RAGFlow empowers businesses to build and deploy generative AI applications that meet their unique requirements, ultimately driving innovation and efficiency.