RAGFlow is an innovative, open-source Retrieval-Augmented Generation (RAG) engine that harnesses deep document understanding to provide reliable and insightful question-answering capabilities. By integrating with large language models (LLMs), RAGFlow offers an unmatched ability to extract relevant information from complex, unstructured data sources, ensuring responses are not only accurate but also backed by trusted citations.
Perfect for businesses and developers seeking to enhance their systems with intelligent question answering, RAGFlow eliminates common issues of hallucinations by leveraging grounded citations. Whether you’re dealing with large data sets or complicated formats, RAGFlow’s cutting-edge features make it easy to retrieve the most relevant answers with ease. It empowers users to streamline workflows and integrate seamlessly into existing systems with minimal effort.
Deep document understanding-based knowledge extraction that finds the ’needle in the haystack’ of massive, unstructured datasets. This feature guarantees high-quality, accurate results even from complex data.
Intelligent, explainable chunking process with multiple templates to choose from, making it easier to break down and understand large documents. This feature ensures flexibility and simplicity in data processing.
Provides citation-backed answers, reducing the occurrence of hallucinations. Users can easily track assets, their locations, and their status, with the ability to click through for detailed histories and updates.
A streamlined RAG orchestration system designed to cater to both personal and enterprise-level businesses. This feature includes configurable LLMs and embedding models, paired with multiple recall systems and fused re-ranking for optimal results.
Offers intuitive APIs for seamless integration with business processes, making it simple to integrate RAGFlow into your existing system for enhanced functionality.
Easily handles complex formatted data from diverse sources, ensuring your document understanding system can operate efficiently with any data format, no matter how intricate.
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