RAGFlow is a powerful open-source RAG (Retrieval-Augmented Generation) engine designed to streamline the development and deployment of AI agents. It combines deep document understanding with vector similarity search to ingest, preprocess, and index unstructured data from PDFs, web pages, and databases into custom knowledge bases. Developers can leverage its Python SDK or RESTful API to retrieve relevant context and generate accurate responses using any LLM model. RAGFlow supports building diverse agent workflows, such as chatbots, document summarizers, and Text2SQL generators, enabling automation of customer support, research, and reporting tasks. Its modular architecture and extension points allow seamless integration with existing pipelines, ensuring scalability and minimal hallucinations in AI-driven applications.

QueryX is a state-of-the-art Text2SQL platform that allows users to convert natural language queries into precise SQL commands. This enables users of all skill levels to extract valuable insights from complex databases effortlessly. The platform features built-in AI capabilities for improved accuracy and can adapt to various database types, ensuring compatibility and performance.