RAG Web UI is an open-source intelligent dialogue system built on retrieval-augmented generation technology, designed to enable users to create AI-powered question answering systems grounded in their own knowledge bases. It combines document retrieval with large language models to provide accurate, context-aware responses based on indexed data rather than generic model knowledge. The platform supports ingestion of multiple document formats, including PDFs, Word files, Markdown, and plain text, automatically processing and vectorizing them for efficient retrieval. It features a multi-turn conversational interface that maintains context across interactions, allowing users to engage in more natural and continuous dialogues with their data. The system is designed with a scalable architecture that separates frontend and backend components, enabling distributed deployment and efficient handling of large datasets. It also supports multiple large language model providers.
Features
- Document ingestion and automatic vectorization
- Retrieval-augmented question answering engine
- Multi-turn contextual chat interface
- Support for multiple LLM providers and local models
- Modular frontend backend architecture
- OpenAPI access for external integrations