Links
- Login: https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Frag-web-ui%2Frag-web-ui
- Register: https://github.com/signup?ref_cta=Sign+up&ref_loc=header+logged+out&ref_page=%2F%3Cuser-name%3E%2F%3Crepo-name%3E&source=header-repo&source_repo=rag-web-ui%2Frag-web-ui
- Documentation: https://github.com/rag-web-ui/rag-web-ui#readme-ov-file
RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology that helps build intelligent Q&A systems based on your own knowledge base. By combining document retrieval and large language models, it achieves accurate and reliable knowledge-based question answering services. The system supports multiple LLM deployment options, including cloud services like OpenAI and DeepSeek, as well as local model deployment through Ollama, meeting privacy and cost requirements in different scenarios. It also provides OpenAPI interfaces for convenient knowledge base access via API calls.
Top Features
- Intelligent Document Management
- Advanced Dialogue Engine
- Robust Architecture
- Multiple LLM Deployment Options
- OpenAPI Interfaces
Simple Definition of Usecases
- Developers use the system to integrate intelligent Q&A capabilities into their applications, leveraging the OpenAPI interfaces for seamless integration.
- Data Scientists utilize the system for advanced data analysis, using the RAG technology to retrieve and generate insights from large datasets.
- AI Researchers employ the system to experiment with different LLM configurations and document management techniques, enhancing their research outcomes.
- Enterprise IT Teams deploy the system to manage internal knowledge bases, ensuring accurate and reliable information retrieval for employees.
- Educational Institutions implement the system to create interactive learning environments, where students can ask questions and receive detailed answers based on course materials.
User Reviews
Aarav Patel
Lead Developer
"RAG Web UI has been a game-changer for our development team. The ability to integrate intelligent Q&A capabilities into our applications using the OpenAPI interfaces has significantly enhanced our product offerings. The system's support for multiple LLM deployment options allows us to choose the best model for our needs, ensuring both cost-efficiency and performance. The documentation is thorough and easy to follow, making the setup process straightforward. We highly recommend RAG Web UI to any team looking to leverage AI for knowledge management."
Aarav Patel
Lead Developer
"RAG Web UI has been a game-changer for our development team. The ability to integrate intelligent Q&A capabilities into our applications using the OpenAPI interfaces has significantly enhanced our product offerings. The system's support for multiple LLM deployment options allows us to choose the best model for our needs, ensuring both cost-efficiency and performance. The documentation is thorough and easy to follow, making the setup process straightforward. We highly recommend RAG Web UI to any team looking to leverage AI for knowledge management."
Sofia Martinez
Data Scientist
"As a data scientist, I find RAG Web UI to be an invaluable tool for analyzing large datasets. The RAG technology enables precise retrieval and generation of insights, which has greatly improved the accuracy of our analyses. The system's robust architecture ensures reliable performance, even when processing extensive documents. One area for improvement would be the user interface, which could be more intuitive for non-technical users. Overall, RAG Web UI is a powerful solution for data-driven organizations."
Elena Ivanova
AI Researcher
"RAG Web UI has transformed our research capabilities. The flexibility to experiment with different LLM configurations and document management techniques has allowed us to achieve groundbreaking results in our AI research. The system's incremental document processing and async chunking features ensure efficient handling of large volumes of data. The community support and active development roadmap are also commendable. We look forward to future updates and enhancements."
Carlos Mendez
IT Manager
"Our IT team has successfully deployed RAG Web UI to manage our internal knowledge base. The system's ability to provide accurate and reliable information retrieval has greatly improved our employees' productivity. The setup process was smooth, and the documentation was helpful. However, we encountered some minor issues with the initial configuration, which were quickly resolved with the help of the support team. We are satisfied with the system's performance and look forward to utilizing its full potential."
Mei Ling
Educator
"RAG Web UI has been an excellent addition to our educational institution. The system's interactive learning environment allows students to ask questions and receive detailed answers based on course materials, enhancing their understanding and engagement. The support for multiple document formats and the ability to manage large knowledge bases make it a versatile tool for educators. The user interface is user-friendly, and the system's performance is reliable. We highly recommend RAG Web UI to other educational institutions."
Frequently Asked Questions
What is RAG Web UI?
RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology that helps build intelligent Q&A systems based on your own knowledge base.
What does RAG Web UI support?
RAG Web UI supports multiple LLM deployment options, including cloud services like OpenAI and DeepSeek, as well as local model deployment through Ollama.
What to expect from RAG Web UI?
You can expect accurate and reliable knowledge-based question answering services, intelligent document management, and robust architecture from RAG Web UI.
How to deploy RAG Web UI?
To deploy RAG Web UI, you need Docker & Docker Compose v2.0+, Node.js 18+, Python 3.9+, and 8GB+ RAM. Clone the repository, configure environment variables, and start the services using Docker Compose.
How to fast start with RAG Web UI?
For a fast start, clone the repository, configure environment variables, and start the services using Docker Compose. Access the frontend UI, API documentation, and MinIO console after service startup.
What are the core features of RAG Web UI?
The core features of RAG Web UI include intelligent document management, advanced dialogue engine, robust architecture, multiple LLM deployment options, and OpenAPI interfaces.
What are the prerequisites for RAG Web UI?
The prerequisites for RAG Web UI include Docker & Docker Compose v2.0+, Node.js 18+, Python 3.9+, and 8GB+ RAM.
What is the architecture of RAG Web UI?
The architecture of RAG Web UI includes a backend stack with Python FastAPI, MySQL + ChromaDB, MinIO, Langchain, and JWT + OAuth2, and a frontend stack with Next.js 14, TypeScript, Tailwind CSS, Shadcn/UI, and Vercel AI SDK.
What are the performance optimizations in RAG Web UI?
The performance optimizations in RAG Web UI include incremental document processing, async chunking, streaming responses, real-time feedback, vector database performance tuning, and distributed task processing.
What is the licensing of RAG Web UI?
RAG Web UI is licensed under the Apache-2.0 License.
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