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RAGs is an open-source application designed to simplify the creation of retrieval-augmented generation pipelines through an interactive interface. Built with Streamlit and powered by the LlamaIndex ecosystem, the tool allows users to construct AI assistants that answer questions using their own data sources. Instead of requiring extensive programming knowledge, the application allows users to configure and build a RAG system using natural language instructions. The system automatically generates pipeline configurations that control how documents are retrieved, processed, and summarized before being used by a language model to generate responses. Users can also inspect and adjust parameters such as the number of retrieved documents, summarization strategies, and query settings through a configuration interface. Once the pipeline is created, the system enables conversational queries over the connected data sources, effectively creating a personalized knowledge assistant.
Features
- Interactive Streamlit interface for building retrieval-augmented generation pipelines
- Natural language configuration of RAG workflows and parameters
- Integration with LlamaIndex for document retrieval and processing
- Adjustable retrieval parameters such as top-k document selection
- Conversational querying over user-provided data sources
- Visualization tools for inspecting and modifying pipeline configurations
Project Samples
