Awesome GitHub RepositoriesGrounded Chat Interfaces

This project is a reference implementation and application template for Retrieval-Augmented Generation (RAG). It integrates Azure OpenAI with Azure AI Search to enable conversational chat interfaces that provide grounded responses based on private enterprise data. The system is distinguished by its multimodal AI interface, allowing it to process and reason over combined text, image, and PDF content. It employs a hybrid search architecture that combines vector and keyword retrieval with semantic reranking to prioritize the most relevant documents for prompt augmentation. The project covers a

Rags is an orchestration tool for building retrieval-augmented generation pipelines and managing conversational data interfaces. It serves as a system for creating these pipelines from local files and web pages using natural language instructions to query, retrieve, and summarize information from connected datasets. The project features a multimodal retrieval system that identifies and extracts information across different data types and modalities. It includes a vector search orchestrator to manage chunking strategies and search parameters, alongside a pipeline builder that translates conver