This repository provides a production-ready, full-stack Retrieval-Augmented Generation (RAG) chat application built with Python FastAPI and React TypeScript. It demonstrates how to use Azure OpenAI (or OpenAI.com, or Ollama) to answer questions about data stored in Azure Database for PostgreSQL with the pgvector extension. The system supports both a simple RAG flow (embedding + direct search) and an advanced flow that uses OpenAI function calling to rewrite queries and apply column filters (brand, price). It features hybrid search combining full-text search and vector similarity via Reciprocal Rank Fusion. The backend is a monolith serving API endpoints and a React frontend, designed for deployment to Azure Container Apps using the Azure Developer CLI. The codebase includes comprehensive unit tests, end-to-end tests with Playwright, evaluation scripts, load testing with locust, and safety evaluation using Azure AI Red Teaming. The architecture is cleanly separated into reusable modules, making it a solid reference architecture for building RAG applications on Azure.
This repository implements a multi-agent orchestrator system for healthcare workflows, specifically designed for cancer care/tumor board scenarios. The system coordinates specialized AI agents (Patient History, Radiology, Patient Status, Clinical Guidelines, Report Creation, Clinical Trials, Medical Research) via Semantic Kernel group chat, with an Orchestrator agent facilitating collaboration. It provides both Microsoft Teams integration and a React/Vite web interface for user interaction. The architecture includes a Python/FastAPI backend handling WebSocket connections and Bot Framework messages, agent-specific tools for data access and external API integration, and an evaluation framework for assessing agent performance. Infrastructure is provisioned via Azure Bicep templates deploying AI services, storage, compute, and networking resources.