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Apim-Samples
PublicThis repository provides a playground to safely experiment with and learn Azure API Management (APIM) policies in various architectures.- End-to-end solution sample for a travel assistant built with the Azure Agent Runtime
retail-search-with-ai
Public- Sample App using Agent Framework for analysis of investment opportunities.
- Samples about using vector in SQL Server and Azure SQL
- A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.
- Automated pipeline for generating, verifying, and preparing high-quality function calling datasets for model fine-tuning. Reduces manual annotation through multi-stage validation and conversion to training formats. Built for Azure ML with permissive license models (Phi3.5 MoE, Phi4 etc).
- Basic sample for deploying chat web apps with Azure AI Foundry and SDKs
- A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
Cosmic-Food-RAG-app
Public- A demonstration of chatting with uploaded images using OpenAI vision models like gpt-4o.
- Sample for context-aware Agentic RaG, Q&A with multi-source verification, and self-curating knowledge base. Powered by Azure AI Foundry Agent Service, Azure AI Search with agentic retrieval and query rewrite, Semantic Kernel and LangGraph agents running in Azure Container Apps, and ready for Copilot Studio
- A simple chat application that integrates Microsoft Entra ID for user authentication. Designed for deployment on Azure Container Apps with the Azure Developer CLI.
pyrit-sample
Publicllm-agent-ops-toolkit-sk
PublicThe LLMAgentOps Toolkit is a repository that provides a foundational structure for building LLM Agent-based applications using the Semantic Kernel. It serves as a starting point for data scientists and developers, facilitating experimentation, evaluation, and deployment of LLM Agent-based applications to production.