This repository is a curated list of resources for Azure OpenAI and Large Language Models (LLMs), covering topics like Retrieval-Augmented Generation (RAG), agent frameworks, prompt engineering, and LLM evaluation. It aims to provide a chronological overview and key terms for professionals and researchers working with these rapidly evolving technologies.

How It Works

The project organizes a vast array of links and memos into thematic sections, from RAG design patterns and Azure OpenAI services to agent frameworks like LangChain and Semantic Kernel. Each entry is briefly described, with dates reflecting initial publication or commit, providing a historical context for the field's advancements.

Quick Start & Requirements

This is a curated list, not a runnable project. No installation or specific requirements are needed beyond a web browser to access the linked resources.

Highlighted Details

• Comprehensive coverage of RAG, including design patterns and vector databases.
• Detailed sections on Azure OpenAI services, Microsoft's LLM frameworks, and Copilot products.
• In-depth exploration of agent design patterns, frameworks, and concepts like Model Context Protocol.
• Comparative analysis of orchestration frameworks like LangChain and LlamaIndex, alongside Semantic Kernel and DSPy.

Maintenance & Community

The repository is maintained by kimtth, with all rights reserved. Further community or contribution details are not specified in the README.

Licensing & Compatibility

The repository itself is not licensed for software use. It is a collection of curated links, and the licensing of the linked external resources would need to be checked individually.

Limitations & Caveats

The README explicitly states that some content may be outdated due to the rapid pace of development in the LLM field.