README.md

Azure OpenAI + LLM (Large Language Model)

This repository contains references to Azure OpenAI, Large Language Models (LLM), and related services and libraries.

🔹Brief each item on a few lines as possible.
🔹The dates are based on the first commit, article publication, or paper version 1 issuance.
🔹Capturing a chronicle and key terms of that rapidly advancing field.
🔹Disclaimer: Please be aware that some content may be outdated.

Table of contents

• Section 1 🎯: RAG
  • RAG (Retrieval-Augmented Generation)
  • RAG Design & Application
  • LlamaIndex
  • Vector DB
• Section 2 🌌: Azure OpenAI
  • Microsoft LLM Framework
  • Copilot Products & Azure OpenAI Service
  • Azure Reference Architecture
• Section 3 🌐: LLM Applications
  • LLM Frameworks & Applications
  • Caching, UX, Proposals & Other topics
  • LLMs for Robotics
  • Awesome demo
• Section 4 🤖: Agent
  • Agent Design Patterns
  • Agent Frameworks & Applications
• Section 5 🏗️: Semantic Kernel & DSPy
  • Semantic Kernel: Micro-orchestration
  • DSPy: Optimizer frameworks
• Section 6 🛠️: LangChain
  • LangChain Features: Macro & Micro-orchestration
  • LangChain Agent & Criticism
  • LangChain vs Competitors
• Section 7 🧠: Prompting | Finetuning
  • Prompt Engineering
  • Finetuning: PEFT (e.g., LoRA), RLHF, SFT
  • Quantization & Optimization
  • Other Techniques: e.g., MoE
  • Visual Prompting
• Section 8 🏄‍♂️: Challenges & Abilities
  • AGI Discussion & Social Impact
  • OpenAI Products & Roadmap
  • Context Constraints: e.g., RoPE
  • Trust & Safety
  • LLM Abilities
• Section 9 🌍: LLM Landscape
  • LLM Taxonomy
  • LLM Collection
  • Domain-Specific LLMs: e.g., Software development
  • Multimodal LLMs
  • Generative AI Landscape
• Section 10 📚: Surveys & References
  • LLM Surveys
  • Building LLMs: from scratch
  • LLMs for Korean & Japanese
• Section 11 🧰: AI Tools & Extensions
  • AI Tools & Extensions
• Section 12 📊: Datasets
  • LLM Training Datasets
• Section 13 📝: Evaluations
  • LLM Evaluation & LLMOps
• Legend 🔑:
  • ref: external URL
  • doc: archived doc
  • cite: the source of comments
  • cnt: number of citations
  • git: GitHub link
  • x-ref: Cross reference
  • 📺: youtube or video

Contributor 👀

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