This repository provides educational materials and code examples for learning Large Language Model Operations (LLM Ops). It targets Generative AI practitioners aiming to build, deploy, and operate production-ready LLM applications using frameworks like LangChain and LlamaIndex. The primary benefit is a structured, video-guided curriculum covering RAG systems, agents, fine-tuning, deployment, and observability.

How It Works

The course follows a modular approach, with each session focusing on specific LLM Ops concepts and tools. It demonstrates building end-to-end Retrieval Augmented Generation (RAG) systems, leveraging LangChain and LlamaIndex for data ingestion, querying, and agentic behavior. The curriculum emphasizes practical application, guiding users through deploying models like Llama 2 with FastAPI and integrating observability tools like Weights & Biases (WandB) and LangSmith.

Quick Start & Requirements

• Install/Run: Code examples are provided per session. Installation typically involves pip install for Python dependencies.
• Prerequisites: Python, LangChain, LlamaIndex, Chainlit, FastAPI, Hugging Face libraries, RAGAS, Eleuther AI Harness, WandB, LangSmith. Specific model weights (e.g., Llama 2) may be required.
• Resources: Access to YouTube videos for each session.
• Links: Session Videos, Code, and Slides (Note: The provided link is a placeholder; the actual link is embedded in the README table).

Highlighted Details

• Covers building production-grade RAG systems with Llama 2, FastAPI, and Chainlit.
• Includes sessions on evaluation using RAGAS and Eleuther AI Harness.
• Features observability tooling with Weights & Biases (WandB) and LangSmith.
• Demonstrates LangChain Agents and LlamaIndex Data Agents for advanced RAG.

Maintenance & Community

• The course was taught from August 15, 2023, to September 7, 2023.
• Users are encouraged to submit pull requests for code updates.

Licensing & Compatibility

• The repository's licensing is not explicitly stated in the provided README snippet. Compatibility for commercial use or closed-source linking would require clarification.

Limitations & Caveats

The course content and code are from late 2023 and may require updates for full functionality due to the rapid evolution of LLM technologies.