This repository provides a comprehensive, one-stop learning resource for developing applications with large language models (LLMs), targeting individuals interested in Generative AI. It offers theoretical foundations, development basics, and practical examples using LangChain and OpenAI's models, enabling rapid prototyping and understanding of LLM capabilities.

How It Works

The project delves into the internal workings of LLMs like BERT and GPT, covering their architectures and training. It then focuses on practical application development using OpenAI's APIs (GPT-3.5, GPT-4, Embeddings) and LangChain, demonstrating techniques such as function calling and building applications like AutoGPT and RAG chatbots. The approach emphasizes hands-on examples to demystify LLM development.

Quick Start & Requirements

• Install: Clone the repository and install dependencies via pip install -r requirements.txt.
• Prerequisites: Python v3.10, Miniconda for environment management, OpenAI API Key.
• Setup: Requires setting up a Python environment (Miniconda recommended) and configuring the OPENAI_API_KEY environment variable. Jupyter Lab is used for development.
• Docs: Course Outline

Highlighted Details

• Covers OpenAI's Embedding, GPT-3.5, and GPT-4 models.
• Provides practical examples for LangChain GenAI applications (AutoGPT, RAG-chatbot).
• Includes guides on LLM tech stack, data privacy, GPU selection, and Hugging Face.
• Demonstrates OpenAI function calling and ChatGPT plugin best practices.

Maintenance & Community

• The project is maintained by Django Peng. Contact: pjt73651@email.com.
• Contributions are welcome; open an issue to discuss changes.

Licensing & Compatibility

• Licensed under the Apache-2.0 license.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The setup instructions are primarily detailed for Ubuntu, requiring adaptation for other operating systems. While comprehensive, the project focuses on OpenAI and LangChain, with broader LLM ecosystem coverage being introductory.