Get SH*T Done with Prompt Engineering and LangChain
"Get SH*T Done with Prompt Engineering and LangChain" is a comprehensive project aimed at guiding individuals through the process of building real-world AI applications using ChatGPT/GPT-4 and LangChain in Python. This project offers valuable tools and insights for both beginners and experienced developers interested in harnessing the power of language models to create functional AI applications.
Contents and Resources
The project provides a variety of resources, including video tutorials and in-depth articles, to assist users in getting started and advancing their skills in AI development:
Watch on YouTube
The project includes a series of YouTube videos designed to visually guide users through the process of using LangChain and tuning AI models:
- Getting Started with LangChain and Llama 2 in 15 Minutes: A quick introduction to using LangChain with the AI model Llama 2.
- Fine-tuning Llama 2 on Your Own Dataset: Guidance on customizing the Llama 2 model for specific datasets.
- Deploy LLM to Production on Single GPU: Instructions on deploying language models to production environments.
- Chat with Multiple PDFs using Llama 2 and LangChain: Methods for interacting with multiple PDF documents using AI.
- AutoGen - Build Powerful AI Agents with ChatGPT/GPT-4: A deep dive into creating AI agents with advanced models like GPT-4.
Tutorials
The tutorials section includes detailed guides on several important aspects of AI development:
-
LangChain:
- LangChain QuickStart with Llama 2 provides a streamlined guide to getting started with LangChain.
- Load Custom Data with Loaders explains how to input and manage data for AI applications.
- Add AI with Models explores integrating models into AI projects.
- Make LLMs Useful with Chains teaches how to enhance the functionality of language models using chains.
- Build Chatbots with Memory covers the creation of chatbots that can remember past interactions.
- Complex Tasks with Agents focuses on using agents for complex AI tasks.
-
Models:
- StableVicuna - Open Source RLHF LLM Chatbot explores a reinforcement learning-based chatbot model.
- OpenLLaMa offers insights into open-source reproductions of the LLaMA model.
- XGen-7B introduces techniques for long sequence modeling.
- Falcon 180B presents another model option for developers.
Projects
Under the projects section, practical implementations of AI concepts are explored:
- Fine-tuning Llama 2 on a Custom Dataset allows for tailored AI performance on unique datasets.
- Chat with Multiple PDFs using Llama 2 and LangChain demonstrates multi-document interaction via AI.
- Chatbot with Local LLM (Falcon 7B) and LangChain provides a guide to setting up local AI chatbots.
- Private GPT4All shows how to interact with PDF files using an open-source language model.
- CryptoGPT analyzes sentiment in the crypto Twitter space.
- Fine-tuning LLM (Falcon 7b) with QLoRA focuses on refining models using specific techniques.
- Deploy LLM to Production with HuggingFace Inference Endpoints and Deploy Your Private Llama 2 Model to Production with RunPod offer insights into taking models from development to deployment.
- AutoGen guide aids in building robust AI agents using ChatGPT/GPT-4.
This project stands as a comprehensive resource for those eager to explore AI development using powerful language models and tools. By providing a wealth of educational materials and project examples, it empowers developers to not only understand but also implement cutting-edge AI technologies.