This repository provides practical tutorials and projects for leveraging LangChain and prompt engineering with large language models (LLMs) like ChatGPT and Llama 2. It targets developers and researchers looking to build AI applications that can interact with custom data, enabling functionalities such as custom chatbots, sentiment analysis, and querying private documents.

How It Works

The project demonstrates core LangChain concepts including data loading and indexing, prompt templating, and the creation of retrieval QA chains. It emphasizes practical application by showcasing how to integrate LLMs with custom datasets, build agents for complex tasks, and deploy models, including private LLMs like Llama 2, for specific use cases.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.8+, PyTorch, Hugging Face libraries, potentially CUDA for GPU acceleration. Specific model requirements vary per project.
• Resources: Projects involve downloading LLM weights (e.g., Llama 2, Falcon 7B), which can be several gigabytes. GPU with sufficient VRAM is recommended for efficient execution.
• Links: YouTube, Tutorials

Highlighted Details

• Demonstrates fine-tuning LLMs (Llama 2, Falcon 7B) on custom datasets using techniques like QLoRA.
• Features projects for building chatbots that can query multiple PDF files or custom knowledge bases.
• Includes examples of deploying LLMs to production environments using HuggingFace Inference Endpoints or RunPod.
• Showcases advanced agentic workflows with AutoGen for building powerful AI agents.

Maintenance & Community

The repository is maintained by curiousily. Community engagement can be found via their YouTube channel.

Licensing & Compatibility

The repository itself appears to be under an unspecified license, but the underlying libraries and models used (e.g., LangChain, Llama 2, Falcon) have their own licenses which may include restrictions on commercial use or redistribution. Users must verify compatibility with their intended application.

Limitations & Caveats

The project requires significant computational resources, particularly a GPU with ample VRAM, for training and running larger LLMs. Setup can be complex due to numerous dependencies and large model downloads. The rapid evolution of LangChain and LLM technologies means some code might require updates for compatibility with newer versions.