This project provides a retrieval-augmented question-answering application built on The Full Stack's educational materials, targeting users who want to query LLM course content. It offers a practical demonstration of RAG pipelines and a Discord bot interface.

How It Works

The application leverages Langchain for orchestrating LLM interactions and prompt management. It utilizes a FAISS index for efficient retrieval of relevant documents from a MongoDB Atlas corpus, which contains cleaned educational materials. The backend and Discord bot are hosted serverlessly on Modal, enabling scalable execution. Gantry is used for production monitoring and user feedback collection.

Quick Start & Requirements

• Install/Run: Follow the best-effort guide in the repository for self-hosting.
• Prerequisites: Requires cloud services (e.g., OpenAI API, MongoDB Atlas, Modal account). Be mindful of potential OpenAI API costs.
• Resources: Setup involves configuring cloud services and potentially running ETL for the corpus.
• Links: Discord Bot Frontend (implied by description), Running ETL to Build the Document Corpus

Highlighted Details

• Demonstrates a complete RAG pipeline using popular libraries.
• Serverless backend and Discord bot hosting via Modal.
• Utilizes FAISS for efficient semantic search over a curated corpus.
• Includes production monitoring with Gantry.

Maintenance & Community

The project is under rapid development, with community contributions noted. Further community engagement details (Discord/Slack links, roadmap) are not explicitly provided in the README.

Licensing & Compatibility

The repository's license is not specified in the README. Compatibility for commercial use or closed-source linking is therefore undetermined.

Limitations & Caveats

This project is experimental and under rapid development, meaning users should expect "sharp edges" during setup. The reliance on multiple cloud services, particularly OpenAI API, can incur costs if usage limits are not managed.