HormoziAI

A RAG (Retrieval-Augmented Generation) system inspired by Alex Hormozi's AI launch, designed to provide intelligent responses based on business and entrepreneurship knowledge.

Overview

This project implements a sophisticated RAG system that combines the power of retrieval-based search with generative AI to deliver contextually relevant and accurate responses. The system is built to understand and respond to queries related to business strategy, entrepreneurship, and growth tactics.

RAG System Architecture

The system utilizes:

Document Processing: Advanced text chunking and preprocessing

Vector Embeddings: Semantic search capabilities for relevant context retrieval

Retrieval Engine: Efficient similarity search to find the most relevant information

Generation Layer: AI-powered response generation based on retrieved context

Context Management: Intelligent handling of conversation history and context windows

Features

Semantic search across business and entrepreneurship content

Context-aware response generation

Efficient document retrieval and ranking

Scalable vector database integration

Real-time query processing

Learning Opportunity

I hope this implementation serves as a valuable learning resource for those interested in:

Building RAG systems from scratch

Understanding vector embeddings and semantic search

Implementing retrieval-augmented generation

Creating domain-specific AI assistants

Feel free to explore the code, experiment with different approaches, and adapt it for your own projects!

Important Note

Copyright Notice: Unfortunately, I cannot include the source books or copyrighted materials in this repository due to copyright restrictions. The system is designed to work with your own content or properly licensed materials.

To use this system with your own content:

Replace the placeholder content with your own documents

Ensure you have proper rights to use any materials

Follow the setup instructions to process your documents

Getting Started

NOTE: You need your own copy of the books to run this code, otherwise you will get an error trying to read "$100M The Lost Chapters"

uv sync
uv run main.py

Remember to copy your secrets to the .env file

Contributing

Contributions, suggestions, and improvements are welcome! Please feel free to submit issues or pull requests.

License

MIT License

This project is for educational and learning purposes. Please respect copyright laws and use only content you have rights to use.