100x Applied AI - Code Snippets

This repository contains code snippets and examples from the 100x Applied AI cohort lectures.

Project Overview

The repository includes implementations of:

• LLM Workflows and Patterns
• RAG (Retrieval Augmented Generation)
• Agentic Patterns
• Chat Completions with various providers
• Function Calling
• And more...

Installation Guide

Prerequisites

• Python 3.8+
• pip

Setup

1. Clone the repository:

   git clone <repository-url>

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Environment setup:
   • Copy .env_example to .env
   • Add your API keys and configurations

Repository Structure

Core Components

LLM Workflows (llm_workflows/)

• Prompt Chaining and Orchestration
• Router-based Workflows
• Parallel Processing Patterns
• Code Review Automation
• Evaluation and Optimization

RAG Implementations (rag/ & rag2/)

• Different approaches to Retrieval Augmented Generation
• Integration examples

Agentic Patterns (agentic_patterns/)

• Implementation of various AI agent patterns
• Agent orchestration examples

Chat Completions (chat_completions/)

• OpenAI integration
• Groq implementation
• Other LLM providers

Function Calling (function_calling/)

• Examples of function calling with LLMs
• Real-world use cases

Hugging Face Integration (huggingface/)

• Model usage examples
• Inference API implementations

Additional Components

• agents/: Various agent implementations
• presentation_generator/: Automated presentation creation
• notion_data_integration/: Notion API integration examples
• api/: FastAPI-based endpoints
• auth/: Authentication implementations
• langchain/: LangChain usage examples

Usage

Each directory contains specific examples and implementations. Refer to individual README files within each directory for detailed usage instructions.

Additional Resources

• Check the prompts/ directory for various prompt engineering examples
• See llm_workflows/README.md for detailed workflow patterns
• Explore individual directories for specific implementation details

Environment Variables

Required environment variables (add to .env):

• OpenAI API keys
• Hugging Face API tokens
• Other provider credentials as needed

Contributing

Feel free to contribute by:

1. Forking the repository
2. Creating a feature branch
3. Submitting a pull request

License

This project is licensed under the terms specified in the LICENSE file.