Gen-AI-Experiments provides a practical, hands-on collection of Jupyter notebooks for mastering Generative AI concepts like LLMs, AI Agents, and RAG. Targeted at beginners and experienced practitioners alike, it offers over 100 open-source library examples, enabling users to build real-world AI applications and enhance their portfolios.

How It Works

The repository's core approach centers on practical, runnable Jupyter notebooks that facilitate learning through direct experimentation. It curates essential AI libraries such as LangChain, Weaviate, and Hugging Face, demonstrating their application across diverse AI projects, from basic NLP tasks to complex multi-agent systems. This learn-by-doing methodology accelerates understanding and skill development in cutting-edge GenAI technologies.

Quick Start & Requirements

• Primary installation involves cloning the repository (git clone https://github.com/buildfastwithai/gen-ai-experiments.git).
• Navigate to a specific project directory (e.g., cd gen-ai-experiments/ai-apps-collection/your-desired-project).
• Install project-specific dependencies using pip install -r requirements.txt.
• Users must follow individual project READMEs for detailed setup and execution instructions.
• Prerequisites include Python and standard AI/ML libraries, with specific projects potentially requiring additional configurations or datasets as detailed in their respective READMEs.

Highlighted Details

• Features a broad spectrum of projects, categorized into Starter, Intermediate, and Featured AI Apps, covering topics from LangChain basics and Hugging Face Transformers to advanced AI Agents (AutoGen, CrewAI) and RAG implementations (Chat with PDF/Webpage).
• Showcases practical applications like AI Customer Support Agents, Travel Agent Streamlit Apps, and multimodal AI projects (Gemini 2.0).
• Integrates over 100 open-source libraries, including popular frameworks like LangChain, LlamaIndex, Hugging Face, and vector databases like ChromaDB and Pinecone.

Maintenance & Community

Contributions are actively encouraged via GitHub Issues and Pull Requests, with guidelines for adding new apps and ensuring project structure consistency. Specific community channels (Discord, Slack) or roadmap links are not provided in the README.

Licensing & Compatibility

The provided README does not specify a software license. This absence creates uncertainty regarding usage rights, modification permissions, and commercial compatibility.

Limitations & Caveats

The repository's primary limitation is the lack of explicit licensing information, which may hinder adoption for commercial or sensitive projects. Users must rely on individual project READMEs for specific setup and execution details, potentially leading to varied user experiences across different notebooks.