# 🌟 GenerativeAI-Starter-Kit

🚀 **A comprehensive, beginner-friendly Generative AI development toolkit**

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Welcome to **GenerativeAI-Starter-Kit**! This repository provides everything you need to get started with Generative AI—from basic concepts to production-ready applications. Perfect for learning, rapid prototyping, and real-world deployment.

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## 🧠 What's Included

- **RAG (Retrieval-Augmented Generation)**: Build intelligent document Q&A systems
- **Multimodal Applications**: Work with text, images, and cross-modal tasks
- **Model Fine-tuning**: Adapt pre-trained models for specific domains
- **Production-Ready APIs**: FastAPI servers with full documentation

### 🛠️ Development Tools

- **One-Click Setup**: Automated environment configuration
- **Interactive Notebooks**: Step-by-step Jupyter tutorials
- **Configuration Management**: Easy YAML-based settings
- **Testing Framework**: Comprehensive test suites

### 📚 Learning Resources

- **Multi-language Docs**: Complete guides in English and Chinese
- **Progressive Tutorials**: From beginner to advanced
- **Best Practices**: Industry-standard approaches
- **Research Examples**: Latest techniques and methods

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## 📦 Installation

### ✅ From PyPI (Recommended)

```bash
pip install genai-starter-kit
```

```python
from genai_starter_kit import chains, utils

response = chains.run_rag_query("What is retrieval-augmented generation?")
print(response)
```

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### 🧪 From Source (Development Mode)

```bash
git clone https://github.com/YY-Nexus/GenerativeAI-Starter-Kit.git
cd GenerativeAI-Starter-Kit
pip install .
```

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## 🚀 Quick Start

### 1️⃣ Clone & Setup

```bash
git clone https://github.com/YY-Nexus/GenerativeAI-Starter-Kit.git
cd GenerativeAI-Starter-Kit
./automation/setup.sh
source venv/bin/activate
```

### 2️⃣ Try the Examples

```bash

# RAG System Demo

python examples/rag/simple_rag.py

# Multimodal Web App

python examples/multimodal/image_text_app.py --web

# Fine-tuning Demo

python examples/fine-tuning/text_classification_tuning.py

# Start API Server

python automation/api_server.py
```

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## 📚 Batch Run All Notebooks

```bash
pip install jupyter nbconvert
find RAG/notebooks -name "*.ipynb" -exec jupyter nbconvert --to notebook --execute --inplace {} \;
```

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## 🗂️ Directory Structure

docs/                         # Documentation and usage guides (with Chinese docs in docs-zh)
RAG/                          # Retrieval-Augmented Generation module
community/                    # Community contributions and experimental resources
examples/                     # Example scripts and demos
scripts/                      # Automation and lint/test/release scripts
tests/                        # Unit tests
automation/                   # Setup and API server scripts
setup.py                      # Build configuration

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## 🔧 Core Features

- End-to-end RAG examples (basic & advanced)
- Multimodal and industry-specific AI agents (text, speech, image, healthcare, finance, security)
- Model fine-tuning, training, evaluation, and safety (Llama, NeMo, Nemotron)
- Community resources, open-source contributions, and tutorials
- Comprehensive documentation (Chinese & English), one-click scripts, batch notebook execution

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## 💼 Typical Use Cases

- Intelligent Q&A, knowledge retrieval, document analysis
- Multimodal interaction (speech, image, text)
- Industry-specific agents (healthcare, finance, security)
- Large model fine-tuning and safety evaluation

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## ❓ FAQ & Help

- **Dependency install failed?** Check Python version or use a local mirror.
- **API service won't start?** Check port usage or run `python main.py --help` for options.
- **Notebooks won't batch run?** Ensure Jupyter and nbconvert are installed.

📖 See [`docs/README.md`](docs/README.md) or open a GitHub Issue for more help.

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## 🤝 Contributing & Feedback

- Pull Requests welcome for code, docs, or examples
- Report issues with clear steps and environment details
- All contributions must comply with the [LICENSE](LICENSE)

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## 📐 Standardization & Usability Commitment

- Unified script and doc formats with clear comments and step-by-step instructions
- Modular directory structure for easy navigation and extension
- Chinese and English documentation for global accessibility
- Continuous improvement—feedback is welcome!

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This project is committed to making generative AI development easy for everyone.

**Join our community and start building today!**

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