# ibmdotcom-tutorials

**Repository Path**: mirrors_ibm/ibmdotcom-tutorials

## Basic Information

- **Project Name**: ibmdotcom-tutorials
- **Description**: Repo for the storing of code and examples from tutorials.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-14
- **Last Updated**: 2026-04-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# IBM Tutorials Repository

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)

Welcome to the IBM Tutorials repository - your comprehensive resource for learning cutting-edge AI, machine learning, and generative AI technologies through hands-on tutorials and projects.

## 🎯 What You'll Find Here

This repository contains **60+ tutorials** organized by learning intent, covering:

- **Retrieval-Augmented Generation (RAG)** - Build intelligent document Q&A systems
- **AI Agents & Orchestration** - Create autonomous agents with LangChain, LangGraph, and more
- **Multi-Agent Systems** - Implement collaborative AI systems with CrewAI, BeeAI, and AutoGen
- **Prompt Engineering** - Master the art of effective LLM communication
- **Multimodal AI** - Work with vision, speech, and multimodal models
- **Tool & Function Calling** - Extend LLM capabilities with external tools
- **Guardrails & Safety** - Build responsible AI systems with security and safety mechanisms
- **Time Series & Forecasting** - Apply AI to temporal data
- **Text Processing & NLP** - Classic and modern NLP techniques
- **Full-Stack AI Applications** - Complete end-to-end AI projects
- **Observability & Monitoring** - Track and optimize AI system performance
- **IBM Bob** - Master the AI-powered coding assistant for documentation and automation

## 🚀 Quick Start

### Prerequisites

- **Python 3.10 - 3.13** (Python 3.11 recommended)
- **IBM watsonx.ai account** (for most tutorials) - [Sign up here](https://www.ibm.com/watsonx)
- **Git** for cloning the repository

### Installation

1. **Clone the repository:**
   ```bash
   git clone https://github.com/IBM/ibmdotcom-tutorials.git
   cd ibmdotcom-tutorials
   ```

2. **Navigate to a tutorial:**
   ```bash
   cd tutorials/01-rag-and-retrieval  # or any other category
   ```

3. **Follow the tutorial's setup instructions:**
   - Each tutorial includes its own setup and dependency installation instructions
   - Most tutorials require IBM watsonx.ai credentials
   - See individual tutorial READMEs for specific requirements

4. **Start learning:**
   - Open any `.ipynb` file in your IDE (VS Code, PyCharm, etc.)
   - Follow the tutorial's step-by-step instructions

## 📚 Tutorial Categories

### [01 - RAG and Retrieval](tutorials/01-rag-and-retrieval/)
Build intelligent systems that answer questions from your documents using vector search and embeddings.

**Featured Tutorials:**
- [LangChain RAG](tutorials/01-rag-and-retrieval/langchain-rag.ipynb) - Your first RAG system
- [Agentic RAG](tutorials/01-rag-and-retrieval/agentic-rag.ipynb) - RAG with reasoning capabilities
- [RAG Evaluation with Ragas](tutorials/01-rag-and-retrieval/rag-evaluation-ragas.ipynb) - Measure performance

### [02 - Agents & Orchestration](tutorials/02-agents-and-orchestration/)
Create autonomous AI agents that can plan, reason, and execute complex tasks.

**Featured Tutorials:**
- [LLM Agent Orchestration](tutorials/02-agents-and-orchestration/llm-agent-orchestration.ipynb)
- [Building Agentic Workflows with LangGraph](tutorials/02-agents-and-orchestration/building-agentic-workflow-langgraph.ipynb)
- [Text Classification Agent with watsonx Orchestrate](tutorials/02-agents-and-orchestration/wxo-text-classification/) - Build sentiment analysis agents
- [SQL Agent Application](tutorials/02-agents-and-orchestration/sql-agent-app/) - Full-stack agent app

### [03 - Multi-Agent Systems](tutorials/03-multi-agent-systems/)
Implement collaborative AI systems where multiple agents work together.

**Featured Projects:**
- [CrewAI Retail Example](tutorials/03-multi-agent-systems/crewAI-multiagent-retail-example.md)
- [BeeAI Agent-to-Agent Tutorial](tutorials/03-multi-agent-systems/a2a_tutorial/)
- [Customer Service Call Analysis](tutorials/03-multi-agent-systems/multiagent-collab-cs-call-center-analysis/)
- [ChatDev with watsonx](tutorials/03-multi-agent-systems/chatdev_watsonx_tutorial_/)

### [04 - Prompt Engineering](tutorials/04-prompt-engineering/)
Master techniques for effective LLM communication and optimization.

### [05 - Multimodal AI](tutorials/05-multimodal-ai/)
Work with vision, speech, and multimodal models for diverse AI applications.

**Featured Tutorials:**
- [Granite Vision PPT Analyzer](tutorials/05-multimodal-ai/ppt-ai-analyzer-granite-vision.ipynb)
- [AI Personal Trainer with Llama](tutorials/05-multimodal-ai/ai-personal-trainer-llama/)
- [Granite Speech 3.3](tutorials/05-multimodal-ai/granite-speech-3.3-8b.ipynb)

### [06 - Tool & Function Calling](tutorials/06-tool-calling-and-function-calling/)
Extend LLM capabilities by integrating external tools and APIs.

### [07 - Guardrails & Safety](tutorials/07-guardrails-and-safety/)
Build responsible AI systems with safety mechanisms and content filtering.

**Featured Tutorials:**
- [AI Agent Security](tutorials/07-guardrails-and-safety/ai-agent-security/) - Secure AI agents with authentication, RBAC, and data protection
- [LLM Guardrails](tutorials/07-guardrails-and-safety/llm-guardrails.ipynb) - Input/output filtering and content moderation
- [Granite Guardian Web App](tutorials/07-guardrails-and-safety/granite-guardian-webapp/) - Real-time content filtering application

### [08 - Time Series & Forecasting](tutorials/08-time-series-and-forecasting/)
Apply AI to temporal data for forecasting and analysis.

### [09 - Text Processing & NLP](tutorials/09-text-processing-and-nlp/)
Classic and modern natural language processing techniques.

### [10 - Machine Learning Foundations](tutorials/10-machine-learning-foundations/)
Core ML concepts and techniques.

### [11 - Model Context Protocol](tutorials/11-model-context-protocol/)
Work with MCP servers and IBM Bob integration.

### [12 - Observability & Monitoring](tutorials/12-observability-and-monitoring/)
Track, monitor, and optimize AI system performance.

**Featured Tutorials:**
- [watsonx Orchestrate with AgentOps](tutorials/12-observability-and-monitoring/wxo_agentops/)
- [watsonx Observability with Langfuse](tutorials/12-observability-and-monitoring/wxo_observability_langfuse/)

### [13 - Full-Stack Applications](tutorials/13-full-stack-applications/)
Complete end-to-end AI applications and projects.

**Featured Projects:**
- [AI Stylist](tutorials/13-full-stack-applications/ai-stylist/)
- [TTRPG AI](tutorials/13-full-stack-applications/ttrpgai/)
- [Silly Story Time](tutorials/13-full-stack-applications/silly_story_time/)

### [14 - LoRA & Fine-Tuning](tutorials/14-lora-and-fine-tuning/)
Customize models for your specific use cases.

### [15 - Docling](tutorials/15-docling/)
Parse, convert, and process documents using IBM's open-source Docling toolkit.

**Featured Tutorials:**
- [Convert Unstructured Data to Structured Data](tutorials/15-docling/unstructured-data-conversion.ipynb) - Transform scanned documents into structured formats
- [Docling Granite Question Answering](tutorials/01-rag-and-retrieval/docling_granite_question_answering.ipynb) - Document Q&A with Granite 3.1
- [DeepSeek RAG Reasoning with Docling](tutorials/01-rag-and-retrieval/deepseek_rag_reasoning_docling_watsonx.ipynb) - Advanced RAG with Docling

### [16 - IBM Bob](tutorials/16-ibm-bob/)
Master IBM Bob, the AI-powered coding assistant for documentation, development, and automation.

**Featured Tutorials:**
- [AI Documentation with IBM Bob](tutorials/16-ibm-bob/ai-docs-ibm-bob/) - Automatically generate and maintain project documentation
- [MCP Server Integration](tutorials/16-ibm-bob/mcp-server-integration-ibm-bob/) - Build and integrate Model Context Protocol servers

## 🎓 Learning Paths

### Beginner Path: Getting Started with AI
Perfect for those new to AI and LLMs. Start here to build foundational knowledge.

1. [LangChain RAG](tutorials/01-rag-and-retrieval/langchain-rag.ipynb) - Build your first document Q&A system
2. [Docling Granite Question Answering](tutorials/01-rag-and-retrieval/docling_granite_question_answering.ipynb) - Document processing with Granite 3.1
3. [LLM Agent Orchestration](tutorials/02-agents-and-orchestration/llm-agent-orchestration.ipynb) - Create autonomous AI agents
4. [Function Calling](tutorials/06-tool-calling-and-function-calling/function-calling.ipynb) - Extend LLM capabilities with tools
5. [Role Prompting](tutorials/04-prompt-engineering/role-prompting-tutorial.ipynb) - Master effective prompt techniques

### Intermediate Path: Building AI Systems
For developers ready to build more sophisticated AI applications.

1. [Agentic RAG](tutorials/01-rag-and-retrieval/agentic-rag.ipynb) - RAG with reasoning capabilities
2. [Building Agentic Workflows with LangGraph](tutorials/02-agents-and-orchestration/building-agentic-workflow-langgraph.ipynb) - Advanced agent orchestration
3. [Text Classification Agent with watsonx Orchestrate](tutorials/02-agents-and-orchestration/wxo-text-classification/) - Build sentiment analysis agents
4. [RAG Evaluation with Ragas](tutorials/01-rag-and-retrieval/rag-evaluation-ragas.ipynb) - Measure and optimize RAG performance
5. [Multimodal AI with Granite Vision](tutorials/05-multimodal-ai/ppt-ai-analyzer-granite-vision.ipynb) - Work with vision models
6. [LLM Guardrails](tutorials/07-guardrails-and-safety/llm-guardrails.ipynb) - Build responsible AI systems
7. [Convert Unstructured Data with Docling](tutorials/15-docling/unstructured-data-conversion.ipynb) - Transform documents into structured formats

### Advanced Path: Production AI
Master enterprise-grade AI systems with multi-agent collaboration and observability.

1. [Multi-Agent Customer Service Analysis](tutorials/03-multi-agent-systems/multiagent-collab-cs-call-center-analysis/) - Collaborative AI systems
2. [BeeAI Agent-to-Agent Communication](tutorials/03-multi-agent-systems/a2a_tutorial/) - Agent collaboration protocols
3. [AI Agent Security](tutorials/07-guardrails-and-safety/ai-agent-security/) - Secure agents with authentication and RBAC
4. [watsonx Orchestrate with AgentOps](tutorials/12-observability-and-monitoring/wxo_agentops/) - Monitor agent performance
5. [watsonx Observability with Langfuse](tutorials/12-observability-and-monitoring/wxo_observability_langfuse/) - Track and optimize AI systems
6. [SQL Agent Application](tutorials/02-agents-and-orchestration/sql-agent-app/) - Full-stack agent application
7. [DeepSeek RAG Reasoning with Docling](tutorials/01-rag-and-retrieval/deepseek_rag_reasoning_docling_watsonx.ipynb) - Advanced RAG techniques

### Specialized Path: Multimodal & Full-Stack AI
Build complete applications with vision, speech, and multimodal capabilities.

1. [Granite Speech 3.3](tutorials/05-multimodal-ai/granite-speech-3.3-8b.ipynb) - Speech processing and transcription
2. [AI Personal Trainer with Llama](tutorials/05-multimodal-ai/ai-personal-trainer-llama/) - Vision-based fitness analysis
3. [AI Stylist](tutorials/13-full-stack-applications/ai-stylist/) - Complete multimodal fashion advisor
4. [Silly Story Time](tutorials/13-full-stack-applications/silly_story_time/) - Interactive storytelling application
5. [Granite Guardian Web App](tutorials/07-guardrails-and-safety/granite-guardian-webapp/) - Real-time content filtering

### IBM Bob Path: AI-Powered Development
Master IBM Bob for documentation, automation, and development workflows.

1. [AI Documentation with IBM Bob](tutorials/16-ibm-bob/ai-docs-ibm-bob/) - Automatically generate project documentation
2. [MCP Server Integration](tutorials/16-ibm-bob/mcp-server-integration-ibm-bob/) - Build Model Context Protocol servers
3. [Model Context Protocol Basics](tutorials/11-model-context-protocol/) - Understand MCP fundamentals

## 🛠️ Technologies Used

- **IBM watsonx.ai** - Enterprise AI platform
- **IBM Granite Models** - Open-source foundation models
- **LangChain** - LLM application framework
- **LangGraph** - Agent workflow orchestration
- **LlamaIndex** - Data framework for LLMs
- **CrewAI** - Multi-agent orchestration
- **BeeAI** - Agent framework
- **AutoGen** - Multi-agent conversations
- **Ollama** - Local LLM deployment
- **Chroma, Milvus** - Vector databases
- **Ragas** - RAG evaluation framework

## 🤝 Contributing

We welcome contributions! Whether you want to:

- 🐛 Report a bug
- 💡 Suggest a new tutorial
- 📝 Improve documentation
- 🔧 Submit a pull request

Please see our [Contributing Guide](CONTRIBUTING.md) for detailed instructions on how to contribute, including setup, development workflow, and code quality standards.

Also see our [Code of Conduct](CODE_OF_CONDUCT.md) for community guidelines.

## 📊 Repository Structure

```
ibmdotcom-tutorials/
├── tutorials/              # All tutorials organized by category
│   ├── 01-rag-and-retrieval/
│   ├── 02-agents-and-orchestration/
│   ├── 03-multi-agent-systems/
│   ├── 04-prompt-engineering/
│   ├── 05-multimodal-ai/
│   ├── 06-tool-calling-and-function-calling/
│   ├── 07-guardrails-and-safety/
│   ├── 08-time-series-and-forecasting/
│   ├── 09-text-processing-and-nlp/
│   ├── 10-machine-learning-foundations/
│   ├── 11-model-context-protocol/
│   ├── 12-observability-and-monitoring/
│   ├── 13-full-stack-applications/
│   ├── 14-lora-and-fine-tuning/
│   ├── 15-docling/
│   ├── 16-ibm-bob/
│   └── shared-assets/      # Shared data, images, and resources
├── .github/                # GitHub workflows and assets
└── README.md              # This file
```

## 🔗 Useful Links

- **[IBM watsonx](https://www.ibm.com/watsonx)** - AI platform
- **[IBM Granite Models](https://www.ibm.com/granite)** - Open-source LLMs
- **[IBM Think](https://www.ibm.com/think)** - Technical articles
- **[GitHub Discussions](https://github.com/IBM/ibmdotcom-tutorials/discussions)** - Community Q&A

## 📝 License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

## 🙋 Support

- **Questions?** Open a [GitHub Discussion](https://github.com/IBM/ibmdotcom-tutorials/discussions)
- **Issues?** Report them in [GitHub Issues](https://github.com/IBM/ibmdotcom-tutorials/issues)

## 🌟 Star History

If you find these tutorials helpful, please consider giving us a star! ⭐

---

**Maintained by:** IBM.com Technical Content Team  
**Last Updated:** February 2026