generative-ai

This repository centers on GenAI learning resources and practical use cases, including many agentic AI patterns and MCP (Model Context Protocol) references. The MCP portion describes a standard for tool interoperability among LLMs, enabling multi-agent coordination, web search integration, and modular tool usage within larger GenAI workflows. Use the MCP concepts to understand how different AI tools (search, retrieval, reasoning agents, and memory) can be orchestrated together in a single session. The materials and projects in this repo illustrate practical patterns such as advanced RAG, agentic AI orchestration, and multi-agent workflows, which align with MCP’s goal of interoperable tool usage in GenAI systems. When you explore the MCP references, you’ll find guidance on setting up agent collaboration, tool invocation protocols, and patterns for latency handling and decision flow among components.

Prerequisites:

• Node.js and npm (for any Node-based tooling)
• A modern web browser for documentation resources
• Git to clone the repository

Installation steps:

1. Clone the repository: git clone https://github.com/genieincodebottle/generative-ai.git

2. Navigate to the project directory: cd generative-ai

3. If there is a Node-based server you intend to run, install dependencies: npm install

4. If you plan to run a Python-based component (per MCP discussions in the repo), set up a virtual environment: python3 -m venv venv source venv/bin/activate pip install -r requirements.txt

5. Start the server or tooling as indicated by your chosen entry point. For example: node path/to/server.js or uvx path/to/uvx-entry

6. Verify that the MCP tools are reachable (e.g., via a local HTTP or IPC endpoint) and consult the repository’s docs for any project-specific flags or environment variables.

Notes and tips:

• This repository emphasizes GenAI resources and MCP concepts for tool interoperability. If you plan to experiment with MCP tooling, ensure your environment variables (like API keys for external services) are securely managed.
• The MCP references in this repo are educational and illustrate patterns (RAG, agentic AI, multi-agent orchestration). If you implement a server, consider exposing a consistent tool invocation protocol and clear error handling to maintain robust inter-tool communication.
• Common issues may include missing environment variables, incompatible library versions, or network access restrictions when integrating external tools. Start with documented examples and gradually expand tool integrations.
• If you add a Node.js or Python server, document the exact entry point and any required configuration in a README to help others reproduce the setup.