AI Agents Repository

This repository showcases a variety of AI agent implementations, ranging from chat applications and RAG (Retrieval-Augmented Generation) pipelines to specialized MCP (Model Context Protocol) servers.

Repository Structure

The repository is organized into the following main projects:

1. Av-Chatfriends

A ChatGPT-like web application that runs locally using llama.cpp and flask. It provides a web interface for interacting with local LLMs.

• Path: Av-Chatfriends/
• Key Features:
  • Flask-based backend.
  • OpenAI-compatible API integration (targeting local llama.cpp).
  • Simple HTML/JS frontend.
  • Docker support.
• Setup: See Av-Chatfriends/README.md for details.

2. MCP (Model Context Protocol) Servers

This directory contains implementations of MCP servers, which allow AI models to interact with local resources.

• Path: MCP/
• Sub-projects:
  • File System Management MCP: A server that provides full CRUD capabilities for file system operations.
    • Path: MCP/File_System_Management_MCP/
    • Setup: See MCP/File_System_Management_MCP/SETUP_INSTRUCTIONS.md.
  • Terminal Execution MCP: A server that enables command execution across various shells (CMD, PowerShell, WSL, Git Bash).
    • Path: MCP/Terminal_Execution_MCP/
    • Setup: See MCP/Terminal_Execution_MCP/SETUP_INSTRUCTIONS.md.

3. RAG (Retrieval-Augmented Generation)

Contains Jupyter notebooks demonstrating RAG pipelines for different data sources.

• Path: RAG/
• Contents:
  • MultRAG System/: A unified advanced RAG system combining PDF and URL ingestion with a FastAPI backend and a modern HTML/JS frontend. See RAG/MultRAG System/Readme.md.
  • RAG_For_Url.ipynb: RAG pipeline for querying web content.
  • Rag_For_Pdf.ipynb: RAG pipeline for querying PDF documents.
• Requirements: See RAG/requirements.txt or specific notebook instructions.

4. Google Adk

Demonstrates a customer support agent using Google's Agent Development Kit (ADK) and Gemini models.

• Path: Google Adk/
• Contents:
  • customer-support-agent.ipynb: A comprehensive notebook implementing a support agent.
• Requirements: See Google Adk/requirements.txt.

Getting Started

1. Clone the repository:

   git clone <repository_url>
   cd <repository_name>

2. Explore a project: Navigate to the project directory you are interested in and follow its specific README.md or SETUP_INSTRUCTIONS.md.

   • For the Chat App: cd Av-Chatfriends
   • For MCP Servers: cd MCP
   • For RAG Notebooks: cd RAG

Prerequisites

• Python 3.10+ is recommended for all projects.
• Docker is optional but recommended for Av-Chatfriends.
• Jupyter Notebook/Lab is required to run the notebooks in RAG and Google Adk.
• API Keys: Some projects (like Google Adk or RAG) may require API keys (e.g., Google Gemini, OpenAI) to be set in environment variables or Colab secrets.

Contributing

Contributions are welcome! Please ensure you:

1. Follow the existing code style.
2. Add docstrings to any new functions or classes.
3. Update the READMEs if you introduce new features or requirements.

License

[License Information Here]