Meridian - Graph-Powered Conversational AI

Warning

This project is in its early stages of development.

Table of Contents

• ✨ Introduction
• 🌟 Key Features
• 🛠️ Technologies Used
• 🏗️ Production Deployment
  • Prerequisites
  • Production Deployment Setup
• 🧑‍💻 Local Development
  • Prerequisites
  • Local Development Setup
• 📄 API Documentation
• 🗺️ Project Structure
• 🤝 Contributing
• 🐛 Issues and Bug Reports
• 📄 License

✨ Introduction

Welcome to Meridian! Built on a graph-based architecture, Meridian goes beyond simple turn-taking, enabling richer, more nuanced, and highly efficient interactions.

Traditional AI chats often struggle with complex queries, requiring multiple back-and-forth interactions to gather information. Our innovative approach, deeply rooted in a dynamic graph structure, allows for intelligent parallelization of information gathering and processing. Imagine asking a question, and instead of a linear response, our system simultaneously consults multiple specialized AI models, synthesizing their insights into a coherent, comprehensive, and accurate answer.

This isn't just about speed; it's about depth, accuracy, and unlocking advanced AI workflows previously out of reach for consumer applications.

🌟 Key Features

• Graph-Based AI Engine: At its core, Meridian leverages a sophisticated graph database to represent and process information. This allows for complex relationships between concepts, enabling more intelligent context retention and dynamic query planning.
• Parallel Query Processing: User prompts can be dispatched to multiple LLMs in parallel. Their responses are then intelligently combined by a final LLM, delivering a unified and comprehensive answer.
• Model Agnostic: Powered by OpenRouter, Meridian seamlessly integrates with various AI models, giving you the flexibility to select the optimal model for each scenario.
• Oauth & UserPass: Secure user authentication and management, ensuring that your data is protected and accessible only to you.
• Attachment Support: Users can upload attachments, enhancing the context and richness of conversations.
• Syntax Highlighting: Code snippets are displayed with syntax highlighting, making it easier to read and understand technical content.
• LaTeX Rendering: Mathematical expressions are rendered beautifully, allowing for clear communication of complex ideas.
• Chat Branching: Using the graph structure, Meridian supports branching conversations, enabling users to explore different paths and topics without losing context.
• Highly Customizable: Meridian is highly configurable, allowing you to tailor the system to your specific needs and preferences.

See a detailed overview of the features in the Features.md file.

🛠️ Technologies Used

• Frontend:
  • Nuxt 3
  • Vue 3
  • Tailwind CSS
• Backend:
  • Python
  • FastAPI
  • PostgreSQL
  • Neo4j

🏗️ Production Deployment

Prerequisites

• Docker and Docker Compose installed on your machine.
• Yq (from Mike Farah) for TOML processing.

Production Deployment Setup

1. Clone the repository:

   git clone git@github.com:MathisVerstrepen/Meridian.git
   cd Meridian/docker

2. Create a config.toml file: Copy the config.example.toml file to config.toml and customize it with your production settings.

   cp config.example.toml config.toml

   Then set the necessary environment variables in the config.toml file.

3. Start Meridian: Use the provided bash script to start the Docker services. This will start the two databases (PostgreSQL and Neo4j), the backend API server, and the frontend application.

   chmod +x run.sh
   ./run.sh -d

4. Access the application: Open your web browser and navigate to http://localhost:3000 (default port) to access the Meridian frontend.

🧑‍💻 Local Development

Prerequisites

• Docker and Docker Compose installed on your machine.
• Yq (from Mike Farah) for TOML processing.
• Python 3.11 or higher installed on your machine.
• Node.js and npm installed on your machine for the frontend.

Local Development Setup

1. Clone the repository:

   git clone git@github.com:MathisVerstrepen/Meridian.git
   cd Meridian/docker

2. Create a config.local.toml file: Copy the config.local.example.toml file to config.local.toml and customize it with your settings.

   cp config.example.toml config.local.toml

   Then set the necessary environment variables in the config.local.toml file.

3. Start the databases: Use the provided bash script to start the Docker services. This will start the two databases (PostgreSQL and Neo4j).

   chmod +x run.sh
   ./run.sh dev -d

4. Start the backend: Open a new terminal window and run the backend server using Docker Compose.

   cd ../api
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   cd app
   fastapi dev main.py

5. Start the frontend: Open another terminal window and run the frontend server.

   cd ../ui
   npm install
   npm run dev

6. Access the application: Open your web browser and navigate to http://localhost:3000 (default port) to access the Meridian frontend.

📄 API Documentation

The backend API documentation (powered by FastAPI's Swagger UI) will be available at: http://localhost:8000/docs (when the backend is running).

🗺️ Project Structure

Meridian/
├── docker/          # Docker-related files and configurations files
├── api/             # Backend API code
├── ui/              # Frontend code
├── docs/            # Documentation files
├── README.md        # Project overview and setup instructions

🤝 Contributing

We welcome contributions to Meridian! Whether it's adding new features, improving existing ones, or fixing bugs, your help is appreciated.

🐛 Issues and Bug Reports

Found a bug or have a feature request? Please open an issue on our GitHub Issues page.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by Mathis Verstrepen