llama-dashboard

Llama Dashboard (llama-dashboard) provides a web-based dashboard application for visualizing data and monitoring systems within the LlamaSearch AI ecosystem. It connects to various data sources to present information graphically.

Key Features

• Web Application: A dashboard application, likely built with a framework like Flask or Streamlit (app.py).
• Data Source Connectors: Components to fetch data from different sources (databases, APIs, logs) (data_sources.py).
• Visualization: Displays data using charts, graphs, and tables.
• Core Module: Manages application setup and data fetching logic (core.py).
• Configurable: Allows defining data sources, visualization types, and refresh rates (config.py).

Installation

pip install llama-dashboard
# Or install directly from GitHub for the latest version:
# pip install git+https://github.com/llamasearchai/llama-dashboard.git

Usage

(Instructions on how to run the dashboard application will be added here.)

# Example command to run the dashboard
llama-dashboard run --config dashboard_config.yaml
# Then access via http://localhost:8050 (or similar)

Architecture Overview

graph TD
    A[User (Web Browser)] --> B{Dashboard Web App (app.py)};
    B -- Requests Data --> C{Core Module (core.py)};
    C --> D{Data Source Interface (data_sources.py)};
    D -- Fetches Data --> E[(Data Source 1: DB)];
    D -- Fetches Data --> F[(Data Source 2: API)];
    D -- Fetches Data --> G[(Data Source 3: Logs)];
    E --> D;
    F --> D;
    G --> D;
    D --> C;
    C -- Formats Data --> B;
    B -- Renders UI / Visualizations --> A;

    H[Configuration (config.py)] -- Configures --> C;
    H -- Configures --> D;

    style B fill:#f9f,stroke:#333,stroke-width:2px
    style E fill:#ccf,stroke:#333,stroke-width:1px
    style F fill:#ccf,stroke:#333,stroke-width:1px
    style G fill:#ccf,stroke:#333,stroke-width:1px

1. User Interface: The user accesses the dashboard through a web browser.
2. Web Application: Handles user requests and renders the dashboard UI.
3. Core Module: Orchestrates data fetching based on the dashboard configuration.
4. Data Source Interface: Connects to and retrieves data from various configured backends.
5. Data Formatting: Data is processed and formatted for visualization.
6. Rendering: The web application displays the data using charts and other UI elements.
7. Configuration: Defines data sources, connection details, visualization types, refresh intervals, etc.

Configuration

(Details on configuring data source connections (DB URIs, API endpoints/keys), dashboard layouts, chart types, etc., will be added here.)

Development

Setup

# Clone the repository
git clone https://github.com/llamasearchai/llama-dashboard.git
cd llama-dashboard

# Install in editable mode with development dependencies
pip install -e ".[dev]"

Testing

pytest tests/

Contributing

Contributions are welcome! Please refer to CONTRIBUTING.md and submit a Pull Request.

License

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