We welcome contributions! Please fork and submit PRs! See our CONTRIBUTING.md guide for details.

• Simplicity 🌱: strong focus on keeping the project easy to use and the codebase simple.
• Privacy 🛡️: use local LLMs through Ollama for your private data.
• Flexibility 🤸: flexible features like:
  • KPI-directed: Prompt the LLMs to prioritize your KPIs in the dataset,
  • Preview what you are passing: data importer wizard preview and filter the dataset by columns and/or rows,
  • Dynamic filters: the LLM infers the most relevant filters for your dataset dynamically,
  • Custom LLM: use your favourite 3rd party LLM (or local through Ollama).
• Reusability 🔄: each dashboard generates a reusable "viz_spec" JSON file in the "llm_responses" folder, accessible for future use through the "Import Previous Viz Specs" feature. Each plot includes a "Code" tab to reproduce it anywhere.

1. Start the application with Docker Compose:

docker-compose up --build

2. Access the dashboard at http://localhost:8050

1. Start both Ollama and the dashboard:

docker-compose -f docker-compose.all-in-one.yml up --build

2. Access the dashboard at http://localhost:8050

1. Start Ollama separately (if using local models)
2. Run the application:

python src/app.py

To run the application in development mode:

pip install uv  # if you don't have it already
uv run ai_dashboard_builder --dev

In the project root folder, you can create a .env file and set the API keys for the LLMs you want to use, or pass them through the webapp.

• OLLAMA_HOST: Ollama server address (default: host.docker.internal)
• OPENAI_API_KEY: OpenAI API key (for GPT models)
• ANTHROPIC_API_KEY: Anthropic API key (for Claude models)
• GROQ_API_KEY: Groq API key (for Mixtral/LLaMA models)

We welcome contributions! Please see our CONTRIBUTING.md guide for details on how to get started.

This project is licensed under a form of MIT License - see the LICENSE.md file for details.