Welcome to the TuringDB Examples repository! This collection provides practical examples and tutorials for using TuringDB in real-world scenarios.

• Python 3.13 or higher
• uv package manager

1. Clone the repository:

   git clone https://github.com/turing-db/turingdb-examples.git
   cd turingdb-examples

2. Install dependencies:

   uv sync

3. Start Jupyter Lab:

   bash run_jupyter.sh

   This will start the Jupyter server on port 8889 (no token required). Open your web browser and go to:

   http://localhost:8889/lab
   

4. Open and run the example notebooks in examples/notebooks/public_version/

Located in examples/notebooks/public_version/:

• London Transport Analysis (london_transport_TfL.ipynb): Demonstrates TuringDB usage with real Transport for London data
• Complete datasets included in the data/ folder

All examples work seamlessly with popular AI providers and include everything needed for data analysis and AI integration.

1. Run a notebook: Navigate to any .ipynb file in Jupyter Lab and run the cells
2. Modify examples: Feel free to experiment with the code and data
3. Add your own data: Replace or add datasets in the data/ folders

Some examples may require API keys for AI services. To set them up:

1. Create a .env file in the project root directory (same level as pyproject.toml):

   touch .env

2. Add your API keys to the .env file using any text editor:

   # Open with your preferred editor (nano, vim, code, etc.)
   nano .env

3. Add the following content (replace with your actual keys):

   # Optional: Add your API keys if needed by specific examples
   ANTHROPIC_API_KEY=your_anthropic_key_here
   OPENAI_API_KEY=your_openai_key_here
   MISTRAL_API_KEY=your_mistral_key_here

4. Save and close the file. The .env file is automatically ignored by git for security.

The repository includes real datasets:

• Transport for London (TfL): Tube station data, routes, and sightseeing information
• All data files are located in examples/notebooks/public_version/data/

All dependencies are automatically managed and installed when you run uv sync. No manual package installation needed!

Import errors: Make sure you've run uv sync to install all dependencies

Jupyter not starting: Ensure you're using bash run_jupyter.sh and not a system-wide jupyter installation

Missing data files: Verify that CSV files exist in examples/notebooks/public_version/data/

API errors: Check that any required API keys are properly set in your .env file

• Check the TuringDB documentation

Ready to dive into AI-powered data analysis? Open those notebooks and discover what TuringDB can do for you! 🎯✨