mindzie-api 2.0.0

Official Python client library for Mindzie Studio API

Mindzie API Python Client

Official Python client library for the Mindzie Studio API. This library provides comprehensive access to all Mindzie Studio API endpoints with a clean, Pythonic interface.

Features

• 🚀 Complete API Coverage - Access to all Mindzie Studio API endpoints
• 🔐 Multiple Authentication Methods - API Key, Bearer Token, and Azure AD support
• 🔄 Automatic Retries - Built-in retry logic with exponential backoff
• 📦 Type-Safe - Full type hints and Pydantic models for all responses
• 📁 File Upload Support - Easy dataset creation from CSV, package, and binary files
• 📄 Pagination Handling - Automatic pagination for large result sets
• 🚦 Rate Limiting - Respects API rate limits with automatic throttling
• ⚡ Async Support - Optional async/await support for high-performance applications
• 🧪 Well-Tested - Comprehensive test suite with >90% code coverage

Installation

Basic Installation

pip install mindzie-api

With Async Support

pip install mindzie-api[async]

With Azure AD Authentication

pip install mindzie-api[azure]

Development Installation

pip install mindzie-api[dev]

Quick Start

from mindzie_api import MindzieAPIClient

# Initialize the client
client = MindzieAPIClient(
    base_url="https://dev.mindziestudio.com",
    tenant_id="your-tenant-id",
    api_key="your-api-key"
)

# Get all projects
projects = client.projects.get_all()
for project in projects.projects:
    print(f"Project: {project.project_name} (ID: {project.project_id})")

# Get datasets for a project
datasets = client.datasets.get_all(project_id="your-project-id")
print(f"Found {datasets['TotalCount']} datasets")

# Create a dataset from CSV
dataset = client.datasets.create_from_csv(
    project_id="your-project-id",
    dataset_name="Sales Data 2024",
    case_id_column="order_id",
    activity_name_column="activity",
    activity_time_column="timestamp",
    csv_file="path/to/your/data.csv"
)

Configuration

Environment Variables

The client can be configured using environment variables:

export MINDZIE_API_URL="https://dev.mindziestudio.com"
export MINDZIE_TENANT_ID="your-tenant-id"
export MINDZIE_API_KEY="your-api-key"

Then initialize without parameters:

client = MindzieAPIClient()

Configuration File

Create a .env file in your project:

MINDZIE_API_URL=https://dev.mindziestudio.com
MINDZIE_TENANT_ID=your-tenant-id
MINDZIE_API_KEY=your-api-key

Authentication

API Key Authentication (Default)

client = MindzieAPIClient(
    base_url="https://dev.mindziestudio.com",
    tenant_id="your-tenant-id",
    api_key="your-api-key"
)

Bearer Token Authentication

from mindzie_api import MindzieAPIClient
from mindzie_api.constants import AuthType

client = MindzieAPIClient(
    base_url="https://dev.mindziestudio.com",
    tenant_id="your-tenant-id",
    auth_type=AuthType.BEARER,
    token="your-bearer-token"
)

Azure AD Authentication

from mindzie_api import MindzieAPIClient
from mindzie_api.constants import AuthType

client = MindzieAPIClient(
    base_url="https://dev.mindziestudio.com",
    tenant_id="your-tenant-id",
    auth_type=AuthType.AZURE_AD,
    azure_tenant_id="azure-tenant-id",
    azure_client_id="azure-client-id",
    azure_client_secret="azure-client-secret"
)

API Examples

Projects

# List all projects
projects = client.projects.get_all(page=1, page_size=50)

# Get project by ID
project = client.projects.get_by_id("project-id")

# Get project summary
summary = client.projects.get_summary("project-id")

# Search projects
results = client.projects.search(
    name_contains="Sales",
    is_active=True,
    min_datasets=5
)

Datasets

# Get all datasets
datasets = client.datasets.get_all("project-id")

# Create dataset from CSV
dataset = client.datasets.create_from_csv(
    project_id="project-id",
    dataset_name="Customer Journey",
    case_id_column="customer_id",
    activity_name_column="action",
    activity_time_column="timestamp",
    csv_file="data.csv",
    resource_column="department",  # Optional
    culture_info="en-US"
)

# Update dataset
updated = client.datasets.update_from_csv(
    project_id="project-id",
    dataset_id="dataset-id",
    case_id_column="customer_id",
    activity_name_column="action",
    activity_time_column="timestamp",
    csv_file="updated_data.csv"
)

Investigations

# Get all investigations
investigations = client.investigations.get_all("project-id")

# Create investigation
investigation = client.investigations.create(
    project_id="project-id",
    name="Q4 Analysis",
    description="Quarterly performance review",
    dataset_id="dataset-id"
)

# Get investigation notebooks
notebooks = client.investigations.get_notebooks(
    project_id="project-id",
    investigation_id="investigation-id"
)

Notebooks

# Get notebook
notebook = client.notebooks.get(
    project_id="project-id",
    notebook_id="notebook-id"
)

# Execute notebook
execution = client.notebooks.execute(
    project_id="project-id",
    notebook_id="notebook-id"
)

# Check execution status
status = client.notebooks.get_execution_status(
    project_id="project-id",
    notebook_id="notebook-id"
)

# Get notebook blocks
blocks = client.notebooks.get_blocks(
    project_id="project-id",
    notebook_id="notebook-id"
)

Blocks

# Get block
block = client.blocks.get(
    project_id="project-id",
    block_id="block-id"
)

# Execute block
result = client.blocks.execute(
    project_id="project-id",
    block_id="block-id"
)

# Create filter block
filter_block = client.blocks.create_filter(
    project_id="project-id",
    name="High Value Orders",
    filter_expression="amount > 1000"
)

# Get block output data
output = client.blocks.get_output_data(
    project_id="project-id",
    block_id="block-id"
)

Dashboards

# Get all dashboards
dashboards = client.dashboards.get_all("project-id")

# Get dashboard panels
panels = client.dashboards.get_panels(
    project_id="project-id",
    dashboard_id="dashboard-id"
)

# Get dashboard URL
url_info = client.dashboards.get_url(
    project_id="project-id",
    dashboard_id="dashboard-id"
)

Execution Queue

# Get execution queue
queue = client.execution.get_queue("project-id")

# Queue notebook execution
execution = client.execution.queue_notebook(
    project_id="project-id",
    notebook_id="notebook-id"
)

# Get execution history
history = client.execution.get_history(
    project_id="project-id",
    page=1,
    page_size=50
)

# Check execution status
status = client.execution.get_status(
    project_id="project-id",
    execution_id="execution-id"
)

Error Handling

from mindzie_api.exceptions import (
    MindzieAPIException,
    AuthenticationError,
    NotFoundError,
    ValidationError,
    RateLimitError,
    ServerError
)

try:
    project = client.projects.get_by_id("invalid-id")
except NotFoundError as e:
    print(f"Project not found: {e.message}")
except AuthenticationError as e:
    print(f"Authentication failed: {e.message}")
except ValidationError as e:
    print(f"Invalid request: {e.message}")
except RateLimitError as e:
    print(f"Rate limit exceeded. Retry after {e.retry_after} seconds")
except ServerError as e:
    print(f"Server error: {e.message}")
except MindzieAPIException as e:
    print(f"API error: {e.message}")

Pagination

# Manual pagination
page = 1
while True:
    response = client.projects.get_all(page=page, page_size=100)
    
    for project in response.projects:
        process_project(project)
    
    if not response.has_next:
        break
    
    page = response.next_page

# Automatic pagination (fetches all items)
all_projects = client.projects.list_projects()  # Handles pagination internally

File Uploads

# Upload from file path
dataset = client.datasets.create_from_csv(
    project_id="project-id",
    dataset_name="Sales Data",
    case_id_column="order_id",
    activity_name_column="status",
    activity_time_column="timestamp",
    csv_file="/path/to/data.csv"
)

# Upload from file object
with open("data.csv", "rb") as f:
    dataset = client.datasets.create_from_csv(
        project_id="project-id",
        dataset_name="Sales Data",
        case_id_column="order_id",
        activity_name_column="status",
        activity_time_column="timestamp",
        csv_file=f
    )

# Upload binary data
dataset = client.datasets.create_from_binary(
    project_id="project-id",
    dataset_name="Processed Data",
    binary_file="data.bin"
)

Advanced Configuration

client = MindzieAPIClient(
    base_url="https://dev.mindziestudio.com",
    tenant_id="your-tenant-id",
    api_key="your-api-key",
    timeout=60,  # Request timeout in seconds
    max_retries=5,  # Maximum retry attempts
    verify_ssl=True,  # SSL certificate verification
    proxies={  # Proxy configuration
        "http": "http://proxy.company.com:8080",
        "https": "https://proxy.company.com:8080"
    }
)

Testing

Run the test suite:

# Install development dependencies
pip install -e ".[dev]"

# Run all tests
pytest

# Run with coverage
pytest --cov=mindzie_api

# Run specific test categories
pytest -m unit
pytest -m integration

# Run tests for specific module
pytest tests/unit/test_project.py

Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Support

• 📧 Email: [e-mail]
• 📖 Documentation: https://docs.mindzie.com/api/python
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

License

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

Changelog

See CHANGELOG.md for a list of changes in each version.

Authors

• Mindzie Development Team - Initial work - Mindzie

Acknowledgments

• Thanks to all contributors who have helped improve this library
• Built with love using Python, Pydantic, and Requests