# Zia Image Recognition SDK
A simple, ergonomic Python SDK for the Zia Image Recognition API.
For more detailed information about the project architecture and design decisions, see [BUILD_SUMMARY.md](BUILD_SUMMARY.md).
## Features
- **Ergonomics first** - Single-line calls for common tasks
- **Type safety** - Rich typings with IDE autocompletion
- **Zero config by default** - Sensible defaults with environment variable support
- **Predictable errors** - Typed exceptions with actionable messages
- **Async/await support** - Modern Python async patterns
- **Image Recognition** - Create tasks, upload images, and retrieve results
- **Catalog Management** - Create, update, and retrieve catalog items
- **Raw JSON Access** - Flexible data access for custom processing
## Installation
### Using Poetry (Recommended for Development)
[Poetry](https://python-poetry.org/) is a modern dependency management tool for Python.
```bash
# Install Poetry (if not already installed)
curl -sSL https://install.python-poetry.org | python3 -
# Clone and install the SDK
git clone https://github.com/neurolabs/zia-sdk-python.git
cd zia-sdk-python
# Install with all dependencies (including DataFrame utilities)
poetry install --with dev
# Or install specific extras
poetry install --extras "dataframe" # For pandas support
poetry install --extras "databricks" # For Databricks support (includes pandas and pyspark)
poetry install --extras "all" # For all optional dependencies
# Activate the virtual environment
poetry shell
```
### Using pip (Production)
```bash
# Basic installation (no DataFrame support)
pip install zia-sdk-python
# With pandas support
pip install zia-sdk-python[dataframe]
# With Databricks support (includes pandas and pyspark)
pip install zia-sdk-python[databricks]
# With all optional dependencies
pip install zia-sdk-python[all]
# Or install from GitHub
pip install git+https://github.com/neurolabs/zia-sdk-python.git
```
### Manual Installation
```bash
# Clone the repository
git clone https://github.com/neurolabs/zia-sdk-python.git
cd zia-sdk-python
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
pip install -e .
```
For detailed installation instructions, see [INSTALLATION.md](INSTALLATION.md).
## Quick Start
### Configuration
The SDK supports multiple ways to configure your API key:
#### 1. Environment Variables (Recommended)
```bash
export NEUROLABS_API_KEY="your-api-key-here"
```
#### 2. .env File
```bash
# Copy the example file
cp env.example .env
# Edit .env with your API key
NEUROLABS_API_KEY=your-api-key-here
```
#### 3. Command Line Arguments
```python
client = Zia(api_key="your-api-key-here")
```
### Async Usage (Recommended)
```python
import asyncio
from zia import Zia, NLCatalogItemCreate
async def main():
# Initialize client (uses NEUROLABS_API_KEY env var)
async with Zia() as client:
# Health check
is_healthy = await client.health_check()
print(f"API Health: {is_healthy}")
# List catalog items
items = await client.catalog.list_items(limit=10)
print(f"Found {len(items)} catalog items")
# Create a catalog item
new_item = NLCatalogItemCreate(
name="My Product",
brand="My Brand",
barcode="1234567890123"
)
# Add thumbnail binary data if available
# with open("thumbnail.jpg", "rb") as f:
# new_item.thumbnail = f.read()
created_item = await client.catalog.create_item(new_item)
print(f"Created: {created_item.name}")
if __name__ == "__main__":
asyncio.run(main())
```
### Sync Usage (Simple Operations)
```python
from zia import Zia
# Initialize client
client = Zia()
# Health check
is_healthy = client.health_check_sync()
print(f"API Health: {is_healthy}")
# List catalog items
items = client.list_items_sync(limit=10)
print(f"Found {len(items)} catalog items")
# List tasks
tasks = client.list_tasks_sync(limit=5)
print(f"Found {len(tasks)} tasks")
```
## Configuration
The SDK supports multiple configuration sources with the following precedence:
1. **Explicit parameters** - Passed to `Zia()` constructor
2. **Environment variables** - Set in your environment
3. **Config file** - Future feature
### Environment Variables
```bash
export NEUROLABS_API_KEY="your-api-key"
export NEUROLABS_BASE_URL="https://api.neurolabs.ai/v2" # Optional
export NEUROLABS_TIMEOUT="30.0" # Optional
export NEUROLABS_MAX_RETRIES="3" # Optional
```
### Explicit Configuration
```python
client = Zia(
api_key="your-api-key",
base_url="https://api.neurolabs.ai/v2",
timeout=30.0,
max_retries=3
)
```
## API Reference
### Client
The main `Zia` client provides access to all API functionality:
```python
client = Zia()
# Catalog operations
await client.catalog.list_items()
await client.catalog.create_item(item)
await client.catalog.get_item(uuid)
# Image recognition operations
await client.task_management.list_tasks()
await client.task_management.create_task(task)
await client.image_recognition.upload_images(task_uuid, image_paths)
```
### Catalog Operations
#### List Items
```python
items = await client.catalog.list_items(
name="product name", # Optional filter
limit=50, # Max 100
offset=0 # Pagination
)
```
#### Create Item
```python
from zia import NLCatalogItemCreate
item = NLCatalogItemCreate(
name="Product Name",
brand="Brand Name",
barcode="1234567890123",
height=0.1, # meters
width=0.05, # meters
depth=0.02 # meters
)
# Add thumbnail binary data if available
# with open("thumbnail.jpg", "rb") as f:
# item.thumbnail = f.read()
created_item = await client.catalog.create_item(item)
```
#### Upload Reference Images
```python
image_paths = [Path("image1.jpg"), Path("image2.jpg")]
updated_item = await client.catalog.upload_reference_images(
item_uuid="item-uuid",
image_paths=image_paths
)
```
### Image Recognition Operations
#### Create Task
```python
from zia import NLIRTaskCreate
task = NLIRTaskCreate(
name="My Recognition Task",
catalog_items=["item-uuid-1", "item-uuid-2"],
compute_realogram=True,
compute_shares=True
)
created_task = await client.task_management.create_task(task)
```
#### Upload Images
```python
# Upload local images
image_paths = [Path("image1.jpg"), Path("image2.jpg")]
result_uuids = await client.image_recognition.upload_images(
task_uuid="task-uuid",
image_paths=image_paths
)
# Upload image URLs
image_urls = [
"https://example.com/image1.jpg",
"https://example.com/image2.jpg"
]
result_uuids = await client.image_recognition.upload_image_urls(
task_uuid="task-uuid",
image_urls=image_urls
)
```
#### Get Results
```python
# Get all results for a task
results = await client.results_management.get_task_results(
task_uuid="task-uuid",
limit=50,
offset=0
)
# Get specific result
result = await client.results_management.get_result(
task_uuid="task-uuid",
result_uuid="result-uuid"
)
```
### Raw JSON Access
For flexible data processing, you can access raw JSON responses:
```python
# Get raw JSON without parsing
raw_result = await client.result_management.get_result_raw(
task_uuid="task-uuid",
result_uuid="result-uuid"
)
# Access data directly
print(f"Status: {raw_result['status']}")
print(f"Duration: {raw_result.get('duration')}")
# Flexible data processing
if 'coco' in raw_result and raw_result['coco']:
annotations = raw_result['coco'].get('annotations', [])
for annotation in annotations:
score = annotation.get('neurolabs', {}).get('score', 0)
if score > 0.9:
print(f"High confidence: {score}")
```
### IRResults DataFrame Conversion
#### Spark Integration
```python
from zia.utils import to_spark_dataframe
from pyspark.sql import SparkSession
# Create Spark session
spark = SparkSession.builder.appName("ZiaAnalysis").getOrCreate()
# Convert to Spark DataFrame
spark_df = to_spark_dataframe(results, spark)
spark_df.show()
# Write to Delta table
spark_df.write.format("delta").mode("overwrite").saveAsTable("catalog.schema.table")
# Import the utilities
from dataframe import (
ir_results_to_dataframe,
ir_results_to_summary_dataframe,
analyze_results_dataframe,
filter_high_confidence_detections,
get_product_summary,
)
# Get results
results = await client.results_management.get_all_task_results(task_uuid)
# Convert to detailed DataFrame (one row per detection)
df_detailed = ir_results_to_dataframe(results)
print(f"Found {len(df_detailed)} detections")
# Convert to summary DataFrame (one row per result)
df_summary = ir_results_to_summary_dataframe(results)
print(f"Processed {len(df_summary)} images")
# Analyze the data
analysis = analyze_results_dataframe(df_detailed)
print(f"Average detection score: {analysis['score_stats']['mean']:.3f}")
print(f"High confidence detections: {analysis['score_distribution']['high_confidence']}")
# Filter high confidence detections
high_conf_df = filter_high_confidence_detections(df_detailed, threshold=0.9)
print(f"High confidence detections: {len(high_conf_df)}")
# Get product summary
product_summary = get_product_summary(df_detailed)
print(f"Unique products: {len(product_summary)}")
```
The detailed DataFrame includes all detection information with proper category-annotation matching. See `utils/README.md` for complete documentation.
## Error Handling
The SDK provides typed exceptions for different error scenarios:
```python
from zia import (
NeurolabsError,
NeurolabsAuthError,
NeurolabsRateLimitError,
NeurolabsValidationError
)
try:
await client.catalog.create_item(item)
except NeurolabsAuthError as e:
print(f"Authentication failed: {e}")
except NeurolabsRateLimitError as e:
print(f"Rate limited. Retry after: {e.retry_after} seconds")
except NeurolabsValidationError as e:
print(f"Validation error: {e}")
except NeurolabsError as e:
print(f"Unexpected error: {e}")
```
## Models
The SDK uses Pydantic models for type safety and validation:
### CatalogItem
```python
from zia import NLCatalogItem
item = NLCatalogItem(
uuid="item-uuid",
status="ONBOARDED",
thumbnail_url="https://...",
name="Product Name",
brand="Brand Name",
created_at=datetime.now(),
updated_at=datetime.now()
)
```
### IRTask
```python
from zia import NLIRTask
task = NLIRTask(
uuid="task-uuid",
name="Task Name",
created_at=datetime.now(),
updated_at=datetime.now(),
compute_realogram=False,
compute_shares=False
)
```
### IRResult
```python
from zia import NLIRResult
result = NLIRResult(
uuid="result-uuid",
task_uuid="task-uuid",
image_url="https://...",
status="PROCESSED",
failure_reason="",
created_at=datetime.now(),
updated_at=datetime.now(),
confidence_score=0.95
)
```
## Development
### Setup
```bash
git clone <repository>
cd zia-sdk-python
poetry install
```
### Testing
```bash
# Run all tests
# NOTE: Integration tests create actual items, tasks, image processing in the backend, based on your API KEY
poetry run pytest
# Run with coverage
poetry run pytest --cov=zia --cov-report=html
# Run specific test file
poetry run pytest zia/tests/test_client.py -v
```
### Code Formatting
```bash
# Format code
poetry run black .
poetry run isort .
# Check formatting
poetry run black --check .
poetry run isort --check-only .
```
### Type Checking
```bash
poetry run mypy zia/
```
### Linting
```bash
poetry run ruff check .
```
## CLI Interface
The SDK includes a command-line interface:
```bash
# Health check
zia health
# List items
zia items 10
# List tasks
zia tasks 5
```
## License
This project is licensed under the MIT License.
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request
Raw data
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"keywords": "ai, computer-vision, image-recognition, neurolabs, zia",
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"description": "# Zia Image Recognition SDK\n\nA simple, ergonomic Python SDK for the Zia Image Recognition API.\n\nFor more detailed information about the project architecture and design decisions, see [BUILD_SUMMARY.md](BUILD_SUMMARY.md).\n\n## Features\n\n- **Ergonomics first** - Single-line calls for common tasks\n- **Type safety** - Rich typings with IDE autocompletion\n- **Zero config by default** - Sensible defaults with environment variable support\n- **Predictable errors** - Typed exceptions with actionable messages\n- **Async/await support** - Modern Python async patterns\n- **Image Recognition** - Create tasks, upload images, and retrieve results\n- **Catalog Management** - Create, update, and retrieve catalog items\n- **Raw JSON Access** - Flexible data access for custom processing\n\n## Installation\n\n### Using Poetry (Recommended for Development)\n\n[Poetry](https://python-poetry.org/) is a modern dependency management tool for Python.\n\n```bash\n# Install Poetry (if not already installed)\ncurl -sSL https://install.python-poetry.org | python3 -\n\n# Clone and install the SDK\ngit clone https://github.com/neurolabs/zia-sdk-python.git\ncd zia-sdk-python\n\n# Install with all dependencies (including DataFrame utilities)\npoetry install --with dev\n\n# Or install specific extras\npoetry install --extras \"dataframe\" # For pandas support\npoetry install --extras \"databricks\" # For Databricks support (includes pandas and pyspark)\npoetry install --extras \"all\" # For all optional dependencies\n\n# Activate the virtual environment\npoetry shell\n```\n\n### Using pip (Production)\n\n```bash\n# Basic installation (no DataFrame support)\npip install zia-sdk-python\n\n# With pandas support\npip install zia-sdk-python[dataframe]\n\n\n# With Databricks support (includes pandas and pyspark)\npip install zia-sdk-python[databricks]\n\n# With all optional dependencies\npip install zia-sdk-python[all]\n\n# Or install from GitHub\npip install git+https://github.com/neurolabs/zia-sdk-python.git\n```\n\n### Manual Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/neurolabs/zia-sdk-python.git\ncd zia-sdk-python\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\npip install -e .\n```\n\nFor detailed installation instructions, see [INSTALLATION.md](INSTALLATION.md).\n\n## Quick Start\n\n### Configuration\n\nThe SDK supports multiple ways to configure your API key:\n\n#### 1. Environment Variables (Recommended)\n\n```bash\nexport NEUROLABS_API_KEY=\"your-api-key-here\"\n```\n\n#### 2. .env File\n\n```bash\n# Copy the example file\ncp env.example .env\n\n# Edit .env with your API key\nNEUROLABS_API_KEY=your-api-key-here\n```\n\n#### 3. Command Line Arguments\n\n```python\nclient = Zia(api_key=\"your-api-key-here\")\n```\n\n### Async Usage (Recommended)\n\n```python\nimport asyncio\nfrom zia import Zia, NLCatalogItemCreate\n\nasync def main():\n # Initialize client (uses NEUROLABS_API_KEY env var)\n async with Zia() as client:\n # Health check\n is_healthy = await client.health_check()\n print(f\"API Health: {is_healthy}\")\n \n # List catalog items\n items = await client.catalog.list_items(limit=10)\n print(f\"Found {len(items)} catalog items\")\n \n # Create a catalog item\n new_item = NLCatalogItemCreate(\n name=\"My Product\",\n brand=\"My Brand\",\n barcode=\"1234567890123\"\n )\n \n # Add thumbnail binary data if available\n # with open(\"thumbnail.jpg\", \"rb\") as f:\n # new_item.thumbnail = f.read()\n \n created_item = await client.catalog.create_item(new_item)\n print(f\"Created: {created_item.name}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n### Sync Usage (Simple Operations)\n\n```python\nfrom zia import Zia\n\n# Initialize client\nclient = Zia()\n\n# Health check\nis_healthy = client.health_check_sync()\nprint(f\"API Health: {is_healthy}\")\n\n# List catalog items\nitems = client.list_items_sync(limit=10)\nprint(f\"Found {len(items)} catalog items\")\n\n# List tasks\ntasks = client.list_tasks_sync(limit=5)\nprint(f\"Found {len(tasks)} tasks\")\n```\n\n## Configuration\n\nThe SDK supports multiple configuration sources with the following precedence:\n\n1. **Explicit parameters** - Passed to `Zia()` constructor\n2. **Environment variables** - Set in your environment\n3. **Config file** - Future feature\n\n### Environment Variables\n\n```bash\nexport NEUROLABS_API_KEY=\"your-api-key\"\nexport NEUROLABS_BASE_URL=\"https://api.neurolabs.ai/v2\" # Optional\nexport NEUROLABS_TIMEOUT=\"30.0\" # Optional\nexport NEUROLABS_MAX_RETRIES=\"3\" # Optional\n```\n\n### Explicit Configuration\n\n```python\nclient = Zia(\n api_key=\"your-api-key\",\n base_url=\"https://api.neurolabs.ai/v2\",\n timeout=30.0,\n max_retries=3\n)\n```\n\n## API Reference\n\n### Client\n\nThe main `Zia` client provides access to all API functionality:\n\n```python\nclient = Zia()\n\n# Catalog operations\nawait client.catalog.list_items()\nawait client.catalog.create_item(item)\nawait client.catalog.get_item(uuid)\n\n# Image recognition operations\nawait client.task_management.list_tasks()\nawait client.task_management.create_task(task)\nawait client.image_recognition.upload_images(task_uuid, image_paths)\n```\n\n### Catalog Operations\n\n#### List Items\n\n```python\nitems = await client.catalog.list_items(\n name=\"product name\", # Optional filter\n limit=50, # Max 100\n offset=0 # Pagination\n)\n```\n\n#### Create Item\n\n```python\nfrom zia import NLCatalogItemCreate\n\nitem = NLCatalogItemCreate(\n name=\"Product Name\",\n brand=\"Brand Name\",\n barcode=\"1234567890123\",\n height=0.1, # meters\n width=0.05, # meters\n depth=0.02 # meters\n)\n\n# Add thumbnail binary data if available\n# with open(\"thumbnail.jpg\", \"rb\") as f:\n# item.thumbnail = f.read()\n\ncreated_item = await client.catalog.create_item(item)\n```\n\n#### Upload Reference Images\n\n```python\nimage_paths = [Path(\"image1.jpg\"), Path(\"image2.jpg\")]\nupdated_item = await client.catalog.upload_reference_images(\n item_uuid=\"item-uuid\",\n image_paths=image_paths\n)\n```\n\n### Image Recognition Operations\n\n#### Create Task\n\n```python\nfrom zia import NLIRTaskCreate\n\ntask = NLIRTaskCreate(\n name=\"My Recognition Task\",\n catalog_items=[\"item-uuid-1\", \"item-uuid-2\"],\n compute_realogram=True,\n compute_shares=True\n)\n\ncreated_task = await client.task_management.create_task(task)\n```\n\n#### Upload Images\n\n```python\n# Upload local images\nimage_paths = [Path(\"image1.jpg\"), Path(\"image2.jpg\")]\nresult_uuids = await client.image_recognition.upload_images(\n task_uuid=\"task-uuid\",\n image_paths=image_paths\n)\n\n# Upload image URLs\nimage_urls = [\n \"https://example.com/image1.jpg\",\n \"https://example.com/image2.jpg\"\n]\nresult_uuids = await client.image_recognition.upload_image_urls(\n task_uuid=\"task-uuid\",\n image_urls=image_urls\n)\n```\n\n#### Get Results\n\n```python\n# Get all results for a task\nresults = await client.results_management.get_task_results(\n task_uuid=\"task-uuid\",\n limit=50,\n offset=0\n)\n\n# Get specific result\nresult = await client.results_management.get_result(\n task_uuid=\"task-uuid\",\n result_uuid=\"result-uuid\"\n)\n```\n\n### Raw JSON Access\n\nFor flexible data processing, you can access raw JSON responses:\n\n```python\n# Get raw JSON without parsing\nraw_result = await client.result_management.get_result_raw(\n task_uuid=\"task-uuid\",\n result_uuid=\"result-uuid\"\n)\n\n# Access data directly\nprint(f\"Status: {raw_result['status']}\")\nprint(f\"Duration: {raw_result.get('duration')}\")\n\n# Flexible data processing\nif 'coco' in raw_result and raw_result['coco']:\n annotations = raw_result['coco'].get('annotations', [])\n for annotation in annotations:\n score = annotation.get('neurolabs', {}).get('score', 0)\n if score > 0.9:\n print(f\"High confidence: {score}\")\n```\n\n### IRResults DataFrame Conversion\n\n#### Spark Integration\n\n```python\nfrom zia.utils import to_spark_dataframe\nfrom pyspark.sql import SparkSession\n\n# Create Spark session\nspark = SparkSession.builder.appName(\"ZiaAnalysis\").getOrCreate()\n\n# Convert to Spark DataFrame\nspark_df = to_spark_dataframe(results, spark)\nspark_df.show()\n\n# Write to Delta table\nspark_df.write.format(\"delta\").mode(\"overwrite\").saveAsTable(\"catalog.schema.table\")\n\n# Import the utilities\n\nfrom dataframe import (\n ir_results_to_dataframe,\n ir_results_to_summary_dataframe,\n analyze_results_dataframe,\n filter_high_confidence_detections,\n get_product_summary,\n)\n\n# Get results\n\nresults = await client.results_management.get_all_task_results(task_uuid)\n\n# Convert to detailed DataFrame (one row per detection)\n\ndf_detailed = ir_results_to_dataframe(results)\nprint(f\"Found {len(df_detailed)} detections\")\n\n# Convert to summary DataFrame (one row per result)\n\ndf_summary = ir_results_to_summary_dataframe(results)\nprint(f\"Processed {len(df_summary)} images\")\n\n# Analyze the data\n\nanalysis = analyze_results_dataframe(df_detailed)\nprint(f\"Average detection score: {analysis['score_stats']['mean']:.3f}\")\nprint(f\"High confidence detections: {analysis['score_distribution']['high_confidence']}\")\n\n# Filter high confidence detections\n\nhigh_conf_df = filter_high_confidence_detections(df_detailed, threshold=0.9)\nprint(f\"High confidence detections: {len(high_conf_df)}\")\n\n# Get product summary\n\nproduct_summary = get_product_summary(df_detailed)\nprint(f\"Unique products: {len(product_summary)}\")\n\n```\n\nThe detailed DataFrame includes all detection information with proper category-annotation matching. See `utils/README.md` for complete documentation.\n\n## Error Handling\n\nThe SDK provides typed exceptions for different error scenarios:\n\n```python\nfrom zia import (\n NeurolabsError,\n NeurolabsAuthError,\n NeurolabsRateLimitError,\n NeurolabsValidationError\n)\n\ntry:\n await client.catalog.create_item(item)\nexcept NeurolabsAuthError as e:\n print(f\"Authentication failed: {e}\")\nexcept NeurolabsRateLimitError as e:\n print(f\"Rate limited. Retry after: {e.retry_after} seconds\")\nexcept NeurolabsValidationError as e:\n print(f\"Validation error: {e}\")\nexcept NeurolabsError as e:\n print(f\"Unexpected error: {e}\")\n```\n\n## Models\n\nThe SDK uses Pydantic models for type safety and validation:\n\n### CatalogItem\n\n```python\nfrom zia import NLCatalogItem\n\nitem = NLCatalogItem(\n uuid=\"item-uuid\",\n status=\"ONBOARDED\",\n thumbnail_url=\"https://...\",\n name=\"Product Name\",\n brand=\"Brand Name\",\n created_at=datetime.now(),\n updated_at=datetime.now()\n)\n```\n\n### IRTask\n\n```python\nfrom zia import NLIRTask\n\ntask = NLIRTask(\n uuid=\"task-uuid\",\n name=\"Task Name\",\n created_at=datetime.now(),\n updated_at=datetime.now(),\n compute_realogram=False,\n compute_shares=False\n)\n```\n\n### IRResult\n\n```python\nfrom zia import NLIRResult\n\nresult = NLIRResult(\n uuid=\"result-uuid\",\n task_uuid=\"task-uuid\",\n image_url=\"https://...\",\n status=\"PROCESSED\",\n failure_reason=\"\",\n created_at=datetime.now(),\n updated_at=datetime.now(),\n confidence_score=0.95\n)\n```\n\n## Development\n\n### Setup\n\n```bash\ngit clone <repository>\ncd zia-sdk-python\npoetry install\n```\n\n### Testing\n\n```bash\n# Run all tests \n# NOTE: Integration tests create actual items, tasks, image processing in the backend, based on your API KEY \npoetry run pytest\n\n# Run with coverage\npoetry run pytest --cov=zia --cov-report=html\n\n# Run specific test file\npoetry run pytest zia/tests/test_client.py -v\n```\n\n### Code Formatting\n\n```bash\n# Format code\npoetry run black .\npoetry run isort .\n\n# Check formatting\npoetry run black --check .\npoetry run isort --check-only .\n```\n\n### Type Checking\n\n```bash\npoetry run mypy zia/\n```\n\n### Linting\n\n```bash\npoetry run ruff check .\n```\n\n## CLI Interface\n\nThe SDK includes a command-line interface:\n\n```bash\n# Health check\nzia health\n\n# List items\nzia items 10\n\n# List tasks\nzia tasks 5\n```\n\n## License\n\nThis project is licensed under the MIT License.\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests\n5. Submit a pull request\n",
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