> ## Documentation Index
> Fetch the complete documentation index at: https://docs.keywordsai.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Update Dataset

> Update dataset information including name, description, and metadata

## Overview

The `update()` method allows you to modify dataset information including name, description, and metadata. This is useful for maintaining accurate dataset information as your data evolves.

## Method Signature

### Synchronous

```python theme={"system"}
client.datasets.update(
    dataset_id: str,
    name: str = None,
    description: str = None,
    metadata: Dict[str, Any] = None,
    **kwargs
) -> Dataset
```

### Asynchronous

```python theme={"system"}
await client.datasets.aupdate(
    dataset_id: str,
    name: str = None,
    description: str = None,
    metadata: Dict[str, Any] = None,
    **kwargs
) -> Dataset
```

## Parameters

<ParamField path="dataset_id" type="str" required>
  The unique identifier of the dataset to update.
</ParamField>

<ParamField path="name" type="str">
  New name for the dataset. If not provided, the current name is preserved.
</ParamField>

<ParamField path="description" type="str">
  New description for the dataset. If not provided, the current description is preserved.
</ParamField>

<ParamField path="metadata" type="Dict[str, Any]">
  New metadata for the dataset. This will merge with existing metadata.
</ParamField>

## Returns

Returns the updated `Dataset` object with the following structure:

```python theme={"system"}
{
    "id": "dataset_123456789",
    "name": "Updated Customer Support Dataset",
    "description": "Updated collection of customer support conversations",
    "log_count": 1250,
    "created_at": "2024-01-15T10:30:00Z",
    "updated_at": "2024-01-25T16:20:00Z",  # Updated timestamp
    "metadata": {
        "category": "support",
        "purpose": "training",
        "version": "2.0",  # Updated version
        "quality_level": "high",
        "last_reviewed": "2024-01-25"
    }
}
```

## Examples

### Basic Dataset Update

```python theme={"system"}
from keywordsai import KeywordsAI

client = KeywordsAI(api_key="your-api-key")

# Update dataset name and description
updated_dataset = client.datasets.update(
    dataset_id="dataset_123456789",
    name="Enhanced Customer Support Dataset",
    description="Comprehensive collection of customer support conversations with quality annotations"
)

print(f"Updated dataset: {updated_dataset.name}")
print(f"New description: {updated_dataset.description}")
print(f"Updated at: {updated_dataset.updated_at}")
```

### Update Only Name

```python theme={"system"}
# Update only the dataset name
updated_dataset = client.datasets.update(
    dataset_id="dataset_123456789",
    name="Customer Support Training Data v2.0"
)

print(f"New name: {updated_dataset.name}")
print(f"Description unchanged: {updated_dataset.description}")
```

### Update Only Description

```python theme={"system"}
# Update only the description
updated_dataset = client.datasets.update(
    dataset_id="dataset_123456789",
    description="High-quality customer support conversations collected from Q1 2024, manually reviewed and annotated for training purposes"
)

print(f"Name unchanged: {updated_dataset.name}")
print(f"New description: {updated_dataset.description}")
```

### Update Metadata

```python theme={"system"}
# Update metadata fields
updated_dataset = client.datasets.update(
    dataset_id="dataset_123456789",
    metadata={
        "version": "2.1",
        "quality_level": "premium",
        "last_reviewed": "2024-01-25",
        "reviewer": "data_team",
        "review_notes": "Added quality annotations and removed duplicates",
        "tags": ["support", "training", "quality", "reviewed"]
    }
)

print(f"Updated metadata:")
for key, value in updated_dataset.metadata.items():
    print(f"  {key}: {value}")
```

### Comprehensive Update

```python theme={"system"}
# Update all fields at once
updated_dataset = client.datasets.update(
    dataset_id="dataset_123456789",
    name="Premium Customer Support Training Dataset",
    description="Curated and annotated customer support conversations for advanced model training",
    metadata={
        "version": "3.0",
        "category": "customer_support",
        "purpose": "training",
        "quality_level": "premium",
        "language": "english",
        "domain": "technology",
        "annotation_status": "complete",
        "review_status": "approved",
        "last_updated": "2024-01-25",
        "updated_by": "data_science_team",
        "changelog": "Added sentiment labels, removed PII, quality review completed",
        "tags": ["support", "training", "premium", "annotated", "reviewed"]
    }
)

print(f"Comprehensive update completed:")
print(f"  Name: {updated_dataset.name}")
print(f"  Description: {updated_dataset.description[:100]}...")
print(f"  Metadata fields: {len(updated_dataset.metadata)}")
print(f"  Version: {updated_dataset.metadata.get('version')}")
```

### Incremental Metadata Updates

```python theme={"system"}
# Get current dataset to preserve existing metadata
current_dataset = client.datasets.get("dataset_123456789")

# Merge new metadata with existing
new_metadata = current_dataset.metadata.copy()
new_metadata.update({
    "last_backup": "2024-01-25",
    "backup_location": "s3://backups/datasets/",
    "backup_status": "completed"
})

# Update with merged metadata
updated_dataset = client.datasets.update(
    dataset_id="dataset_123456789",
    metadata=new_metadata
)

print(f"Added backup information to metadata")
print(f"Total metadata fields: {len(updated_dataset.metadata)}")
```

### Conditional Updates

```python theme={"system"}
def update_dataset_if_needed(dataset_id, min_logs=100):
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    
    updates = {}
    
    # Update quality level based on log count
    if dataset.log_count >= 1000 and dataset.metadata.get("quality_level") != "high":
        updates["metadata"] = dataset.metadata.copy()
        updates["metadata"]["quality_level"] = "high"
        updates["metadata"]["auto_updated"] = "2024-01-25"
    
    # Update name if it doesn't reflect size
    if dataset.log_count >= 1000 and "Large" not in dataset.name:
        updates["name"] = f"Large {dataset.name}"
    
    # Update description if it's too short
    if not dataset.description or len(dataset.description) < 50:
        updates["description"] = f"Dataset containing {dataset.log_count} logs for {dataset.metadata.get('purpose', 'general')} purposes"
    
    # Apply updates if any
    if updates:
        updated_dataset = client.datasets.update(dataset_id, **updates)
        print(f"Applied {len(updates)} updates to {dataset.name}")
        return updated_dataset
    else:
        print(f"No updates needed for {dataset.name}")
        return dataset

# Conditionally update dataset
updated = update_dataset_if_needed("dataset_123456789")
```

### Asynchronous Updates

```python theme={"system"}
import asyncio

async def update_dataset_async(dataset_id, **updates):
    client = KeywordsAI(api_key="your-api-key")
    
    try:
        updated_dataset = await client.datasets.aupdate(dataset_id, **updates)
        print(f"Async updated: {updated_dataset.name}")
        print(f"  Updated at: {updated_dataset.updated_at}")
        return updated_dataset
    except Exception as e:
        print(f"Async update error: {e}")
        return None

# Run async update
updated = asyncio.run(update_dataset_async(
    "dataset_123456789",
    name="Async Updated Dataset",
    metadata={"async_update": True, "update_method": "async"}
))
```

### Batch Async Updates

```python theme={"system"}
import asyncio

async def update_multiple_datasets_async(updates_list):
    client = KeywordsAI(api_key="your-api-key")
    
    async def update_single_dataset(dataset_id, updates):
        try:
            result = await client.datasets.aupdate(dataset_id, **updates)
            return {"success": True, "dataset": result, "id": dataset_id}
        except Exception as e:
            return {"success": False, "error": str(e), "id": dataset_id}
    
    # Create tasks for all updates
    tasks = []
    for item in updates_list:
        dataset_id = item["dataset_id"]
        updates = {k: v for k, v in item.items() if k != "dataset_id"}
        task = update_single_dataset(dataset_id, updates)
        tasks.append(task)
    
    # Wait for all updates to complete
    results = await asyncio.gather(*tasks)
    
    # Separate successful and failed updates
    successful = [r["dataset"] for r in results if r["success"]]
    failed = [(r["id"], r["error"]) for r in results if not r["success"]]
    
    print(f"Batch async updates completed:")
    print(f"  Successful: {len(successful)}")
    print(f"  Failed: {len(failed)}")
    
    for dataset in successful:
        print(f"  ✓ Updated: {dataset.name}")
    
    for dataset_id, error in failed:
        print(f"  ✗ Failed {dataset_id}: {error}")
    
    return successful, failed

# Batch update configuration
updates = [
    {
        "dataset_id": "dataset_123456789",
        "metadata": {"batch_update": True, "batch_id": "batch_001"}
    },
    {
        "dataset_id": "dataset_987654321",
        "name": "Batch Updated Dataset 2",
        "metadata": {"batch_update": True, "batch_id": "batch_001"}
    },
    {
        "dataset_id": "dataset_555666777",
        "description": "Updated via batch operation",
        "metadata": {"batch_update": True, "batch_id": "batch_001"}
    }
]

# Run batch updates
successful, failed = asyncio.run(update_multiple_datasets_async(updates))
```

## Advanced Use Cases

### Version Management

```python theme={"system"}
def increment_dataset_version(dataset_id, version_notes=None):
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    
    # Parse current version
    current_version = dataset.metadata.get("version", "1.0")
    try:
        major, minor = map(int, current_version.split("."))
        new_version = f"{major}.{minor + 1}"
    except ValueError:
        new_version = "1.1"  # Default if version format is invalid
    
    # Prepare version metadata
    version_metadata = dataset.metadata.copy()
    version_metadata.update({
        "version": new_version,
        "previous_version": current_version,
        "version_updated_at": "2024-01-25T16:20:00Z",
        "version_notes": version_notes or f"Incremented from {current_version}"
    })
    
    # Update dataset
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        metadata=version_metadata
    )
    
    print(f"Version updated: {current_version} → {new_version}")
    print(f"Notes: {version_notes or 'No notes provided'}")
    
    return updated_dataset

# Increment version
updated = increment_dataset_version(
    "dataset_123456789",
    version_notes="Added quality annotations and removed duplicates"
)
```

### Quality Level Management

```python theme={"system"}
def update_quality_level(dataset_id, new_quality_level, review_notes=None):
    # Validate quality level
    valid_levels = ["low", "medium", "high", "premium"]
    if new_quality_level not in valid_levels:
        raise ValueError(f"Quality level must be one of: {valid_levels}")
    
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    current_quality = dataset.metadata.get("quality_level", "unknown")
    
    # Prepare quality metadata
    quality_metadata = dataset.metadata.copy()
    quality_metadata.update({
        "quality_level": new_quality_level,
        "previous_quality_level": current_quality,
        "quality_updated_at": "2024-01-25T16:20:00Z",
        "quality_reviewer": "data_team",
        "quality_review_notes": review_notes or f"Updated from {current_quality} to {new_quality_level}"
    })
    
    # Update name to reflect quality if premium
    new_name = dataset.name
    if new_quality_level == "premium" and "Premium" not in dataset.name:
        new_name = f"Premium {dataset.name}"
    elif new_quality_level != "premium" and "Premium" in dataset.name:
        new_name = dataset.name.replace("Premium ", "")
    
    # Update dataset
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        name=new_name,
        metadata=quality_metadata
    )
    
    print(f"Quality level updated: {current_quality} → {new_quality_level}")
    if new_name != dataset.name:
        print(f"Name updated: {dataset.name} → {new_name}")
    
    return updated_dataset

# Update quality level
updated = update_quality_level(
    "dataset_123456789",
    "premium",
    "Manual review completed, all logs verified for quality"
)
```

### Metadata Cleanup

```python theme={"system"}
def cleanup_dataset_metadata(dataset_id, remove_keys=None, standardize=True):
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    
    # Start with current metadata
    cleaned_metadata = dataset.metadata.copy()
    
    # Remove specified keys
    if remove_keys:
        for key in remove_keys:
            cleaned_metadata.pop(key, None)
    
    # Remove empty or null values
    cleaned_metadata = {k: v for k, v in cleaned_metadata.items() 
                       if v is not None and v != "" and v != []}
    
    if standardize:
        # Standardize common fields
        if "category" in cleaned_metadata:
            cleaned_metadata["category"] = cleaned_metadata["category"].lower().replace(" ", "_")
        
        if "purpose" in cleaned_metadata:
            cleaned_metadata["purpose"] = cleaned_metadata["purpose"].lower().replace(" ", "_")
        
        # Ensure required fields exist
        if "created_date" not in cleaned_metadata:
            cleaned_metadata["created_date"] = dataset.created_at[:10]  # YYYY-MM-DD
        
        if "last_cleanup" not in cleaned_metadata:
            cleaned_metadata["last_cleanup"] = "2024-01-25"
    
    # Update dataset
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        metadata=cleaned_metadata
    )
    
    print(f"Metadata cleanup completed:")
    print(f"  Original fields: {len(dataset.metadata)}")
    print(f"  Cleaned fields: {len(cleaned_metadata)}")
    print(f"  Removed: {len(dataset.metadata) - len(cleaned_metadata)} fields")
    
    return updated_dataset

# Cleanup metadata
updated = cleanup_dataset_metadata(
    "dataset_123456789",
    remove_keys=["temp_field", "debug_info", "old_version"],
    standardize=True
)
```

### Status Tracking

```python theme={"system"}
def update_dataset_status(dataset_id, status, status_notes=None):
    # Valid statuses
    valid_statuses = [
        "draft", "in_progress", "review", "approved", 
        "active", "archived", "deprecated"
    ]
    
    if status not in valid_statuses:
        raise ValueError(f"Status must be one of: {valid_statuses}")
    
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    current_status = dataset.metadata.get("status", "unknown")
    
    # Prepare status metadata
    status_metadata = dataset.metadata.copy()
    status_metadata.update({
        "status": status,
        "previous_status": current_status,
        "status_updated_at": "2024-01-25T16:20:00Z",
        "status_updated_by": "system",
        "status_notes": status_notes or f"Status changed from {current_status} to {status}"
    })
    
    # Update description to reflect status if archived or deprecated
    new_description = dataset.description
    if status in ["archived", "deprecated"]:
        status_prefix = f"[{status.upper()}] "
        if not new_description.startswith(status_prefix):
            new_description = status_prefix + new_description
    else:
        # Remove status prefixes if status is active
        for prefix in ["[ARCHIVED] ", "[DEPRECATED] "]:
            if new_description.startswith(prefix):
                new_description = new_description[len(prefix):]
    
    # Update dataset
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        description=new_description,
        metadata=status_metadata
    )
    
    print(f"Status updated: {current_status} → {status}")
    if status_notes:
        print(f"Notes: {status_notes}")
    
    return updated_dataset

# Update status
updated = update_dataset_status(
    "dataset_123456789",
    "approved",
    "Quality review completed, ready for production use"
)
```

### Automated Maintenance Updates

```python theme={"system"}
def perform_maintenance_update(dataset_id):
    from datetime import datetime
    
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    
    # Prepare maintenance metadata
    maintenance_metadata = dataset.metadata.copy()
    
    # Update maintenance fields
    maintenance_metadata.update({
        "last_maintenance": datetime.now().isoformat()[:10],
        "maintenance_version": maintenance_metadata.get("maintenance_version", 0) + 1,
        "health_check_passed": True,
        "metadata_validated": True,
        "backup_verified": True
    })
    
    # Calculate and update statistics
    if dataset.log_count > 0:
        # Estimate dataset health score
        health_score = 100
        
        # Deduct points for various issues
        if not dataset.description or len(dataset.description) < 20:
            health_score -= 10
        
        if len(dataset.metadata) < 3:
            health_score -= 15
        
        # Check age
        created = datetime.fromisoformat(dataset.created_at.replace('Z', '+00:00'))
        age_days = (datetime.now(created.tzinfo) - created).days
        if age_days > 180:  # Older than 6 months
            health_score -= 5
        
        maintenance_metadata["health_score"] = max(0, health_score)
        maintenance_metadata["size_category"] = (
            "small" if dataset.log_count < 100 else
            "medium" if dataset.log_count < 1000 else
            "large" if dataset.log_count < 10000 else
            "very_large"
        )
    
    # Update dataset
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        metadata=maintenance_metadata
    )
    
    print(f"Maintenance update completed for {dataset.name}")
    print(f"  Health score: {maintenance_metadata.get('health_score', 'N/A')}")
    print(f"  Size category: {maintenance_metadata.get('size_category', 'N/A')}")
    print(f"  Maintenance version: {maintenance_metadata['maintenance_version']}")
    
    return updated_dataset

# Perform maintenance update
updated = perform_maintenance_update("dataset_123456789")
```

## Error Handling

### Comprehensive Error Handling

```python theme={"system"}
from keywordsai.exceptions import (
    KeywordsAIError,
    NotFoundError,
    ValidationError,
    AuthenticationError,
    RateLimitError
)
import time

def update_dataset_with_retry(dataset_id, max_retries=3, **updates):
    for attempt in range(max_retries):
        try:
            updated_dataset = client.datasets.update(dataset_id, **updates)
            return updated_dataset
        except NotFoundError:
            print(f"Dataset {dataset_id} not found")
            return None  # Don't retry for not found
        except AuthenticationError:
            print("Authentication failed. Check your API key.")
            return None  # Don't retry for auth errors
        except ValidationError as e:
            print(f"Validation error: {e}")
            return None  # Don't retry for validation errors
        except RateLimitError:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Rate limited. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                print("Rate limit exceeded. Max retries reached.")
                return None
        except KeywordsAIError as e:
            print(f"API error (attempt {attempt + 1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(1)
            else:
                return None
        except Exception as e:
            print(f"Unexpected error (attempt {attempt + 1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(1)
            else:
                return None
    
    return None

# Use retry logic
updated = update_dataset_with_retry(
    "dataset_123456789",
    name="Retry Updated Dataset",
    metadata={"retry_update": True}
)
```

### Validation Before Update

```python theme={"system"}
def validate_update_data(name=None, description=None, metadata=None):
    errors = []
    
    # Validate name
    if name is not None:
        if not isinstance(name, str):
            errors.append("Name must be a string")
        elif len(name.strip()) < 3:
            errors.append("Name must be at least 3 characters long")
        elif len(name) > 100:
            errors.append("Name must be less than 100 characters")
    
    # Validate description
    if description is not None:
        if not isinstance(description, str):
            errors.append("Description must be a string")
        elif len(description) > 1000:
            errors.append("Description must be less than 1000 characters")
    
    # Validate metadata
    if metadata is not None:
        if not isinstance(metadata, dict):
            errors.append("Metadata must be a dictionary")
        elif len(str(metadata)) > 5000:  # Rough size check
            errors.append("Metadata is too large")
        
        # Check for reserved keys
        reserved_keys = ["id", "created_at", "updated_at", "log_count"]
        for key in reserved_keys:
            if key in metadata:
                errors.append(f"Cannot update reserved metadata key: {key}")
    
    return errors

def update_dataset_safely(dataset_id, **updates):
    # Validate input
    errors = validate_update_data(**updates)
    if errors:
        print("Validation errors:")
        for error in errors:
            print(f"  - {error}")
        return None
    
    # Perform update
    try:
        updated_dataset = client.datasets.update(dataset_id, **updates)
        return updated_dataset
    except Exception as e:
        print(f"Update failed: {e}")
        return None

# Safe update with validation
updated = update_dataset_safely(
    "dataset_123456789",
    name="Validated Update",
    description="This update has been validated",
    metadata={"validated": True}
)
```

## Best Practices

### Atomic Updates

```python theme={"system"}
def atomic_dataset_update(dataset_id, updates_dict):
    """Perform all updates in a single API call to ensure atomicity."""
    try:
        # Get current dataset for backup
        original_dataset = client.datasets.get(dataset_id)
        
        # Perform update
        updated_dataset = client.datasets.update(dataset_id, **updates_dict)
        
        print(f"Atomic update successful for {original_dataset.name}")
        print(f"  Updated fields: {list(updates_dict.keys())}")
        
        return updated_dataset
        
    except Exception as e:
        print(f"Atomic update failed: {e}")
        print(f"Dataset remains in original state")
        return None

# Atomic update
updated = atomic_dataset_update(
    "dataset_123456789",
    {
        "name": "Atomically Updated Dataset",
        "description": "Updated atomically",
        "metadata": {"atomic_update": True, "update_timestamp": "2024-01-25"}
    }
)
```

### Change Tracking

```python theme={"system"}
def update_with_changelog(dataset_id, changes_description, **updates):
    # Get current dataset
    current_dataset = client.datasets.get(dataset_id)
    
    # Prepare changelog metadata
    changelog_metadata = current_dataset.metadata.copy()
    
    # Add to existing metadata if provided
    if "metadata" in updates:
        changelog_metadata.update(updates["metadata"])
    
    # Add changelog information
    changelog_metadata.update({
        "last_change_description": changes_description,
        "last_change_timestamp": "2024-01-25T16:20:00Z",
        "change_count": changelog_metadata.get("change_count", 0) + 1
    })
    
    # Update the metadata in updates
    updates["metadata"] = changelog_metadata
    
    # Perform update
    updated_dataset = client.datasets.update(dataset_id, **updates)
    
    print(f"Update with changelog completed:")
    print(f"  Change: {changes_description}")
    print(f"  Total changes: {changelog_metadata['change_count']}")
    
    return updated_dataset

# Update with changelog
updated = update_with_changelog(
    "dataset_123456789",
    "Added quality annotations and updated categorization",
    name="Quality Annotated Dataset",
    metadata={"quality_annotations": True, "annotation_date": "2024-01-25"}
)
```

### Backup Before Update

```python theme={"system"}
def update_with_backup(dataset_id, **updates):
    # Get current dataset as backup
    backup_dataset = client.datasets.get(dataset_id)
    
    # Store backup information
    backup_info = {
        "original_name": backup_dataset.name,
        "original_description": backup_dataset.description,
        "original_metadata": backup_dataset.metadata.copy(),
        "backup_timestamp": "2024-01-25T16:20:00Z"
    }
    
    try:
        # Add backup info to metadata if metadata is being updated
        if "metadata" in updates:
            updates["metadata"]["backup_info"] = backup_info
        else:
            # Create new metadata with backup info
            new_metadata = backup_dataset.metadata.copy()
            new_metadata["backup_info"] = backup_info
            updates["metadata"] = new_metadata
        
        # Perform update
        updated_dataset = client.datasets.update(dataset_id, **updates)
        
        print(f"Update with backup completed")
        print(f"  Backup stored in metadata")
        
        return updated_dataset
        
    except Exception as e:
        print(f"Update failed: {e}")
        print(f"Original dataset information preserved")
        return None

# Update with backup
updated = update_with_backup(
    "dataset_123456789",
    name="Backup Protected Update",
    description="Updated with backup protection"
)
```

## Common Use Cases

### Dataset Lifecycle Management

```python theme={"system"}
def promote_dataset_to_production(dataset_id):
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    
    # Validate dataset is ready for production
    if dataset.log_count < 100:
        print("Dataset too small for production")
        return None
    
    if dataset.metadata.get("quality_level") not in ["high", "premium"]:
        print("Dataset quality not sufficient for production")
        return None
    
    # Update for production
    production_metadata = dataset.metadata.copy()
    production_metadata.update({
        "environment": "production",
        "promoted_at": "2024-01-25T16:20:00Z",
        "promoted_by": "data_team",
        "production_ready": True,
        "status": "active"
    })
    
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        name=f"[PROD] {dataset.name}",
        metadata=production_metadata
    )
    
    print(f"Dataset promoted to production: {updated_dataset.name}")
    return updated_dataset

# Promote to production
production_dataset = promote_dataset_to_production("dataset_123456789")
```

### Dataset Archival

```python theme={"system"}
def archive_dataset(dataset_id, archive_reason=None):
    # Get current dataset
    dataset = client.datasets.get(dataset_id)
    
    # Prepare archive metadata
    archive_metadata = dataset.metadata.copy()
    archive_metadata.update({
        "status": "archived",
        "archived_at": "2024-01-25T16:20:00Z",
        "archived_by": "system",
        "archive_reason": archive_reason or "Routine archival",
        "original_status": dataset.metadata.get("status", "active")
    })
    
    # Update dataset
    updated_dataset = client.datasets.update(
        dataset_id=dataset_id,
        name=f"[ARCHIVED] {dataset.name}",
        description=f"[ARCHIVED] {dataset.description}",
        metadata=archive_metadata
    )
    
    print(f"Dataset archived: {updated_dataset.name}")
    print(f"Reason: {archive_reason or 'Routine archival'}")
    
    return updated_dataset

# Archive dataset
archived = archive_dataset(
    "dataset_123456789",
    "Dataset no longer needed for current projects"
)
```