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Overview

The get method allows you to retrieve detailed information about a specific experiment, including its configuration, variants, current status, and results.

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Method Signature

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Synchronous

def get(experiment_id: str) -> Dict[str, Any]

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Asynchronous

async def get(experiment_id: str) -> Dict[str, Any]

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Parameters

ParameterTypeRequiredDescription
experiment_idstrYesThe unique identifier of the experiment

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Returns

Returns a dictionary containing detailed experiment information including configuration, variants, metrics, and current status.

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Examples

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Basic Retrieval

from keywordsai import KeywordsAI

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

# Get experiment details
experiment = client.experiments.get("exp_123")

print(f"Experiment: {experiment['name']}")
print(f"Status: {experiment['status']}")
print(f"Variants: {len(experiment['variants'])}")
print(f"Created: {experiment['created_at']}")

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Accessing Experiment Details

# Get comprehensive experiment information
experiment = client.experiments.get("exp_123")

print(f"Experiment ID: {experiment['id']}")
print(f"Name: {experiment['name']}")
print(f"Description: {experiment['description']}")
print(f"Status: {experiment['status']}")

# Variant information
print("\nVariants:")
for variant in experiment['variants']:
    print(f"- {variant['name']}: {variant['description']}")
    print(f"  Prompt ID: {variant['prompt_id']}")
    if 'traffic_percentage' in variant:
        print(f"  Traffic: {variant['traffic_percentage']}%")

# Metrics being tracked
if 'metrics' in experiment:
    print(f"\nTracked Metrics: {', '.join(experiment['metrics'])}")

# Traffic split
if 'traffic_split' in experiment:
    print("\nTraffic Split:")
    for variant_name, percentage in experiment['traffic_split'].items():
        print(f"- {variant_name}: {percentage * 100}%")

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Checking Experiment Status

# Check if experiment is ready to start
experiment = client.experiments.get("exp_123")

status = experiment['status']
print(f"Current status: {status}")

if status == 'draft':
    print("Experiment is in draft mode - ready to start")
    print(f"Variants configured: {len(experiment['variants'])}")
elif status == 'running':
    print(f"Experiment is running since: {experiment['started_at']}")
    if 'current_results' in experiment:
        print("Current results available")
elif status == 'completed':
    print(f"Experiment completed at: {experiment['completed_at']}")
    print("Final results available")
elif status == 'paused':
    print(f"Experiment paused at: {experiment['paused_at']}")
    print("Can be resumed")

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Analyzing Experiment Configuration

# Analyze experiment setup
experiment = client.experiments.get("exp_123")

print(f"Experiment Analysis: {experiment['name']}")
print("=" * 50)

# Validate traffic split
if 'traffic_split' in experiment:
    total_traffic = sum(experiment['traffic_split'].values())
    print(f"Total traffic allocation: {total_traffic * 100}%")
    
    if abs(total_traffic - 1.0) > 0.001:
        print("⚠️  Warning: Traffic split doesn't sum to 100%")
    else:
        print("✅ Traffic split is valid")

# Check variant configuration
variant_names = [v['name'] for v in experiment['variants']]
print(f"\nVariants ({len(variant_names)}): {', '.join(variant_names)}")

# Check if all variants have required fields
for variant in experiment['variants']:
    if 'prompt_id' not in variant:
        print(f"⚠️  Warning: Variant '{variant['name']}' missing prompt_id")
    else:
        print(f"✅ Variant '{variant['name']}' properly configured")

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Error Handling for Missing Experiments

try:
    experiment = client.experiments.get("exp_nonexistent")
    print(f"Found experiment: {experiment['name']}")
except Exception as e:
    if "not found" in str(e).lower():
        print("Experiment does not exist")
    elif "access denied" in str(e).lower():
        print("You don't have permission to access this experiment")
    else:
        print(f"Error retrieving experiment: {e}")

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Asynchronous Usage

import asyncio
from keywordsai import AsyncKeywordsAI

async def get_experiment_example():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    try:
        experiment = await client.experiments.get("exp_123")
        
        print(f"Retrieved experiment: {experiment['name']}")
        print(f"Status: {experiment['status']}")
        
        return experiment
    except Exception as e:
        print(f"Error: {e}")
        return None

asyncio.run(get_experiment_example())

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Accessing Current Results

# Get experiment with current results (if running or completed)
experiment = client.experiments.get("exp_123")

if experiment['status'] in ['running', 'completed']:
    if 'current_results' in experiment:
        results = experiment['current_results']
        
        print("Current Results:")
        print(f"Total requests: {results.get('total_requests', 0)}")
        print(f"Start time: {results.get('start_time')}")
        
        # Variant performance
        if 'variant_results' in results:
            print("\nVariant Performance:")
            for variant_name, metrics in results['variant_results'].items():
                print(f"\n{variant_name}:")
                print(f"  Requests: {metrics.get('request_count', 0)}")
                print(f"  Success rate: {metrics.get('success_rate', 0):.2%}")
                print(f"  Avg response time: {metrics.get('avg_response_time', 0):.2f}ms")
                
                # Custom metrics
                for metric_name, value in metrics.items():
                    if metric_name not in ['request_count', 'success_rate', 'avg_response_time']:
                        print(f"  {metric_name}: {value}")
else:
    print(f"No results available for {experiment['status']} experiment")

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Batch Retrieval

# Get multiple experiments
experiment_ids = ["exp_123", "exp_456", "exp_789"]
experiments = []

for exp_id in experiment_ids:
    try:
        experiment = client.experiments.get(exp_id)
        experiments.append(experiment)
        print(f"✅ Retrieved: {experiment['name']}")
    except Exception as e:
        print(f"❌ Failed to get {exp_id}: {e}")

print(f"\nSuccessfully retrieved {len(experiments)} experiments")

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Asynchronous Batch Retrieval

import asyncio
from keywordsai import AsyncKeywordsAI

async def get_experiments_batch(experiment_ids):
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    async def get_single_experiment(exp_id):
        try:
            return await client.experiments.get(exp_id)
        except Exception as e:
            print(f"Error getting {exp_id}: {e}")
            return None
    
    # Get all experiments concurrently
    tasks = [get_single_experiment(exp_id) for exp_id in experiment_ids]
    results = await asyncio.gather(*tasks)
    
    # Filter out None results
    experiments = [exp for exp in results if exp is not None]
    
    print(f"Retrieved {len(experiments)} out of {len(experiment_ids)} experiments")
    return experiments

# Usage
experiment_ids = ["exp_123", "exp_456", "exp_789"]
experiments = asyncio.run(get_experiments_batch(experiment_ids))

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Experiment Validation

# Validate experiment configuration
def validate_experiment(experiment):
    issues = []
    
    # Check required fields
    required_fields = ['id', 'name', 'variants', 'status']
    for field in required_fields:
        if field not in experiment:
            issues.append(f"Missing required field: {field}")
    
    # Check variants
    if 'variants' in experiment:
        if len(experiment['variants']) < 2:
            issues.append("Experiment should have at least 2 variants")
        
        variant_names = [v['name'] for v in experiment['variants']]
        if len(variant_names) != len(set(variant_names)):
            issues.append("Duplicate variant names found")
    
    # Check traffic split
    if 'traffic_split' in experiment:
        total = sum(experiment['traffic_split'].values())
        if abs(total - 1.0) > 0.001:
            issues.append(f"Traffic split sums to {total:.3f}, should be 1.0")
    
    return issues

# Validate an experiment
experiment = client.experiments.get("exp_123")
issues = validate_experiment(experiment)

if issues:
    print("Validation Issues:")
    for issue in issues:
        print(f"- {issue}")
else:
    print("✅ Experiment configuration is valid")

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Export Experiment Details

import json
from datetime import datetime

# Export experiment to JSON file
experiment = client.experiments.get("exp_123")

# Create filename with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"experiment_{experiment['id']}_{timestamp}.json"

# Export with pretty formatting
with open(filename, 'w', encoding='utf-8') as f:
    json.dump(experiment, f, indent=2, ensure_ascii=False)

print(f"Experiment exported to {filename}")
print(f"File size: {len(json.dumps(experiment))} characters")

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Error Handling

try:
    experiment = client.experiments.get("exp_123")
    print(f"Retrieved experiment: {experiment['name']}")
except Exception as e:
    error_msg = str(e).lower()
    
    if "not found" in error_msg:
        print("Experiment not found - check the experiment ID")
    elif "access denied" in error_msg or "permission" in error_msg:
        print("Access denied - you don't have permission to view this experiment")
    elif "invalid" in error_msg:
        print("Invalid experiment ID format")
    else:
        print(f"Unexpected error: {e}")

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Experiment Structure

A retrieved experiment contains:
  • id: Unique experiment identifier
  • name: Experiment name
  • description: Optional description
  • status: Current status (draft, running, completed, paused)
  • variants: List of variants being tested
  • metrics: Metrics being tracked
  • traffic_split: Traffic distribution between variants
  • created_at: Creation timestamp
  • created_by: User who created the experiment
  • started_at: When experiment was started (if applicable)
  • completed_at: When experiment was completed (if applicable)
  • current_results: Current performance data (if running/completed)
  • metadata: Custom metadata

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Best Practices

  • Always handle the case where an experiment might not exist
  • Check experiment status before performing status-dependent operations
  • Validate experiment configuration before starting
  • Use async methods for better performance when retrieving multiple experiments
  • Cache experiment data when appropriate to reduce API calls

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Common Use Cases

  • Displaying experiment details in a dashboard
  • Validating experiment configuration before starting
  • Monitoring experiment progress and results
  • Exporting experiment data for analysis
  • Checking experiment status before performing operations
  • Retrieving experiment metadata for reporting
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