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The A/B Testing Experiment Management API is a crucial component of the Sophra System’s analytics and optimization infrastructure. This API provides a robust interface for creating, retrieving, and managing A/B testing experiments, enabling data-driven decision-making and continuous improvement of user experiences. Built on Next.js 14 with TypeScript, it leverages the power of Prisma ORM for database interactions and integrates seamlessly with Sophra’s logging and monitoring systems. At its core, this API serves as the backbone for Sophra’s experimentation framework, allowing for the systematic testing of hypotheses and measurement of their impact on key business metrics. It’s designed to handle multiple concurrent experiments, each with its own set of variants and target metrics. The API’s architecture emphasizes scalability and reliability, crucial for supporting large-scale A/B testing operations across diverse client applications. One of the key architectural decisions in this component is the use of Zod for request validation. This ensures that all incoming data adheres to a predefined schema, significantly reducing the risk of data inconsistencies and enhancing the overall robustness of the system. The validation schema is comprehensive, covering all aspects of an experiment’s configuration, including variant definitions and target metrics. Performance is a critical consideration in the design of this API. It utilizes efficient database queries through Prisma, minimizing response times even when dealing with large volumes of experiment data. The API also implements error handling strategies that provide detailed feedback for debugging while maintaining security by not exposing sensitive information in production environments. A unique feature of this API is its flexibility in experiment configuration. It allows for dynamic definition of variants and their associated weights, as well as custom configuration objects for each variant. This flexibility enables sophisticated experimentation strategies, supporting everything from simple A/B tests to complex multivariate experiments.

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Exported Components

const ExperimentCreateSchema = z.object({
  name: z.string(),
  description: z.string().optional(),
  startDate: z.string().datetime(),
  endDate: z.string().datetime(),
  configuration: z.object({
    variants: z.array(
      z.object({
        id: z.string(),
        name: z.string(),
        weight: z.number().min(0).max(1),
        config: z.record(z.unknown()),
      })
    ),
    targetMetrics: z.array(z.string()),
  }),
});
The ExperimentCreateSchema defines the structure for creating new experiments:
  • name: String, required - The name of the experiment
  • description: Optional string - A description of the experiment
  • startDate: ISO 8601 datetime string - The start date of the experiment
  • endDate: ISO 8601 datetime string - The end date of the experiment
  • configuration: Object containing:
    • variants: Array of variant objects, each with:
      • id: String - Unique identifier for the variant
      • name: String - Name of the variant
      • weight: Number between 0 and 1 - Traffic allocation weight
      • config: Record of unknown type - Custom configuration for the variant
    • targetMetrics: Array of strings - Metrics to be measured in the experiment
The GET and POST functions handle retrieving and creating experiments, respectively.

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Implementation Examples

const response = await fetch('/api/nous/ab-testing/experiments?status=ACTIVE');
const { success, data } = await response.json();
if (success) {
  console.log('Active experiments:', data);
}

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Sophra Integration Details

The A/B Testing Experiment Management API integrates with several Sophra components:
  1. Database Integration: Uses Prisma client for database operations.
  2. Logging: Utilizes Sophra’s shared logger for consistent log formatting.
  3. Error Handling: Implements standardized error responses.
  4. Authentication: (Not implemented in this file, but typically handled by middleware)

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Error Handling

The API implements comprehensive error handling:
  • Database connection errors
  • Invalid query parameters
  • Unexpected server errors
catch (error) {
  logger.error("Failed to fetch experiments", {
    error: error instanceof Error ? { message: error.message, stack: error.stack } : error,
    operation: "GET",
  });
  return NextResponse.json(
    { success: false, error: "Failed to fetch experiments" },
    { status: 500 }
  );
}
  • Invalid request body (caught by Zod validation)
  • Database insertion errors
  • Unexpected server errors
if (!validation.success) {
  logger.info("Invalid experiment creation request", {
    errors: validation.error.format(),
    component: COMPONENT,
    operation: "POST.validate",
  });
  return NextResponse.json(
    {
      success: false,
      error: "Invalid request format",
      details: validation.error.format(),
    },
    { status: 400 }
  );
}

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Performance Considerations

  • Uses efficient Prisma queries for database operations
  • Implements request validation to prevent unnecessary database calls
  • Potential for implementing caching mechanisms for frequently accessed experiments
Consider implementing Redis caching for active experiments to reduce database load and improve response times.

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Security Implementation

While not explicitly implemented in this file, the API should be protected by:
  • JWT authentication middleware
  • Role-based access control for experiment management
  • Rate limiting to prevent abuse

Security Recommendation

Implement API key validation for service-to-service communication and JWT validation for user-initiated requests.

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Configuration

The API relies on the following configuration:
  • Database connection string (managed by Prisma)
  • Logger configuration (imported from shared library)
  • Environment-specific settings (e.g., development vs. production error handling)
DATABASE_URL="postgresql://username:password@localhost:5432/sophra"
NODE_ENV="development"
This API forms a critical part of Sophra’s experimentation infrastructure, enabling data-driven decision-making and continuous optimization of user experiences.
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