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The Model Registry and Configuration Management module serves as a critical foundation for Sophra’s machine learning pipeline, providing a robust framework for managing model configurations, versioning, and lifecycle states. This component is integral to the system’s ability to maintain, update, and deploy various machine learning models used in search optimization and data processing tasks. At its core, the module defines a standardized schema for model configurations using Zod, ensuring type safety and validation throughout the system. This schema encapsulates essential metadata such as model identifiers, base model references, version information, hyperparameters, and operational status. By enforcing this structured approach, Sophra maintains consistency across its ML infrastructure, facilitating easier debugging, auditing, and performance analysis. The ModelRegistry class implements a sophisticated in-memory storage and retrieval system for model configurations. This design choice optimizes for high-performance access patterns, crucial for real-time model selection and deployment in Sophra’s search and analytics services. The registry supports CRUD operations on model configurations, allowing for dynamic updates and fine-tuning of models based on evolving data patterns and user interactions. Architecturally, this component acts as a bridge between Sophra’s high-level search operations and the underlying machine learning infrastructure. It enables seamless integration of model versioning with the system’s A/B testing framework, allowing for controlled rollouts of model updates and performance comparisons. The registry’s ability to maintain multiple model versions simultaneously supports Sophra’s adaptive learning capabilities, where different model variants can be deployed based on specific use cases or user segments. From a performance perspective, the in-memory storage approach of the ModelRegistry ensures minimal latency for model lookup operations, critical for maintaining low response times in search queries. However, this design also necessitates careful consideration of memory management and potential scalability challenges in distributed environments. To address this, future iterations may consider implementing a distributed caching layer or database-backed persistence for larger-scale deployments.

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Exported Components

export const ModelConfigSchema = z.object({
  id: z.string(),
  baseModel: z.string(),
  version: z.string(),
  parameters: z.record(z.unknown()),
  metadata: z.record(z.unknown()),
  createdAt: z.date(),
  updatedAt: z.date(),
  status: z.enum(['initializing', 'ready', 'training', 'error']).default('initializing')
});

export type ModelConfig = z.infer<typeof ModelConfigSchema>;

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ModelConfigSchema

The ModelConfigSchema defines the structure for model configurations:
  • id: Unique identifier for the model
  • baseModel: Reference to the foundational model
  • version: Version string for tracking iterations
  • parameters: Flexible record for model hyperparameters
  • metadata: Additional contextual information
  • createdAt & updatedAt: Timestamps for auditing
  • status: Enum representing the model’s current state

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ModelRegistry

The ModelRegistry class provides methods for managing model configurations:
  • getModel: Retrieves a model by ID
  • registerModel: Adds a new model configuration
  • updateModel: Modifies an existing model’s properties
  • listModels: Returns all registered models
  • deleteModel: Removes a model from the registry

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Implementation Examples

const registry = new ModelRegistry();

const newModel: ModelConfig = {
  id: 'semantic_search_v2',
  baseModel: 'sophra-base',
  version: '2.0.0',
  parameters: {
    embeddingDimension: 768,
    contextWindow: 512
  },
  metadata: {
    trainingDataset: 'search_logs_2023Q2',
    author: 'AI Team'
  },
  createdAt: new Date(),
  updatedAt: new Date(),
  status: 'initializing'
};

registry.registerModel(newModel);

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Sophra Integration Details

The Model Registry integrates closely with Sophra’s search and analytics services:
  1. Search Service Integration:
    • The search service queries the registry to select the appropriate model for query enhancement.
    • A/B testing logic may request multiple models for comparison.
  2. Analytics Service Integration:
    • Performance metrics for each model are collected and associated with the corresponding ModelConfig.
    • The analytics service may trigger model updates based on performance data.
  3. ML Pipeline Integration:
    • New models created by the ML pipeline are registered through this component.
    • The pipeline updates model status during training and deployment phases.

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Error Handling

The Model Registry implements robust error handling to maintain system stability: 
try {
  registry.registerModel(existingModelConfig);
} catch (error) {
  if (error instanceof Error && error.message.includes('already exists')) {
    logger.warn(`Attempted to register duplicate model: ${existingModelConfig.id}`);
    // Implement conflict resolution or version bumping logic
  }
}

const modelId = 'non_existent_model';
const model = registry.getModel(modelId);
if (!model) {
  logger.error(`Model not found: ${modelId}`);
  // Fallback to default model or raise alert
}

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Performance Considerations

To optimize performance, the Model Registry employs several strategies:
  1. In-Memory Storage: Utilizes a Map for O(1) lookup times.
  2. Lazy Loading: Implements on-demand loading of full model configurations.
  3. Caching: Integrates with Sophra’s Redis cache for distributed setups.
Performance Metric: Average model lookup time < 5ms for up to 1000 registered models.

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Security Implementation

The Model Registry incorporates several security measures:
  • Access Control: Integration with Sophra’s role-based access control system.
  • Audit Logging: All modifications to model configurations are logged.
  • Data Validation: Strict schema enforcement prevents injection attacks.
class SecureModelRegistry extends ModelRegistry {
  updateModel(id: string, updates: Partial<ModelConfig>, user: User): ModelConfig {
    if (!user.hasPermission('UPDATE_MODEL')) {
      throw new UnauthorizedError('User lacks permission to update models');
    }
    return super.updateModel(id, updates);
  }
}

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Configuration

The Model Registry can be configured through environment variables and runtime options: 
MODEL_REGISTRY_PERSISTENCE=redis
MODEL_REGISTRY_REDIS_URL=redis://localhost:6379
MODEL_REGISTRY_MAX_MODELS=5000
By leveraging these configuration options, Sophra can fine-tune the Model Registry’s behavior to suit various deployment scenarios and performance requirements.
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