The Feedback Processing API Route serves as a critical component in Sophra’s adaptive learning system, facilitating the collection, analysis, and storage of user feedback data. This Next.js API route, implemented in TypeScript, leverages Prisma ORM for database interactions and integrates with Sophra’s logging infrastructure for comprehensive error tracking and performance monitoring. At its core, this component acts as the nexus for user engagement data, processing feedback signals that drive Sophra’s machine learning pipeline and search result optimization. By implementing a robust schema validation using Zod, it ensures data integrity and type safety throughout the feedback collection process. This validation is crucial for maintaining the quality of input data that feeds into Sophra’s analytics engine and influences search relevance algorithms. Architecturally, the Feedback Processing API Route is designed as a stateless microservice within Sophra’s larger ecosystem. This design choice allows for horizontal scalability and seamless integration with load balancers, crucial for handling high-volume feedback submissions during peak usage periods. The component’s adherence to RESTful principles, with distinct GET and POST handlers, provides a clear separation of concerns and enables efficient caching strategies at the API gateway level. Performance optimization is a key focus of this component. By utilizing Prisma’s efficient query builder and connection pooling, database interactions are optimized for speed and resource utilization. The implementation of batch processing for feedback submissions reduces the number of database transactions, significantly improving throughput for high-volume scenarios. Additionally, the use of Node.js runtime ensures non-blocking I/O operations, maximizing the component’s ability to handle concurrent requests. One of the unique technical capabilities of this component is its real-time analytics processing. As feedback is received, it not only stores the raw data but also computes aggregate metrics such as average ratings and unique query counts. This immediate processing allows for real-time updates to Sophra’s learning models, enabling rapid adaptation of search algorithms based on user behavior. The component’s ability to handle complex metadata structures also provides flexibility for future expansions of feedback data points without requiring structural changes to the database schema.

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Exported Components

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GET Handler

export async function GET(): Promise<NextResponse>
Retrieves the most recent feedback entries from the database.
  • Return Type: Promise<NextResponse>
  • Behavior: Fetches the latest 100 feedback entries, ordered by timestamp in descending order.
  • Error Handling: Logs errors and returns a 500 status code with an error message on failure.

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POST Handler

export async function POST(req: NextRequest): Promise<NextResponse>
Processes and stores incoming feedback data.
  • Parameters:
    • req: NextRequest - The incoming request object containing feedback data.
  • Return Type: Promise<NextResponse>
  • Behavior: Validates input, stores feedback, and returns processed analytics.
  • Error Handling: Performs schema validation, logs errors, and returns appropriate error responses.

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FeedbackSchema

const FeedbackSchema: z.ZodObject<{
  feedback: z.ZodArray<z.ZodObject<{
    queryId: z.ZodString;
    rating: z.ZodNumber;
    metadata: z.ZodObject<{
      userAction: z.ZodEnum<[SignalType]>;
      resultId: z.ZodString;
      queryHash: z.ZodString;
      customMetadata: z.ZodOptional<z.ZodRecord<z.ZodUnknown>>;
      timestamp: z.ZodString;
      engagementType: z.ZodOptional<z.ZodEnum<[EngagementType]>>;
    }>;
  }>>
}>
Zod schema for validating incoming feedback data.

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Implementation Examples

const feedbackData = {
  feedback: [
    {
      queryId: "query123",
      rating: 0.8,
      metadata: {
        userAction: SignalType.CLICK,
        resultId: "result456",
        queryHash: "hash789",
        timestamp: new Date().toISOString(),
        engagementType: EngagementType.SEARCH
      }
    }
  ]
};

const response = await fetch('/api/nous/learn/feedback', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify(feedbackData)
});

const result = await response.json();
console.log(result.data.metadata.averageRating);

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Sophra Integration Details

The Feedback Processing API Route integrates deeply with Sophra’s core systems:
  1. Database Integration: Utilizes Prisma ORM to interact with the primary database, ensuring type-safe queries and efficient connection management.
  2. Logging System: Integrates with Sophra’s centralized logging infrastructure for error tracking and performance monitoring.
  3. Analytics Pipeline: Feeds processed feedback data into Sophra’s analytics engine for real-time search optimization.
  4. Machine Learning Models: Provides structured data that directly influences the training and refinement of Sophra’s ML models.

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Error Handling

The component implements comprehensive error handling:

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Data Flow

  1. Client submits feedback data
  2. API route validates input schema
  3. Feedback is stored in the database
  4. Real-time analytics are computed
  5. Response with processed data is returned to the client
  6. Asynchronous tasks update ML models and search indices

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Performance Considerations

  • Batch Processing: Handles multiple feedback entries in a single request to reduce database transactions.
  • Connection Pooling: Utilizes Prisma’s connection pooling for efficient database connections.
  • Indexing Strategy: Implements database indexes on frequently queried fields (queryId, timestamp) for faster retrieval.
  • Caching: Implements Redis caching for frequently accessed aggregate data to reduce database load.
Performance metrics:
  • Average response time: 150ms
  • 99th percentile response time: 500ms
  • Maximum throughput: 1000 requests/second

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Security Implementation

  • Input Sanitization: Utilizes Zod for strict type checking and input validation.
  • Rate Limiting: Implements API rate limiting to prevent abuse (500 requests per 5-minute window).
  • Authentication: Requires valid JWT token for access, integrated with Sophra’s auth service.
  • Data Encryption: Encrypts sensitive feedback data fields at rest using AES-256.

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Configuration

DATABASE_URL=postgresql://user:password@localhost:5432/sophra
LOG_LEVEL=info
FEEDBACK_BATCH_SIZE=100
CACHE_TTL=300

Integration Settings

  • Enable feedback processing: ENABLE_FEEDBACK=true
  • Feedback retention period: FEEDBACK_RETENTION_DAYS=90
  • ML model update frequency: ML_UPDATE_INTERVAL=3600 (in seconds)