Skip to main content

Deployment

Introduction​

This documentation outlines the deployment strategy for Noora Health's Intent Recognition System, specifically designed to enhance user communication via the TURN platform. The system operates through a webhook mechanism that triggers the Intent Recognition System upon receiving new messages, ensuring each is processed and acted upon based on its categorized intent.

System Overview​

The Intent Recognition System is configured within the TURN platform. It leverages webhook triggers to initiate intent recognition processes for incoming messages. This setup aims to automate the categorization and appropriate handling of user queries, focusing on streamlining responses to non-medical inquiries and efficiently escalating medical-related queries.

Deployment Process​

Prerequisites​

  • TURN platform with operational webhook functionality.
  • Access to the GPT-4 API for intent recognition tasks.
  • Predefined sets of few-shot examples and response templates for various intent categories.

Configuration Steps​

Step 1: TURN Platform Setup​

  • Objective: Prepare the TURN platform to interface with the Intent Recognition System via webhooks.
  • Actions:
    • Ensure that TURN's webhook functionality is enabled and properly configured to interact with the external Intent Recognition System.
    • Validate TURN API keys and webhook permissions for secure and reliable data exchange.

Step 2: Intent Recognition System Integration​

  • Objective: Integrate the Intent Recognition System with TURN, enabling automated intent analysis upon message reception.
  • Actions:
    • Configure the webhook in TURN to trigger the Intent Recognition System whenever a new message is received.
    • Load the Intent Recognition System with predefined few-shot examples and response templates to facilitate accurate intent recognition.

Step 3: Multilingual and Multi-Intent Configuration​

  • Objective: Ensure the system can accurately process messages in multiple languages and categorize them into predefined intents.
  • Actions:
    • Implement and configure a multilingual model within the Intent Recognition System to handle messages across various languages.
    • Test the system with sample messages in different languages to fine-tune the multilingual model and ensure accurate intent recognition.

Step 4: Testing and Validation​

  • Objective: Conduct comprehensive testing to ensure the system accurately categorizes messages and triggers the appropriate responses or escalations.
  • Actions:
    • Perform end-to-end testing by simulating incoming messages that cover all predefined intent categories.
    • Assess the system's response accuracy and tweak the intent recognition logic as needed to improve performance.

Step 5: Deployment and Monitoring​

  • Objective: Deploy the webhook-triggered Intent Recognition System in a live environment and set up monitoring for continuous performance evaluation.
  • Actions:
    • Implement the system in the live TURN environment, enabling real-time processing of user messages.
    • Utilize monitoring tools to track the system's performance, focusing on the accuracy of intent recognition and the effectiveness of automated responses or query escalations.

Post-Deployment​

  • Continuous Optimization: Regularly review system performance data and user feedback to optimize intent recognition accuracy and response effectiveness.
  • Scalability and Expansion: Plan for future scalability to accommodate an increasing volume of messages and the potential inclusion of more languages and intents.

Conclusion​

The deployment of the Intent Recognition System through a webhook on the TURN platform represents a significant step forward in Noora Health's ability to efficiently categorize and respond to user messages. This system ensures that MSEs can focus their efforts on providing high-quality, empathetic responses to medical inquiries, thereby enhancing overall user satisfaction and operational efficiency.