Here are the six distinct microservices that would form the core of your backend, designed for independent development, scaling, and maintenance.
- Primary Responsibility: To ingest the real-time audio stream from the client and convert it into a text transcript. This service is a dedicated gateway to Google's speech recognition engine.
- Key Technologies: It would primarily interact with the Google Cloud Speech-to-Text API, configured with the specialized medical model for high accuracy.
- Primary Responsibility: To perform all Natural Language Processing tasks on the transcribed text. It acts as the "comprehension" engine of the system.
- Key Functions:
- Entity Recognition: Identifies medical terms, medications, symptoms, and measurements.
- Insight Generation: Creates the real-time "Key Insights" summaries.
- Grammar & Tense Analysis: Runs custom logic to check for grammatical consistency.
- Key Technologies: It would heavily use the Google Cloud Natural Language API and custom-trained models.
- Primary Responsibility: To act as a fast, queryable database for all structured information like medication names and medical term definitions.
- Key Functions:
- Provides multi-faceted medication data (brand, generic, aliases).
- Serves definitions, translations, and image URLs for medical terms.
- Handles measurement conversions (kg to lbs, cm to ft/in).
- Key Technologies: A high-performance database like Firestore or a managed SQL/NoSQL database with a simple API in front of it.
- Primary Responsibility: To process the raw audio stream to extract vocal metrics, focusing on how things are said, not what is said.
- Key Functions: Analyzes the audio for pace (words per minute), pitch, tonal variations, and clarity.
- Key Technologies: Custom audio processing libraries (e.g., Librosa in Python) running in a containerized environment like Cloud Run.
- Primary Responsibility: To generate the final, post-session Quality Assurance report by orchestrating data and calling the specialized LLM.
- Key Functions:
- Gathers relevant data (transcript snippets, grammar analysis, acoustic metrics).
- Sends the data to the specialized, ethics-trained LLM using the "interprecoach-QA feedback" prompt.
- Formats the LLM's response into the final user-facing report.
- Key Technologies: Interacts directly with your custom-trained LLM on Vertex AI.
- Primary Responsibility: To manage the lifecycle of an interpreting session and enforce all data privacy and security rules.
- Key Functions:
- Handles session initiation and authentication.
- Orchestrates calls to the other microservices.
- Enforces the ephemeral storage and auto-deletion policies for all sensitive data.
- Key Technologies: A lightweight web framework running in Cloud Functions or Cloud Run, interacting with a cache like Memorystore (Redis) for managing session state.