Technical Plan for MVP Development

1. MVP Overview

Objective: Build a platform that offers users with an interactive AI-powered experience focused on psychology and therapy. The platform will include:

• A curated LLM designed for mental health insights and therapeutic resources.

• Specialist chat and voice modes to enhance user interaction and provide a natural, supportive experience.

The MVP will be built with scalability, security, and user experience at the forefront.

2. Architecture Overview

The system will follow a microservices architecture to maintain modularity and flexibility, consisting of the following key components:

• Front End: User interface for web and mobile.

• Back End: API layer, authentication, and business logic.

• LLM Integration: Engine to handle the psychology and therapy language model.

• Voice and Chat Specialist Modes: Features to enable natural, conversational interactions.

Front End Development

Purpose: Provide an intuitive, responsive, and engaging interface for users to interact with the AI in both chat and voice modes.

• Framework: We’ll use React for the web app and React Native for mobile. These frameworks offer component-based architectures, enhancing maintainability and scalability.

• State Management: Redux (or Recoil for a simpler solution) to manage global state for user sessions, preferences, and LLM responses.

• UI/UX Design:

  • Material UI for web components for a clean, professional look.

  • Custom Components for chat bubbles, voice icons, and modal dialogs.

  • Accessibility will be considered throughout, especially for mental health use cases.

• Key Features:

  • Chat Interface: A structured, conversational UI where users can type and receive responses from the LLM.

  • Voice Mode: Toggle button to activate voice interactions, which integrates with Web Speech API or similar technology to process spoken input and respond with synthesized speech.

• APIs for Voice Processing:

  • Web Speech API for browser-based voice recognition and synthesis (fallback options like Speechly or Deepgram if more control is needed).

  • Speech Synthesis Markup Language (SSML) to improve the quality and tone of AI-generated speech.

Back End Development

Purpose: Handle business logic, manage data storage, and connect with the LLM for processing user inputs.

• Framework: Node.js with Express.js for handling HTTP requests and creating a RESTful API.

• Database:

  • MongoDB for storing user profiles, session data, and user interaction logs (as JSON-like documents).

  • Redis (optional) for caching frequently requested data and reducing response latency.

• Authentication and Authorization:

  • JWT (JSON Web Tokens) for secure, stateless authentication.

  • OAuth 2.0 for potential integrations with social login (e.g., Google or LinkedIn).

• Data Storage and Security:

  • Encryption: User data is encrypted in transit (TLS) and at rest.

  • GDPR Compliance: Implement data handling processes to comply with privacy regulations, especially considering the sensitivity of psychological data.

Endpoints:

• /chat: Handles user messages, sending them to the LLM, and returning the response.

• /voice: Accepts audio input, processes it, and sends it to the LLM for response.

• /history: Stores or retrieves past interactions for user reference.

LLM Integration (Curated for Psychology and Therapy)

Purpose: Power the conversation with an LLM model that provides responses grounded in psychological knowledge and therapeutic support.

• Model Selection: LLaMA (7B or 13B) or similar open-source model fine-tuned on psychology-related texts, therapeutic dialogues, and empathetic language.

• Deployment:

  • Hugging Face Transformers: Use the Transformers library to load and interact with the model.

  • Containerization with Docker: Deploy the model within Docker containers for portability and efficient scaling.

• Hosting:

  • AWS Sagemaker for managed model deployment, allowing on-demand scaling.

  • GPU Instances (such as AWS EC2 instances with GPU) to handle the compute-intensive tasks of LLM inference.

• Fine-Tuning and Specialized Dataset:

  • Data Sources: Use licensed datasets relevant to psychology, open-source therapy transcripts, and mental health literature.

  • Techniques: Supervised fine-tuning on dialogues to improve response accuracy, intent recognition, and empathetic tone.

Specialist Features

1. Chat Mode

• Natural Language Processing:

  • Intent Recognition: Classify user intents (e.g., “seeking advice,” “venting”) for more contextually appropriate responses.

  • Emotion Detection: Implement basic emotion detection algorithms to tailor responses (e.g., offering empathy when sadness is detected).

• Response Generation:

  • Contextual Awareness: Ensure the model retains some conversation context within each session.

  • Content Moderation: Filters to detect and handle potentially sensitive content.

• Logging and Analytics:

  • Store conversation histories (with user consent) for reviewing user interactions and continuously improving the model.

2. Voice Mode

• Voice-to-Text and Text-to-Voice Processing:

  • Voice Recognition: Integrate with Google Cloud Speech-to-Text or Azure Cognitive Services for accurate transcription.

  • Speech Synthesis: Use Amazon Polly or Google Text-to-Speech for natural and expressive responses.

• Voice Quality Customization:

  • SSML (Speech Synthesis Markup Language): Adjust tone and pacing to make responses sound calm and empathetic.

• Voice Interaction Flow:

  • Users can initiate voice conversations, allowing a hands-free experience. This flow will need to handle multi-turn conversations with accurate session management.

Future Scalability Considerations

To handle increased traffic and features in the future, the following architecture improvements are planned:

• Load Balancing: Use AWS Elastic Load Balancer to distribute traffic across instances, improving reliability.

• Serverless Functions: For smaller tasks, use AWS Lambda to manage short-lived processes and reduce costs.

• Model Optimization:

  • Distillation: Explore distilling the LLM into a smaller, more efficient model for quicker responses.

  • Quantization: Use 8-bit or 16-bit precision to reduce memory load without sacrificing too much performance.

Project Timeline

1. Phase 1: Set up infrastructure, initialize backend and front-end development environments.

2. Phase 2: Build chat and voice interfaces on the front end; set up backend endpoints.

3. Phase 3: Deploy initial LLM version, test response accuracy, and fine-tune the model.

4. Phase 4: Integrate voice processing, refine UX, and implement security measures.

5. Phase 5: Conduct full testing, make optimizations, and prepare for MVP launch.