Project Status

User Interface (UI): JavaScript (React, Vue) for the front end, and HTML/CSS.

• MVP Capabilities:

  • Simple form for user input handling (used for reminder, weather, to-do tasks, chatbot questions, and sensitive data access requests)
  • Basic rendering of static and dynamic content
  • Minimal responsive design
  • Basic state management
  • Simple integration with a backend API

UI Designer (UID): Python or JavaScript for adjusting interface elements dynamically.

• MVP Capabilities:

  • Basic UI adjustment (e.g., form field visibility based on program requirements)
  • Simple component customization
  • Integration with a basic design system

Planner: Python for NLP tasks and plan evaluation.

• MVP Capabilities:

  • Simple natural language command parsing (used for identifying tasks such as reminders, weather checks, to-do list operations, chatbot questions, and sensitive data access requests)
  • Basic task decomposition
  • Simple execution plan creation

Evaluator: Python for evaluation tasks, ensuring plans meet objectives and constraints.

• MVP Capabilities:

  • Basic validation of execution plans (e.g., verifying parsed reminder time, weather request validity, chatbot input, or sensitive data request)
  • Simple feedback generation (e.g., notifying if input commands are not understood, or if access requests require further approval)

Executor: Python for executing planned tasks with concurrency support.

• MVP Capabilities:

  • Basic task execution (e.g., scheduling reminders, fetching weather data, managing to-do list items, handling chatbot requests, processing sensitive data access)
  • Simple concurrency with threading (e.g., handling multiple user requests simultaneously)
  • Basic error handling

Capabilities (Side Effects): Python, integrated for specific system commands.

• MVP Capabilities:

  • Execute simple system-level commands (e.g., sending notifications for reminders, approving sensitive data access)
  • Security checks for command execution (e.g., verifying user identity for database access requests)

Insights (Intelligence): Python with AI models (like PyTorch).

• MVP Capabilities:

  • Integrate a pre-trained model for basic insights (e.g., analyzing user input patterns, responding to chatbot queries, and assessing data access requests)
  • Simple data analysis from user inputs (e.g., identifying frequently requested reminders, questions, or sensitive data requests)

HitL Terminal: WebSocket-based terminal, using Python’s Flask-SocketIO.

• MVP Capabilities:

  • Basic command-line interface for user input (e.g., manually entering reminder, weather, to-do commands, sensitive data requests, or chatbot queries)
  • Simple WebSocket communication
  • Human-in-the-loop approval interface (e.g., approve or deny access to sensitive databases and monitor chatbot-generated information)

Info Layer, Queries, Commands, Data: SQLite for local prototyping.

• MVP Capabilities:

  • Store and retrieve basic data (e.g., to-do list items, reminder schedules, sensitive database records, chatbot conversations)
  • Simple command processing (e.g., CRUD operations for to-do list, handling sensitive data access requests, storing chatbot interactions)

Execution Agent: Central component responsible for managing program execution.

• MVP Capabilities:

  • Basic worker provisioning (e.g., assigning tasks such as fetching weather data, scheduling reminders, handling chatbot queries, processing sensitive data access)
  • Simple task assignment
  • Basic error detection

API for UI Interaction: REST API to facilitate communication between the UI and backend components.

• MVP Capabilities:

  • Basic RESTful endpoints for CRUD operations (e.g., create, read, update, delete tasks in to-do list, handle chatbot queries, approve sensitive data access)
  • Integration with Planner, Evaluator, and Executor
  • Simple JSON response handling
  • Authentication for basic security

Infrastructure: The underlying system and technologies needed to support the components for a local prototype.

• MVP Capabilities:

  • Local Server: Host services on a local machine.
  • Containerization: Docker for packaging services.
  • Orchestration: Docker Compose for managing services locally.
  • Logging: Basic logging with Python’s logging module.
  • Networking: Simple localhost communication.
  • Security: Basic authentication for local access.

Event-Driven Architecture (EDA) Components: Simplified for local processing and testing.

• MVP Capabilities:

  • Basic event producer and consumer setup (e.g., generating events for reminders)
  • Lightweight message broker (e.g., RabbitMQ) for local message passing

• Libraries and Tools for EDA:

  • Event Broker: RabbitMQ for local message queuing.
  • Asynchronous Task Management: Celery (Python) for simple task queues.
  • Integration: Flask for basic REST APIs.
  • Monitoring: Simple logging with Python’s logging module.

Basic English Language Programs for Prototype:

• Program 1: “Remind Me”

  • Users can input a simple reminder (e.g., “Remind me to take a break in 30 minutes”).
  • The system parses the input, schedules a task, and uses the Execution Agent to trigger a notification after the specified time.

• Program 2: “Weather Check”

  • Users can ask for the current weather (e.g., “What’s the weather like today?”).
  • The system queries a mock weather API or local data source and returns a basic weather update to the user interface.

• Program 3: “To-Do List Manager”

  • Users can add, view, and delete tasks in a simple to-do list (e.g., “Add ‘buy groceries’ to my to-do list”).
  • The system manages tasks in a local SQLite database and interacts with the UI through the API for CRUD operations.

• Program 4: “Sensitive Database Access via HitL Terminal”

  • Users can request access to a sensitive database (e.g., “Access customer data records”).
  • The system processes the request, but requires a human-in-the-loop (HitL) to approve the access before any sensitive data is retrieved.
  • The HitL Terminal allows a designated user to manually approve or deny the database access request, ensuring secure handling of sensitive information.

• Program 5: “Ask a Chat Model”

  • Users can ask a general question (e.g., “What is the capital of France?”).
  • The system forwards the question to a pre-trained chat model, which processes the input and generates an appropriate response.
  • The response is returned to the user interface for display.