Local Context-Aware Personal AI Assistant

Technical Design Document (TDD) the Technical Design Document (TDD) for your personalized local AI assistant — designed to evolve into a private, emotionally intelligent companion running entirely within your system and network.

Author: Priyanshu Verma
Date: November 2025
Version: 1.0

1. System Overview #

1.1 Goal #

To build a local, private, emotionally aware AI assistant that:

• Operates entirely offline or within a home network.

• Interacts through CLI and voice input/output.

• Understands and adapts to context, emotion, and environment.

• Evolves over time using user-provided data (journals, notes, etc.).

• Behaves as a human-like companion, not a Q&A chatbot.

2. Core Principles #

3. System Architecture #

3.1 Layer Overview #

+---------------------------------------------------------+
| CLI / Voice Interface Layer                            |
+---------------------------------------------------------+
| Interaction Engine (NLP + Context Handler)              |
+---------------------------------------------------------+
| Memory Layer (Local DB / Filesystem Storage)            |
+---------------------------------------------------------+
| Sensor & Environment Interface                          |
+---------------------------------------------------------+
| Core Logic & Personality Model                          |
+---------------------------------------------------------+
| System Services (File IO, Speech, Network, Security)    |
+---------------------------------------------------------+

4. Components Detail #

4.1 CLI Interface #

• Purpose: Provide text-based, real-time communication.

• Features:

  • Supports ongoing dialogue threads.

  • Context-aware command recognition.

  • Natural pacing (responds after multiple messages, not every line).

• Libraries: Python cmd, or prompt_toolkit for richer interactions.

4.2 Voice Interface #

• Purpose: Enable natural spoken interactions.

• Speech-to-Text:

  • Use Vosk (offline, lightweight, accurate).

• Text-to-Speech:

  • Use pyttsx3 (offline) for a female, calm, natural voice.

• Future Add-on: Custom neural TTS model for more emotional expression.

4.3 Interaction Engine #

Core of the assistant. Handles all message understanding, context tracking, and response generation.

Submodules:

1. Input Processor — Cleans and tokenizes input, detects sentiment, tone, and intent.

2. Context Manager — Tracks conversation flow and previous context (short-term).

3. Response Generator — Generates replies dynamically (rule + generative hybrid).

4. Dialogue Controller — Controls timing and conversational rhythm (delayed responses, pauses, etc.).

Technologies:

• spaCy for tokenization and part-of-speech tagging.

• TextBlob or VADER for sentiment analysis.

• Custom rule engine for behavioral logic.

4.4 Memory Layer #

Initial State: No memory (stateless mode).
Later: Persistent, local memory growing from interactions.

Structure:

Checkpoints:

• Create schema for user data storage.

• Implement lightweight memory.json for preferences.

• Expand to SQLite when needed for journaling and reflection tracking.

4.5 Sensor & Environment Layer #

Goal: Contextual awareness of physical surroundings and system status.

Inputs:

• Watch microphone (local audio feed)

• Raspberry Pi sensors (temperature, light, etc.)

• System data (CPU load, uptime, ambient info)

Behavior:

• Adjusts tone and responses based on environment (e.g., tired tone at night, energetic in morning).

• Triggers routines (“It’s warm today, shall I lower the fan?”).

Checkpoints:

• Establish local MQTT or socket connection with Pi sensors.

• Create listener script for sensor data.

• Build internal “context dictionary” (e.g., {time: "night", mood: "calm"}).

4.6 Personality Core #

Purpose: Defines emotional tone, pacing, empathy, and self-expression.

Examples:

• User: “I messed up again today.”

  • Assistant: “It’s okay. Want to talk about what happened, or just vent a little?”

4.7 Security & Privacy Module #

Rules:

• All processing and data storage are local.

• No outbound network requests except user-approved API fetches.

• Sensitive data (like journals) are AES-256 encrypted.

• Optional access control with a local passphrase.

Checkpoints:

• Implement local encryption using cryptography library.

• Create a toggle for “offline-only” mode.

• Maintain audit logs locally.

5. Data Flow #

User Input → Input Processor → Sentiment Analysis → Context Manager
            ↓                              ↑
         Memory Layer <—————— Response Generator
            ↓
         Output Layer (Text / Voice)

6. Development Phases & Checkpoints #

7. Example Scenarios #

Scenario 1 — Casual Chat #

User: “You’re quiet today.”
Assistant: “Just letting you think. How’s your afternoon going?”

Trigger: Detects user emotional curiosity → mild empathetic response.

Scenario 2 — Stress Support #

User: “I can’t focus anymore.”
Assistant: “You’ve been pushing hard. Maybe step away for a minute?”

Trigger: Detects stress pattern, gentle corrective feedback.

Scenario 3 — Environmental Cue #

Sensor Data: Temp = 32°C
User: “It’s really hot.”
Assistant: “Yeah, I can feel that too. Want me to switch on the fan?”

Trigger: Context + sensor correlation.

8. Future Integrations #

9. Success Metrics #

10. Maintenance & Expansion #

• Code modularity: Each module runs standalone.

• Testing: Unit + integration tests for all subsystems.

• Logging: Minimal text logs for debugging and behavioral tuning.

• Updates: Modular plugin-style expansions — e.g., adding a new emotion model or device interface without rewriting core logic.