PrivateScribe.ai: Build Your Private AI Writing Assistant Locally
Why You Need an Offline AI Writing Companion
Imagine conducting sensitive client meetings or recording proprietary research without worrying about cloud privacy. PrivateScribe.ai solves this by running entirely on your personal computer – no internet connection needed. This open-source platform combines note-taking with local AI processing, keeping all data within your control. Whether you’re a journalist protecting sources or a developer handling confidential code, it provides intelligent text processing without sacrificing privacy.
The modular design makes deployment accessible even without deep technical expertise. Let me walk you through how it works and how to set it up.
Understanding the Technical Architecture (Simplified)
Think of the system as a well-coordinated team with specialized roles:
| Component | Technology | Function | Real-World Analogy |
|---|---|---|---|
| AI Brain | Ollama | Processes text using AI models | The creative thinker |
| Memory Bank | SQLite | Stores all notes securely | A locked filing cabinet |
| Control Hub | Flask | Connects interface with AI | A skilled translator |
| User Console | Vite | Provides visual interaction | Your dashboard controls |
The default “thinking engine” uses Llama 3.2 – a highly efficient local AI model. You can swap models like changing tools in a workshop, using any Ollama-compatible alternative.
Step-by-Step Installation (Windows/Mac/Linux)
Essential Tools Checklist
Prepare these foundational components first:
# Core Requirements
1. Python 3.8+ # Backend programming language
2. Node.js 16+ # Frontend runtime environment
3. npm # Interface component manager
4. Ollama # Local AI engine (https://ollama.ai/)
“
Critical Note: After installing Ollama, run
ollama servein your terminal to activate the AI service
Four-Step Setup Process
Step 1: Download the Software Package
Open your terminal (Command Prompt/PowerShell on Windows) and execute:
git clone https://github.com/yourusername/private-ai-scribe.git
cd private-ai-scribe # Enter project directory
Step 2: Configure the Backend System
# Create isolated environment (prevents software conflicts)
python -m venv venv
# Activate environment (system-specific commands)
source venv/bin/activate # Mac/Linux
venv\Scripts\activate # Windows
# Install core components
pip install -r requirements.txt
# Initialize database
flask db upgrade
Step 3: Build the User Interface
cd frontend # Navigate to frontend directory
npm install # Install interface components (takes 2-5 minutes)
Step 4: Launch the AI Engine
In a new terminal window:
# Download default AI model (Llama 3.2)
ollama pull llama:3.2
# Verify model functionality
ollama run llama:3.2
> Enter test text (e.g., "Hello"), confirm response appears
Launching Your Private AI Workspace
Dual-Terminal Operation
Run these commands in separate terminal windows:
Terminal 1 – Start Backend Services
# From main project directory
source venv/bin/activate # Activate environment
flask run # Launch backend
→ Success when you see Running on http://127.0.0.1:5000
Terminal 2 – Activate User Interface
cd frontend # Enter frontend directory
npm run dev # Start visual interface
→ Access your AI scribe at http://127.0.0.1:3000
Real-World Application Example
When you input:
"Organize meeting notes: 1. Q3 sales target +15% 2. New version launches Sept 3. Need more QA staff"
The system processes this internally:
-
Vite frontend sends text to Flask backend -
Flask communicates with Ollama’s Llama model -
AI returns structured output: ## Meeting Summary - **Sales Target**: 15% increase for Q3 - **Release Schedule**: September launch - **Resource Needs**: Additional QA personnel -
Results save automatically to SQLite database
Entire workflow occurs on your local machine – confidential data never leaves your device.
Technical Design Choices Explained
Why These Technologies?
-
SQLite Database: Single-file storage, zero configuration -
Flask Backend: Lightweight Python framework, easily extendable -
Vite Frontend: Instant interface updates, smooth development -
Ollama Integration: Optimized for local AI processing
Customizing Your AI Model
While Llama 3.2 is default, switch models via configuration:
# In Flask configuration file
AI_MODEL = "llama:3.2" # Replace with any Ollama-supported model
“
Pro Tip: Visit Ollama’s documentation for compatible model options
Frequently Asked Questions (FAQ)
1. Do I need special hardware?
No. Llama 3.2 runs efficiently on standard CPUs. 16GB RAM ensures optimal performance.
2. Where is my data stored?
All data resides in the project’s instance folder. Back up this directory to migrate your notes.
3. Can teams collaborate?
Designed for individual use. Advanced users can share via local networks with proper security.
4. What’s the response speed?
Initial model load takes 30-60 seconds. Subsequent responses complete in 2-5 seconds.
5. Can it process files?
Current version accepts text input only. PDF/image support requires custom development.
6. Why local instead of cloud?
Critical for: 1) Sensitive fields like healthcare/law 2) Offline environments 3) No subscription costs.
Developer Customization Guide
For programmers:
-
Add Authentication: Integrate Flask-Login in app.py -
Enable File Processing: Incorporate PyPDF2 for PDF support -
Create Shortcuts: Modify keyboard listeners in src/components -
Optimize Prompts: Adjust templates in services/ai_integration.py
# Example: Modify AI processing instructions
PROMPT_TEMPLATE = """
Format this content professionally:
{user_input}
---
Output requirements:
- Use hierarchical headings
- Emphasize key points
- Preserve original meaning
"""
Value Proposition and Future Potential
PrivateScribe.ai exemplifies practical local AI implementation. Community feedback shows particular value in:
-
Confidential Professions: Legal case summaries/medical documentation -
Field Research: Data recording without internet access -
Personal Knowledge Bases: Lifetime private information repositories