🧠 Personal AI Framework

Your private AI assistant that knows your emails, projects, writing style, and technical documents. Runs 100% locally on your hardware.

✨ Features

Feature	Description
Personal Knowledge Base	Your documents -- emails, transcripts, blogs, audio recordings, datasheets
Your Writing Style	LoRA fine-tuned on your emails - writes like you
Streaming Responses	Real-time word-by-word output
Hybrid RAG Search	BM25 + semantic + reranking for best results
Email Drafting	Generate emails with one-click open in Gmail/Outlook
Meeting Summarizer	Transcribe audio/video with Whisper + summarize
Document Generator	Create specs, proposals, reports from your knowledge
Datasheet Ingestion	Ingest PDFs - reference manuals, app notes
Daily Digest	Automated morning summary of your AI activity
Web UI	Clean interface with settings panel

🖥️ Requirements

• GPU: NVIDIA RTX 3080+ (16GB+ VRAM recommended)
• RAM: 32GB+
• Storage: 50GB+ for models and knowledge base
• OS: Ubuntu 22.04+ (or similar Linux)

🚀 Quick Start

# Clone the repo
git clone https://github.com/kylefoxaustin/personal-ai-framework.git
cd personal-ai-framework

# Run setup (installs dependencies, creates config)
./setup.sh

# Download model (Mixtral 8x7B Q4)
./run.sh download-model

# Start services
./run.sh start

# Open web UI
xdg-open http://localhost:3000

📁 Project Structure

personal-ai-framework/
├── docker/                 # Docker configurations
├── knowledge/              # Your knowledge base
│   ├── documents/          # General documents
│   ├── emails/             # Exported emails (.eml, .mbox)
│   ├── transcripts/        # Meeting/video transcripts
│   ├── datasheets/         # PDF datasheets & manuals
│   └── writing/            # Your blog posts, articles
├── pipeline/               # Core Python services
│   ├── llm_server.py       # FastAPI LLM server
│   ├── rag_service.py      # ChromaDB RAG
│   ├── advanced_rag.py     # Hybrid search + reranking
│   ├── meeting_summarizer.py
│   ├── doc_generator.py
│   ├── email_service.py
│   ├── daily_digest.py
│   └── settings_manager.py
├── web/                    # Web UI
├── training/               # LoRA fine-tuning
└── run.sh                  # Main CLI

🛠️ Commands

Basic Operations

./run.sh start              # Start all services
./run.sh stop               # Stop all services
./run.sh status             # Check service status
./run.sh logs               # View logs

Knowledge Base

./run.sh sync               # Sync knowledge base
./run.sh ingest-datasheets  # Ingest PDF datasheets
./run.sh ingest-emails      # Ingest email exports

AI Features

# Email drafting
./run.sh email draft "project status update" -t recipient@email.com
./run.sh email draft "follow up on meeting" --open  # Opens in email client

# Document generation
./run.sh generate technical_spec "i.MX display system"
./run.sh generate project_proposal "embedded AI project"
./run.sh generate status_report "Q1 development progress"

# Meeting summarization
./run.sh summarize recording.mp4 -t "Team Meeting" --add-to-kb

# Daily digest
./run.sh digest                          # Print digest
./run.sh digest -m mailto -t you@email.com  # Open in email client
./run.sh digest --schedule 08:00 -t you@email.com  # Schedule daily

🌐 Web UI

Access at http://localhost:3000

• Chat: Ask questions, get answers from your knowledge base
• Email Drafting: Type "Draft an email about..." → Copy/Save/Open buttons
• Streaming: Responses appear word-by-word in real-time
• Settings (⚙️): Configure digest, sync, view email provider status

📊 Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Web UI    │────▶│  LLM Server │────▶│  Mixtral    │
│  :3000      │     │  :8080      │     │  8x7B Q4    │
└─────────────┘     └──────┬──────┘     └─────────────┘
                          │
                          ▼
                   ┌─────────────┐
                   │  ChromaDB   │
                   │  (RAG)      │
                   │  61K+ docs  │
                   └─────────────┘

🔧 Configuration

Settings are stored in ~/.personal-ai/settings.json and can be configured via:

• Web UI Settings panel (⚙️ button)
• CLI commands
• Direct file editing

Key settings:

• Daily Digest: Enable, time, email address
• Auto-Sync: Enable, interval (1/4/12/24 hours)
• Context Window: 8K/16K/32K tokens

📈 Performance

Tested on RTX 5090 (32GB VRAM):

• Inference: ~30 tokens/sec
• Context: 16K tokens default (32K max)
• Knowledge Base: 61,500 documents
• Datasheet Ingestion: 37 PDFs (6,129 chunks) in ~5 min

🏷️ Versions

• v2.0.0 - Advanced Features (streaming, settings UI, daily digest, datasheets)
• v1.0.0 - Initial Release (RAG, email drafting, LoRA training)

Author

Kyle Fox - GitHub

📝 License

MIT License - Use freely for personal projects.

🙏 Acknowledgments

• Mixtral 8x7B - Base model
• llama.cpp - Efficient inference
• ChromaDB - Vector database
• OpenAI Whisper - Audio transcription

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Trust the Awesomeness! 🚀