As a human who lives off-grid, in nature, this has been the tool that has revolutionized how I live and thrive in the digital age.
Our off-grid assistant is a sophisticated, two-part system designed for resilience and power. It’s a hybrid architecture where a local, low-power Raspberry Pi 5 handles critical tasks and data collection, while a more powerful VPS provides computational horsepower for heavy-duty AI, data analysis, and centralized services.
Here is a detailed outline of the plan.
Hardware
Raspberry Pi 5 (The Local “Edge” Device)
- Raspberry Pi 5: The heart of the system. An 8GB model is highly recommended for running large language models and other resource-intensive tasks.
- NVMe SSD: Connected via a dedicated HAT, this provides fast, reliable, and durable local storage for logs, models, and files. This is a critical upgrade over a slower SD card.
- High-Quality Power Supply: A robust power solution is essential for an off-grid system to ensure stable operation and prevent data corruption.
- Camera Module: An official Raspberry Pi Camera Module or a compatible USB webcam for visual monitoring and analysis.
- USB Microphone & Speakers: To enable local speech-to-text (STT) and text-to-speech (TTS) functionality for a voice-activated assistant.
- GPIO Interface: Used for connecting to power relays to control devices like a generator, and for receiving data from sensors.
- Optional: LoRaWAN/Meshtastic Module: For extending the system’s reach with a long-range, low-power mesh network for remote sensors.
Software
Raspberry Pi 5 (Local Stack)
- Operating System: Raspberry Pi OS (64-bit) is required for running Ollama and other modern software.
- Ollama: The local LLM engine. A small, quantized model (like Llama3 8B or Phi-3 Mini) will run locally on the CPU for quick, offline queries and commands.
- TTS & STT:
- Text-to-Speech: Piper TTS for high-quality, local voice output.
- Speech-to-Text: Vosk for offline voice command recognition.
- Computer Vision: A lightweight model such as YOLO or MobileNet is used for local image analysis (e.g., detecting objects in a security feed or analyzing the state of a control panel).
- Automation Script: A custom Python or shell script to manage the generator based on sensor data and to handle all the local integrations.
- Nextcloud Client: To automatically synchronize logs and other important files with the central Nextcloud server on the VPS when an internet connection is available.
VPS (Central “Hub” Server)
- Operating System: A stable Linux distribution (e.g., Debian, Ubuntu) running Docker and Docker Compose for managing all services.
- Ollama: A second, more powerful instance of Ollama running a larger LLM for complex tasks like in-depth log analysis, creative writing, or summarizing large documents.
- FastSDCPU/WebUI: For generating images with Stable Diffusion, which is too computationally demanding for the Pi.
- N8N: The central automation and integration tool. It connects all the services together, orchestrating workflows triggered by events from the Pi.
- Nextcloud Server: The main cloud instance for centralized file storage and backup.
- Baserow: A self-hosted, open-source database for structured logging and data analysis. The Pi’s sensor data would be pushed here for long-term storage and advanced querying.
- Minio: An S3-compatible object storage server for large, unstructured files like images and videos.
- Chatterbox: For providing a web-based chat interface to your LLMs.
- Pi-hole: To provide network-wide ad-blocking and DNS services, improving security and performance for all connected devices.
Workflow Outline
- Local Operation (No Internet): The Pi 5 operates autonomously. Its automation script monitors the power system, makes decisions, and performs real-time tasks. You can interact with it using voice commands via a microphone, with Vosk converting your speech to text and Piper TTS providing spoken responses from the local LLM. The Pi logs all activity and collects data locally on its NVMe SSD.
- Internet Connectivity: When a connection is established, the Pi’s Nextcloud client automatically synchronizes its local log files and any images to the Nextcloud server on the VPS.
- VPS Automation: An n8n workflow on the VPS is triggered by the new files in Nextcloud. It can then:
- Send the log files to the larger Ollama model on the VPS for a detailed, intelligent summary.
- Store the sensor data in Baserow for long-term analysis.
- Use FastSDCPU to perform specific tasks, like generating an image based on a prompt you sent from the Pi.
- Provide you with a centralized dashboard (via WebUI or Nextcloud) to view all data, logs, and generated content.
This hybrid model ensures that critical functionality is always available locally, while the powerful VPS handles tasks that require more resources, creating a resilient and highly capable off-grid assistant.
