LocalFoodAI_FerRo988/PROJECT_CONTEXT.md

PROJECT_CONTEXT.md

Project Overview

LocalFoodAI is a local food AI that provides complete nutritional information on foods and can generate menu proposals based on user specifications. It runs entirely on a local Ubuntu 24.04 VM (8 vCPU, 30 GB RAM, no GPU). No user data leaves the server. The backend is Python-based.

Tech Stack

• Operating System: Ubuntu 24.04 (VM)
• Backend: Python 3.11+
• Database: SQLite (local, no cloud)
• Local LLM: Qwen 3.5 9B (quantized via Ollama, Q4_K_M or equivalent)
  • CPU-only compatible
  • Fits in 30 GB RAM with quantization
  • Instruction-following tuned
  • Open-source license (compatible with student projects)
• Local Web Search Tool: SearXNG (fully local, anonymous)
• Version Control: Git via Gogs on git.btshub.lu
• CI / Deployment: Antigravity Agent Manager handles task execution
• LLM Hosting: Ollama local instance, no cloud APIs

Rules & Constraints

• No external APIs or cloud services for computation or data fetching
• All data and computation must remain on the local VM
• All commits must be traceable to a Taiga Task ID
• Antigravity must read this file before starting any task to avoid hallucinating cloud-based solutions
• Model and backend selection must fit VM constraints (CPU-only, RAM limit)

Best Practices

• Use quantized models for CPU efficiency
• Verify all AI-generated Python or database logic before approving commits
• Test database queries and prompt logic locally before integrating
• Attach all artifacts (Implementation Plans, task lists, browser recordings) to the corresponding Taiga task
• Always include the TG- prefix in commit messages