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GAIA Chat UI - Implementation Plan

Status: Active Development Priority: High View full plan on GitHubVote with 👍

Executive Summary

Build GAIA Chat - a privacy-first desktop chat application for AI PCs that runs 100% locally on AMD Ryzen AI hardware. Unlike cloud-based alternatives, your conversations and documents never leave your machine.

Core Value Proposition

FeatureBenefit
PrivateYour data stays on YOUR device
FastAMD Ryzen AI NPU acceleration
SmartRAG-powered document Q&A
FreeNo API costs, no subscriptions
“ChatGPT for your private documents, running entirely on your AMD AI PC. No cloud, no subscription, no compromises.”

Key Features

Document Support

The RAG SDK already supports 50+ formats:
  • PDF (with VLM for images)
  • TXT, LOG
  • Markdown (.md, .markdown)
  • CSV, JSON
  • ReStructuredText (.rst)

Session Management

  • Global Document Library - Index once, use everywhere
  • Per-Session Attachments - Choose which docs to use per conversation
  • Shared CLI/UI State - Start in terminal, continue in desktop app

Privacy-First Design

  • Visual ”🔒 Local” indicator always visible
  • Network monitor showing no outbound connections
  • Data location display in settings
  • One-click export and secure deletion
  • No telemetry by default (opt-in only)

Architecture


Milestones

1

Foundation

Project structure, database schema, basic FastAPI server, CLI gaia chat init
2

Core Chat

Chat API endpoints, React UI, SSE streaming, message persistence
3

Documents

Document upload, RAG integration, library UI, source citations
4

Onboarding

System state detection, setup wizard, model download progress, error states
5

Polish

Privacy indicators, settings panel, export, keyboard shortcuts
6

Distribution

Electron packaging, installer integration, documentation

Success Metrics

MetricTarget
Time to first chat (new user)< 5 minutes
Time to first chat (returning)< 10 seconds
Document indexing< 5 seconds per MB
Streaming latency< 200ms first token
Error recovery rate> 90%

Full Implementation Plan

View the complete technical specification on GitHub