Stefan
002ed92bea
This commit implements iOS/Android mobile app support using Capacitor and adds a comprehensive opening training feature with LLM-powered explanations. ## Mobile App Infrastructure - Add Capacitor configuration for iOS/Android builds - Create mobile build script that excludes API routes - Update Next.js config for conditional static export - Add layout components with generateStaticParams for static builds - Generate 500+ static pages for offline mobile use ## Chess Engine Abstraction - Create ChessEngine interface for pluggable implementations - Add LocalEngine (GPL - uses stockfish.js in browser) - Add RemoteEngine (proprietary - calls API server) - Factory pattern selects engine based on environment - Enables GPL compliance for web, proprietary for mobile ## Opening Training Feature - Interactive opening repertoire training - Move validation with engine-backed feedback - LLM explanations using Gemini API - Wikipedia integration for opening context - Opening family grouping (e4, d4, c4, etc.) - Session state management - Real-time move feedback with evaluation Components: - OpeningSelector: Browse and select openings by family - OpeningTrainer: Main training interface with chessboard - MoveFeedback: Display move quality and LLM explanations - WikipediaSummary: Show opening history and context - ErrorBoundary: Graceful error handling Services: - openingLoader: Load and filter opening database - engineService: Engine evaluation wrapper - moveValidator: Validate moves against repertoire - feedbackGenerator: Generate contextual feedback - wikipediaService: Fetch and cache Wikipedia data - sessionManager: Track training session state ## Wikipedia Integration - Automatic Wikipedia article fetching for openings - Client-side and server-side caching - Sanitized summaries with proper formatting - Link opening database to Wikipedia slugs - API endpoints for on-demand fetching ## Docker Improvements - Add entrypoint script for automatic data setup - Fetch Wikipedia data on first container startup - Generate opening move index automatically - Remove generated data from git (public/openings/*.json, public/wikipedia/*.json) - Add READMEs explaining data requirements - Update .gitignore for generated files ## Dual Licensing Strategy - Add LICENSING.md explaining dual licensing approach - GPL-3.0 for web builds (includes Stockfish) - Proprietary option for mobile builds (no GPL code) - Single codebase, multiple licensing models - Legal compliance documented ## API Endpoints - POST /api/v1/llm/opening-explanation - Get LLM move explanations - GET /api/v1/wikipedia/summary - Fetch Wikipedia summaries ## Type Updates - Add openingTraining types - Update Tutor component to use ChessEngine interface - Add Gemini error handling types 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
65 lines
1.5 KiB
Markdown
65 lines
1.5 KiB
Markdown
# Opening Database
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This directory contains the ECO (Encyclopedia of Chess Openings) database files.
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## Required Files
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The following files are required for opening training:
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- `ecoA.json` - ECO codes A00-A99
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- `ecoB.json` - ECO codes B00-B99
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- `ecoC.json` - ECO codes C00-C99
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- `ecoD.json` - ECO codes D00-D99
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- `ecoE.json` - ECO codes E00-E99
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- `moveIndex.json` - Generated move sequence index
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## File Format
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Each ECO file (ecoA-E.json) should be a JSON object mapping FEN positions to opening metadata:
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```json
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{
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"fen_position": {
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"eco": "A00",
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"name": "Opening Name",
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"moves": "e4 e5 Nf3 Nc6",
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"wikipediaSlug": "opening-name" // optional
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}
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}
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```
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## Setup
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### Option 1: Docker (Automatic)
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When running via Docker, these files should be provided as a volume mount:
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```bash
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docker run -v ./openings:/app/public/openings ghcr.io/stefan-kp/chess-tutor
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```
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### Option 2: Local Development
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1. Obtain ECO database files (ecoA-E.json)
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2. Place them in this directory
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3. Generate the move index:
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```bash
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npm run build:opening-index
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```
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This will create `moveIndex.json` from the ECO files.
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### Option 3: Generate from PGN
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If you have a PGN database, you can extract ECO codes using chess tools like:
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- `pgn-extract`
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- Custom scripts
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## Notes
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- These files are **not included in git** (too large, ~4MB total)
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- Users must provide their own opening database
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- Wikipedia integration is optional (see `public/wikipedia/README.md`)
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- The move index is automatically generated during Docker startup
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