Claude/legal doc ai sota upgrade 011 c uhg wh kj7n l kn w tfx7p4a #3
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This commit delivers the complete Phase 1 implementation transforming
ChittyChronicle from keyword-only search to intelligent semantic search
with RAG-powered document Q&A.
🎯 CORE CAPABILITIES DELIVERED:
1. **Vector Embeddings Infrastructure**
- PostgreSQL pgvector extension support
- 768-dim (Legal-BERT) and 1536-dim (OpenAI) embedding columns
- IVFFlat indexing for sub-second similarity search
- Embedding coverage tracking and monitoring
2. **Hybrid Search (60% Semantic + 40% Keyword)**
- Reciprocal Rank Fusion (RRF) algorithm implementation
- Combines keyword precision with semantic understanding
- Configurable alpha parameter (0=keyword, 1=semantic)
- Metadata filtering (dates, types, confidence levels)
- Expected: 50-70% improvement in search relevance
3. **RAG Document Q&A**
- Natural language questions over case timelines
- Claude Sonnet 4 for answer generation (temp=0.1)
- Automatic citation tracking and source attribution
- Confidence scoring based on retrieval quality
- Batch query support for case analysis
4. **Production-Ready Services**
- Embedding service with batch processing (100/batch, 1s delays)
- Cost estimation and monitoring
- Graceful fallback to keyword search if embeddings unavailable
- Error handling and retry logic
📁 NEW FILES:
Database & Schema:
- migrations/001_add_pgvector.sql - pgvector migration with indexes
- shared/schema.ts - Added vector embedding columns to timeline tables
Core Services:
- server/embeddingService.ts - OpenAI embedding generation (779 lines)
- server/hybridSearchService.ts - RRF hybrid search algorithm (425 lines)
- server/ragService.ts - Claude-powered RAG Q&A (312 lines)
API Layer:
- server/sotaRoutes.ts - 11 new API endpoints (540 lines)
* GET /api/timeline/search/hybrid - Hybrid search
* POST /api/timeline/ask - RAG document Q&A
* GET /api/timeline/summary/:caseId - AI-generated summaries
* GET /api/timeline/analyze/gaps/:caseId - Gap analysis
* POST /api/timeline/ask/batch - Batch queries
* POST /api/admin/embeddings/entry/:id - Single entry embedding
* POST /api/admin/embeddings/generate - Batch embedding job
* GET /api/admin/embeddings/coverage - Coverage statistics
* POST /api/admin/embeddings/estimate-cost - Cost estimation
* GET /api/timeline/search/keyword - Keyword-only (fallback)
* GET /api/timeline/search/semantic - Semantic-only (testing)
Tooling:
- scripts/generate-embeddings.ts - CLI tool for batch embedding (178 lines)
- package.json - Added npm scripts: embeddings:generate, embeddings:coverage
- server/index.ts - Integrated SOTA routes with feature flag
Documentation:
- docs/PHASE1_DEPLOYMENT_GUIDE.md - Complete deployment guide (450+ lines)
🔧 TECHNICAL HIGHLIGHTS:
Database Migration (001_add_pgvector.sql):
- Enables pgvector extension
- Adds vector columns: description_embedding, content_embedding
- Creates IVFFlat indexes (lists=100) for fast similarity search
- Adds embedding_coverage view for monitoring
- Creates find_similar_entries() PostgreSQL function
Embedding Service Features:
- OpenAI text-embedding-3-small (1536-dim, $0.02/1M tokens)
- Batch processing up to 100 texts per request
- Automatic truncation for long documents (32K chars ≈ 8K tokens)
- Combines description + detailedNotes + tags for rich embeddings
- Cost estimation: ~$0.01 per 1000 documents
- Coverage tracking and reporting
Hybrid Search Implementation:
- Reciprocal Rank Fusion: RRF_score = Σ 1/(k + rank), k=60
- Configurable balance via alpha parameter (default: 0.6)
- Keyword search: PostgreSQL LIKE queries (future: full-text search)
- Semantic search: pgvector cosine similarity (<=> operator)
- Result fusion with match type detection (keyword|semantic|hybrid)
- Highlight extraction for keyword matches
RAG Service Architecture:
- Retrieval: Hybrid search (default topK=5, alpha=0.6)
- Generation: Claude Sonnet 4 (claude-sonnet-4-20250514)
- Temperature: 0.1 for factual accuracy
- System prompt: Legal analyst with strict citation requirements
- Source attribution: [1], [2], [3] citation format
- Confidence: Based on average retrieval relevance scores
- Multi-turn conversation support via RAGConversation class
API Design:
- RESTful endpoints following existing patterns
- Query parameters for search tuning (topK, alpha, filters)
- JSON request/response bodies
- Consistent error handling with status codes
- Feature flag support (ENABLE_HYBRID_SEARCH)
- Development mode: always enabled
CLI Tool:
- Batch processing with progress indicators
- Cost estimation before generation
- Coverage reporting
- Case-specific or global embedding generation
- Error tracking and statistics
🚀 DEPLOYMENT REQUIREMENTS:
Environment Variables (new):
```bash
OPENAI_API_KEY=sk-... # Required for embeddings
EMBEDDING_MODEL=text-embedding-3-small
EMBEDDING_DIMENSIONS=1536
ENABLE_HYBRID_SEARCH=true # Feature flag
ENABLE_RAG=true # Future: disable RAG if needed
```
Database Migration:
```bash
psql -d $DATABASE_URL -f migrations/001_add_pgvector.sql
```
Initial Embedding Generation:
```bash
npm run embeddings:generate # All entries
npm run embeddings:coverage # Check status
```
💰 COST ESTIMATES:
Development Investment: $22,500-45,500 (8 weeks, 1-2 engineers)
Ongoing Operational: $250-500/month
- OpenAI embeddings: ~$50-150/month (1-3M tokens)
- Anthropic Claude RAG: ~$100-200/month (varies by usage)
- Additional compute: ~$100-150/month
Cost per 1000 documents embedded: ~$0.01
Average query cost: ~$0.0002-0.001 (embedding + RAG)
📊 EXPECTED RESULTS:
Performance Targets:
- Search recall improvement: +50-70% vs keyword-only
- Response time p95: <1000ms for hybrid search
- RAG answer accuracy: ≥80% on evaluation dataset
- Embedding coverage: >95% of active timeline entries
User Experience:
- Find documents by meaning, not just exact keywords
- Ask natural language questions about case timelines
- Get AI-generated answers with source citations
- Discover related documents through semantic similarity
Examples:
Query: "breach of contract"
→ Finds: "violation of agreement", "contractual breach", "failed to perform"
Query: "What evidence supports the negligence claim?"
→ RAG Answer: "Based on timeline entry [1], the plaintiff documented..."
🔍 VALIDATION & TESTING:
Included in deployment guide (docs/PHASE1_DEPLOYMENT_GUIDE.md):
- Step-by-step deployment checklist
- Testing procedures for all endpoints
- Comparison tests (keyword vs semantic vs hybrid)
- Troubleshooting common issues
- Performance tuning guidelines
- Monitoring setup instructions
⚡ INTEGRATION:
Routes registered in server/index.ts with feature flag:
```typescript
if (process.env.ENABLE_HYBRID_SEARCH === 'true' || app.get("env") === "development") {
registerSOTARoutes(app);
}
```
Development: Always enabled for testing
Production: Controlled by ENABLE_HYBRID_SEARCH env var
Backward Compatibility:
- Existing /api/timeline/search remains unchanged
- New capabilities available at new endpoints
- Zero breaking changes to current functionality
- Gradual rollout supported via feature flags
🎓 NEXT STEPS:
1. Deploy to staging environment
2. Run database migration (pgvector extension)
3. Generate initial embeddings for existing documents
4. Conduct user acceptance testing
5. Monitor costs and performance
6. Decision gate for Phase 2 (Document Classification)
📚 DOCUMENTATION:
- PHASE1_DEPLOYMENT_GUIDE.md: Complete deployment instructions
- SOTA_UPGRADE_IMPLEMENTATION_PLAN.md: Detailed technical specs
- EXECUTIVE_SUMMARY_SOTA_UPGRADE.md: Business case and ROI
- ROADMAP_SOTA_UPGRADE.md: 5-phase rollout plan
🔗 RELATED:
Issue: SOTA Upgrade Planning
Branch: claude/legal-doc-ai-sota-upgrade-011CUhgWHKj7nLKnWTfx7p4a
Previous Commit: 82ae13d (Planning documentation)
---
**Status**: Ready for staging deployment
**Estimated Production Launch**: January 20, 2026
**ROI**: 3-7 month payback period, 78-251% Year 1 ROI
Provides comprehensive backup solution for private git repository
to Google Drive using rclone for incremental syncing.
📦 NEW FILES:
Scripts:
- scripts/setup-backup.sh (150 lines)
* One-command setup: installs rclone, configures Google Drive
* Interactive wizard walks through OAuth authentication
* Creates backup directories and tests connection
* Includes error handling and clear status messages
- scripts/backup-to-gdrive.sh (180 lines)
* Production-ready backup script with logging
* Two-tier backup strategy:
1. Full rsync: Incremental sync preserving git history
2. Git bundle: Single-file timestamped snapshots
* Excludes node_modules, dist, .env for efficiency
* Dry-run mode for testing
* Detailed backup summary with git info
Documentation:
- docs/BACKUP_SETUP_GUIDE.md (400+ lines)
* Complete setup guide from installation to automation
* Step-by-step rclone configuration
* Multiple restore scenarios documented
* Troubleshooting common issues
* Security best practices
* Cost considerations (fits in 15 GB free tier)
🔧 FEATURES:
Backup Strategy:
- Full repository sync with rclone (fast incremental updates)
- Daily git bundles (timestamped, single-file snapshots)
- Excludes: node_modules, dist, .env, log files
- Includes: All source code, git history, documentation
Security:
- OAuth2 authentication via browser
- Excludes sensitive files (.env)
- Optional encryption support documented
- Preserves file permissions and metadata
Performance:
- Incremental sync (only uploads changes)
- Batch processing for git bundles
- Compression during transfer
- Progress indicators
Automation:
- Cron-ready scripts with logging
- Example: 0 2 * * * for daily 2 AM backups
- Non-interactive operation after setup
- Email notifications on failure (configurable)
🚀 USAGE:
Quick Setup (10 minutes):
```bash
cd /home/user/chittychronicle
./scripts/setup-backup.sh
# Follow interactive prompts to authenticate with Google
```
Manual Backup:
```bash
./scripts/backup-to-gdrive.sh # Full backup
./scripts/backup-to-gdrive.sh --dry-run # Test without uploading
```
Automated Backups:
```bash
crontab -e
# Add: 0 2 * * * /home/user/chittychronicle/scripts/backup-to-gdrive.sh
```
Restore:
```bash
# From live sync
rclone sync gdrive:backups/chittychronicle/ ./restored/
# From bundle
rclone copy gdrive:backups/bundles/chittychronicle-20251102.bundle ./
git clone chittychronicle-20251102.bundle restored
```
💾 BACKUP LOCATIONS:
Google Drive structure:
```
backups/
├── chittychronicle/ # Live sync (full repo)
│ ├── server/
│ ├── client/
│ ├── docs/
│ └── ... (all files)
└── bundles/ # Daily snapshots
├── chittychronicle-20251101.bundle
├── chittychronicle-20251102.bundle
└── ... (dated backups)
```
📊 COST & STORAGE:
Free Tier (15 GB):
- Repo size: ~50-200 MB (without node_modules)
- Daily bundles: ~50 MB each
- Capacity: 30-90 days of history
Paid ($1.99/month for 100 GB):
- Years of backup history
- Recommended for production use
💡 WHY THIS MATTERS:
Private Repository Protection:
- GitHub is primary, Google Drive is safety backup
- Protection against:
* GitHub account issues
* Repository deletion
* Branch force-pushes
* Internet connectivity loss
Disaster Recovery:
- Point-in-time restore via dated bundles
- Full git history preserved
- Can restore specific files or entire repo
- Tested restore procedures documented
Peace of Mind:
- Automated daily backups
- Off-site storage (different provider than GitHub)
- No manual intervention after setup
- Verification and monitoring built-in
🔍 TESTING:
Setup script includes:
- rclone installation verification
- Google Drive connection test
- Directory creation
- Dry-run backup
Backup script includes:
- Pre-flight checks (rclone installed, remote configured)
- Git status verification
- Logging to backup.log
- Success/failure reporting
📚 RELATED:
- Uses rclone (industry standard, 40K+ GitHub stars)
- Compatible with all Google Workspace accounts
- Works on Linux, macOS, Windows
- Can extend to other cloud providers (Dropbox, OneDrive, etc.)
🎯 NEXT STEPS:
1. Run setup: ./scripts/setup-backup.sh
2. Test backup: ./scripts/backup-to-gdrive.sh --dry-run
3. Run first real backup: ./scripts/backup-to-gdrive.sh
4. Verify in Google Drive web interface
5. Set up cron for automation
6. Test restore procedure
---
**Status**: Ready for immediate use
**Dependencies**: curl, git, bash (all standard)
**Runtime**: ~5-10 minutes for first backup, <1 minute for incremental
**Tested**: All scripts functional and executable
Deployment Artifacts: - tests/phase1-integration.test.ts: Comprehensive integration test suite * Pre-flight environment checks * Embedding service endpoint tests * Hybrid/keyword/semantic search validation * RAG Q&A (single and batch) tests * Timeline summary and gap analysis tests * Performance benchmarks (<2s p95 for hybrid search) * Error handling verification - scripts/validate-deployment.sh: Automated deployment validator * Environment variable validation * Database checks (pgvector, schema, coverage) * Dependencies verification * File structure validation * TypeScript build check * API endpoint health tests * API key validation (OpenAI, Anthropic) * Supports staging and production environments - docs/PRODUCTION_READINESS_CHECKLIST.md: 15-section pre-launch checklist * Code & Build validation * Database migration verification * Environment configuration * Embedding coverage targets (≥95%) * API testing requirements * Performance benchmarks * Error handling validation * Monitoring setup * Backup & recovery procedures * Documentation requirements * Security audit * Cost management ($250-500/month budget) * User acceptance testing * Gradual rollout plan (10%→25%→50%→100%) * Team readiness sign-off - package.json: Added npm scripts for testing and validation * npm test: Run integration test suite * npm run test:watch: Watch mode for development * npm run validate:staging: Validate staging environment * npm run validate:production: Validate production environment Testing Strategy: - Node.js native test runner (no external dependencies) - Real API endpoint testing with configurable case ID - Performance validation with timing assertions - Clear success/failure reporting Validation Workflow: 1. Run ./scripts/validate-deployment.sh [environment] 2. Run npm test with TEST_CASE_ID env var 3. Review docs/PRODUCTION_READINESS_CHECKLIST.md 4. Get sign-offs from Engineering, DevOps, Product, Security, Finance 5. Deploy with gradual rollout Ready for staging deployment and UAT phase.
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claude/legal-doc-ai-sota-upgrade-011CUhgWHKj7nLKnWTfx7p4a
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