diff --git a/docs/BACKUP_SETUP_GUIDE.md b/docs/BACKUP_SETUP_GUIDE.md
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+# Google Drive Backup Setup Guide
+
+**Quick Start**: Get automated backups to Google Drive in 10 minutes
+
+## Step 1: Install rclone
+
+```bash
+# Install rclone
+curl https://rclone.org/install.sh | sudo bash
+
+# Verify installation
+rclone version
+```
+
+## Step 2: Configure Google Drive
+
+```bash
+# Start configuration wizard
+rclone config
+
+# Follow these prompts:
+# n) New remote
+# name> gdrive
+# Storage> drive (or type number for Google Drive)
+# client_id> [press Enter to use defaults]
+# client_secret> [press Enter to use defaults]
+# scope> 1 (Full access)
+# root_folder_id> [press Enter]
+# service_account_file> [press Enter]
+# Edit advanced config? n
+# Use auto config? y (this will open a browser)
+#
+# [Authenticate in browser when it opens]
+#
+# Configure this as a team drive? n
+# y) Yes this is OK
+# q) Quit config
+```
+
+**Important**: The browser window will open for Google OAuth authentication. Sign in with the Google account where you want backups stored.
+
+## Step 3: Test the Connection
+
+```bash
+# List your Google Drive files
+rclone ls gdrive:
+
+# Create a test file
+echo "test" > /tmp/test.txt
+rclone copy /tmp/test.txt gdrive:test/
+
+# Verify it was uploaded
+rclone ls gdrive:test/
+
+# Clean up
+rclone delete gdrive:test/test.txt
+rclone rmdir gdrive:test/
+rm /tmp/test.txt
+```
+
+## Step 4: Run Your First Backup
+
+```bash
+# Navigate to the repo
+cd /home/user/chittychronicle
+
+# Test backup (dry run - won't actually copy anything)
+./scripts/backup-to-gdrive.sh --dry-run
+
+# Run actual backup
+./scripts/backup-to-gdrive.sh
+```
+
+**What gets backed up:**
+- ✅ All source code
+- ✅ Full git history
+- ✅ Documentation
+- ✅ Configuration files
+- ❌ node_modules (excluded - can be reinstalled)
+- ❌ dist/ build artifacts (excluded)
+- ❌ .env files (excluded for security)
+- ❌ Log files (excluded)
+
+## Step 5: Verify Backup in Google Drive
+
+```bash
+# List backups
+rclone ls gdrive:backups/
+
+# You should see:
+# - backups/chittychronicle/ (full repository sync)
+# - backups/bundles/chittychronicle-YYYYMMDD.bundle (daily bundles)
+```
+
+Or check in your Google Drive web interface:
+- Visit https://drive.google.com
+- Look for `backups/` folder
+
+## Step 6: Automate Daily Backups (Optional)
+
+```bash
+# Open crontab editor
+crontab -e
+
+# Add this line (backup daily at 2 AM):
+0 2 * * * /home/user/chittychronicle/scripts/backup-to-gdrive.sh >> /home/user/backup-cron.log 2>&1
+
+# Or weekly on Sundays at 3 AM:
+0 3 * * 0 /home/user/chittychronicle/scripts/backup-to-gdrive.sh >> /home/user/backup-cron.log 2>&1
+
+# Save and exit
+```
+
+## Restoring from Backup
+
+### Option A: Restore from Live Sync
+
+```bash
+# Download entire repo
+rclone sync gdrive:backups/chittychronicle/ /home/user/chittychronicle-restored/
+
+# This gives you a complete working copy
+cd /home/user/chittychronicle-restored
+git status  # Should show a clean working tree
+```
+
+### Option B: Restore from Bundle
+
+```bash
+# List available bundles
+rclone ls gdrive:backups/bundles/
+
+# Download specific bundle
+rclone copy gdrive:backups/bundles/chittychronicle-20251102.bundle ./
+
+# Clone from bundle
+git clone chittychronicle-20251102.bundle chittychronicle-restored
+
+# You now have a complete repo with full history
+cd chittychronicle-restored
+git log  # See all commits
+```
+
+### Option C: Restore Specific Files
+
+```bash
+# Copy just the docs folder
+rclone copy gdrive:backups/chittychronicle/docs/ ./docs-backup/
+
+# Copy a specific file
+rclone copy gdrive:backups/chittychronicle/package.json ./
+```
+
+## Manual Backup Commands
+
+### Full Sync
+```bash
+rclone sync /home/user/chittychronicle/ gdrive:backups/chittychronicle/ \
+    --exclude='node_modules/**' \
+    --exclude='dist/**' \
+    --progress
+```
+
+### Create Bundle Backup
+```bash
+cd /home/user/chittychronicle
+git bundle create /tmp/backup.bundle --all
+rclone copy /tmp/backup.bundle gdrive:backups/bundles/
+rm /tmp/backup.bundle
+```
+
+### Check Backup Status
+```bash
+# Compare local vs remote
+rclone check /home/user/chittychronicle/ gdrive:backups/chittychronicle/ \
+    --exclude='node_modules/**' \
+    --exclude='dist/**'
+```
+
+## Troubleshooting
+
+### Error: "gdrive remote not found"
+```bash
+# List configured remotes
+rclone listremotes
+
+# If 'gdrive:' is not listed, reconfigure:
+rclone config
+```
+
+### Error: "Failed to authenticate"
+```bash
+# Delete existing config and reconfigure
+rclone config delete gdrive
+rclone config
+# Follow setup prompts again
+```
+
+### Slow uploads
+```bash
+# Use --transfers flag to upload multiple files simultaneously
+rclone sync /home/user/chittychronicle/ gdrive:backups/chittychronicle/ \
+    --transfers=8 \
+    --exclude='node_modules/**'
+```
+
+### Too many small files
+```bash
+# Use --fast-list for directories with many files
+rclone sync /home/user/chittychronicle/ gdrive:backups/chittychronicle/ \
+    --fast-list \
+    --exclude='node_modules/**'
+```
+
+## Advanced: Encryption
+
+To encrypt backups before uploading to Google Drive:
+
+```bash
+# Configure encrypted remote
+rclone config
+
+# n) New remote
+# name> gdrive-crypt
+# Storage> crypt
+# remote> gdrive:backups/chittychronicle-encrypted
+# filename_encryption> standard
+# directory_name_encryption> true
+# password> [enter a strong password]
+# confirm password> [confirm]
+# salt password> [enter or press Enter to generate]
+
+# Now sync to encrypted remote
+rclone sync /home/user/chittychronicle/ gdrive-crypt: \
+    --exclude='node_modules/**'
+```
+
+**Files will be encrypted before upload. Only you can decrypt them with your password.**
+
+## Cost Considerations
+
+**Google Drive Free Tier**: 15 GB
+- ChittyChronicle repo: ~50-200 MB (without node_modules)
+- Daily bundles: ~50 MB each
+- **You can store 30-90 days of daily bundles within free tier**
+
+**Google Drive Paid** ($1.99/month for 100 GB):
+- Plenty of space for years of backups
+- Can keep unlimited bundle history
+
+## Backup Strategy Recommendation
+
+**Daily**:
+- Full sync with `rclone sync` (keeps live copy up-to-date)
+
+**Weekly**:
+- Create git bundle snapshot (timestamped, easy to restore specific dates)
+
+**Monthly**:
+- Download one bundle locally as extra safety backup
+- Delete bundles older than 90 days to save space
+
+**Result**:
+- Always have latest code in Google Drive
+- Can restore to any point in the last 90 days
+- Complete git history preserved
+- Costs nothing (or $1.99/month for peace of mind)
+
+## Security Best Practices
+
+1. **Never commit `.env` files** - Already excluded in backup script
+2. **Use encrypted remotes** for extra security (see Advanced section)
+3. **Use strong Google account password** + 2FA
+4. **Don't share rclone config** - Contains OAuth tokens
+5. **Regularly test restores** - Backups are useless if you can't restore
+
+## Next Steps
+
+After setting up backups, consider:
+
+1. **Test a restore** - Make sure you can actually recover your code
+2. **Set up monitoring** - Check `backup.log` weekly
+3. **Automate cleanup** - Delete old bundles after 90 days
+4. **Document recovery procedures** - So anyone on the team can restore
+
+---
+
+**Questions?**
+- rclone docs: https://rclone.org/docs/
+- rclone forum: https://forum.rclone.org/
diff --git a/docs/PHASE1_DEPLOYMENT_GUIDE.md b/docs/PHASE1_DEPLOYMENT_GUIDE.md
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+# Phase 1 Deployment Guide: Semantic Search Foundation
+
+**Version**: 1.0
+**Date**: 2025-11-01
+**Status**: Ready for Deployment
+
+## Overview
+
+This guide walks through deploying Phase 1 of the SOTA upgrade: **Semantic Search Foundation**. After completing these steps, ChittyChronicle will have:
+
+✅ Vector embeddings for semantic document understanding
+✅ Hybrid search combining keyword + semantic algorithms
+✅ RAG-powered document Q&A with Claude Sonnet 4
+✅ 50-70% improvement in search relevance
+
+## Prerequisites
+
+### Required
+
+- [ ] **PostgreSQL 14+ with pgvector support** (NeonDB recommended)
+- [ ] **OpenAI API Key** for embedding generation
+- [ ] **Anthropic API Key** (already configured for contradiction detection)
+- [ ] **Node.js 20+** and npm
+- [ ] **Database admin access** to run migrations
+- [ ] **Budget approval** for ongoing API costs ($250-500/month)
+
+### Recommended
+
+- [ ] Staging environment for testing
+- [ ] Monitoring/logging infrastructure
+- [ ] Backup of current database
+- [ ] Load testing plan
+
+## Step 1: Environment Setup
+
+### 1.1 Add Environment Variables
+
+Add the following to your `.env` file:
+
+```bash
+# OpenAI for Embeddings (REQUIRED)
+OPENAI_API_KEY=sk-...
+
+# Embedding Configuration
+EMBEDDING_MODEL=text-embedding-3-small
+EMBEDDING_DIMENSIONS=1536
+
+# Feature Flags
+ENABLE_HYBRID_SEARCH=true
+ENABLE_RAG=true
+
+# Optional: Legal-BERT (future enhancement)
+ENABLE_LEGAL_BERT=false
+```
+
+### 1.2 Verify API Keys
+
+```bash
+# Test OpenAI connection
+curl https://api.openai.com/v1/models \
+  -H "Authorization: Bearer $OPENAI_API_KEY" \
+  | jq '.data[0].id'
+
+# Test Anthropic connection (should already work)
+curl https://api.anthropic.com/v1/messages \
+  -H "x-api-key: $ANTHROPIC_API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -H "content-type: application/json" \
+  -d '{"model":"claude-sonnet-4-20250514","max_tokens":10,"messages":[{"role":"user","content":"Hi"}]}'
+```
+
+## Step 2: Database Migration
+
+### 2.1 Install pgvector Extension
+
+**For NeonDB** (recommended):
+
+```sql
+-- Connect to your database and run:
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- Verify installation:
+SELECT * FROM pg_extension WHERE extname = 'vector';
+```
+
+**For self-hosted PostgreSQL**:
+
+```bash
+# Install pgvector (Ubuntu/Debian)
+sudo apt install postgresql-14-pgvector
+
+# Or build from source
+git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git
+cd pgvector
+make
+sudo make install
+
+# Then connect and enable
+psql -d your_database -c "CREATE EXTENSION vector;"
+```
+
+### 2.2 Run Database Migration
+
+```bash
+# Apply the pgvector migration
+psql -d $DATABASE_URL -f migrations/001_add_pgvector.sql
+
+# Verify vector columns were added
+psql -d $DATABASE_URL -c "\d timeline_entries" | grep embedding
+
+# Should show:
+# description_embedding | character varying
+# content_embedding    | character varying
+# embedding_model      | character varying(100)
+# embedding_generated_at | timestamp without time zone
+```
+
+### 2.3 Verify Migration Success
+
+```bash
+# Check embedding coverage view
+psql -d $DATABASE_URL -c "SELECT * FROM embedding_coverage;"
+
+# Should return:
+#   table_name      | total_records | embedded_records | coverage_percentage
+# ------------------+---------------+------------------+--------------------
+#  timeline_entries |           100 |                0 |                0.00
+#  timeline_sources |            50 |                0 |                0.00
+```
+
+## Step 3: Code Deployment
+
+### 3.1 Pull Latest Code
+
+```bash
+git checkout claude/legal-doc-ai-sota-upgrade-011CUhgWHKj7nLKnWTfx7p4a
+git pull origin claude/legal-doc-ai-sota-upgrade-011CUhgWHKj7nLKnWTfx7p4a
+```
+
+### 3.2 Install Dependencies
+
+No new dependencies required! Phase 1 uses existing packages:
+- `openai` (already installed)
+- `@anthropic-ai/sdk` (already installed)
+- `drizzle-orm` (already installed)
+
+### 3.3 Build Application
+
+```bash
+# Type check
+npm run check
+
+# Build for production
+npm run build
+```
+
+### 3.4 Update Routes
+
+Add SOTA routes to your server initialization in `server/index.ts`:
+
+```typescript
+// Add this import at the top
+import { registerSOTARoutes } from "./sotaRoutes";
+
+// After existing routes, add:
+if (process.env.ENABLE_HYBRID_SEARCH === 'true') {
+  registerSOTARoutes(app);
+}
+```
+
+## Step 4: Initial Embedding Generation
+
+### 4.1 Estimate Cost
+
+```bash
+# Check how many entries need embedding
+npm run embeddings:coverage
+
+# Output example:
+# Timeline Entries:
+#   Total: 1000
+#   Embedded: 0
+#   Coverage: 0.0%
+```
+
+**Cost Calculation**:
+- Average legal document: ~500 tokens
+- 1000 documents = ~500,000 tokens
+- OpenAI pricing: $0.02 per 1M tokens
+- **Estimated cost**: ~$0.01 for 1000 documents
+
+### 4.2 Generate Embeddings (Staging First!)
+
+```bash
+# Test on a single case first
+npm run embeddings:case=<case-uuid>
+
+# Monitor progress
+# This will show:
+# - Number of entries processed
+# - Tokens used
+# - Estimated cost
+# - Any errors
+
+# If successful, generate for all
+npm run embeddings:generate
+
+# This runs in batches of 100 with 1-second delays
+# For 1000 entries, expect ~10-15 minutes
+```
+
+### 4.3 Verify Embedding Coverage
+
+```bash
+# Check final coverage
+npm run embeddings:coverage
+
+# Should show:
+# Timeline Entries:
+#   Total: 1000
+#   Embedded: 1000
+#   Coverage: 100.0%
+```
+
+## Step 5: Testing
+
+### 5.1 Test Hybrid Search Endpoint
+
+```bash
+# Test hybrid search
+curl "http://localhost:5000/api/timeline/search/hybrid?caseId=<uuid>&query=contract%20breach&alpha=0.6" \
+  -H "Cookie: connect.sid=<your-session-cookie>"
+
+# Expected response:
+# {
+#   "results": [...],
+#   "metadata": {
+#     "query": "contract breach",
+#     "totalResults": 10,
+#     "searchType": "hybrid",
+#     "executionTimeMs": 450,
+#     "alpha": 0.6
+#   }
+# }
+```
+
+### 5.2 Test RAG Q&A Endpoint
+
+```bash
+# Test document Q&A
+curl -X POST "http://localhost:5000/api/timeline/ask" \
+  -H "Content-Type: application/json" \
+  -H "Cookie: connect.sid=<your-session-cookie>" \
+  -d '{
+    "caseId": "<uuid>",
+    "question": "What evidence supports the breach of contract claim?",
+    "topK": 5
+  }'
+
+# Expected response:
+# {
+#   "answer": "Based on the timeline entries, the following evidence supports...",
+#   "sources": [
+#     {
+#       "entryId": "...",
+#       "description": "...",
+#       "date": "2024-01-15",
+#       "relevanceScore": 0.85,
+#       "citation": "[1]"
+#     }
+#   ],
+#   "confidence": 0.82
+# }
+```
+
+### 5.3 Test Keyword vs Semantic vs Hybrid
+
+```bash
+# Compare search methods
+QUERY="force majeure clause"
+CASE_ID="<uuid>"
+
+# Keyword-only
+curl "http://localhost:5000/api/timeline/search/keyword?caseId=$CASE_ID&query=$QUERY"
+
+# Semantic-only
+curl "http://localhost:5000/api/timeline/search/semantic?caseId=$CASE_ID&query=$QUERY"
+
+# Hybrid (best results)
+curl "http://localhost:5000/api/timeline/search/hybrid?caseId=$CASE_ID&query=$QUERY&alpha=0.6"
+```
+
+### 5.4 Run Integration Tests
+
+Create test queries that validate:
+- [x] Exact keyword matches still work
+- [x] Semantic matches find related concepts
+- [x] Hybrid combines both effectively
+- [x] Citations are accurate in RAG responses
+- [x] Response times are acceptable (<1 second)
+
+## Step 6: Production Deployment
+
+### 6.1 Staging Validation Checklist
+
+- [ ] All embeddings generated successfully (100% coverage)
+- [ ] Hybrid search returns relevant results
+- [ ] RAG Q&A provides accurate citations
+- [ ] Response times meet SLA (<1 second p95)
+- [ ] No errors in logs
+- [ ] Cost tracking is accurate
+
+### 6.2 Production Rollout
+
+**Option A: Gradual Rollout** (Recommended)
+
+```typescript
+// server/index.ts
+const HYBRID_SEARCH_ROLLOUT_PERCENTAGE = 0.1; // Start with 10%
+
+app.get('/api/timeline/search', async (req, res) => {
+  const useHybrid = Math.random() < HYBRID_SEARCH_ROLLOUT_PERCENTAGE;
+
+  if (useHybrid && process.env.ENABLE_HYBRID_SEARCH === 'true') {
+    // Use new hybrid search
+    return await searchService.hybridSearch({ /* ... */ });
+  } else {
+    // Use existing keyword search
+    return await storage.searchTimelineEntries(/* ... */);
+  }
+});
+```
+
+Increase percentage over 2 weeks:
+- Week 1: 10% → 25% → 50%
+- Week 2: 75% → 100%
+
+**Option B: Feature Flag** (Safer)
+
+```typescript
+// Let users opt-in via UI preference
+if (user.preferences?.useSemanticSearch) {
+  return await searchService.hybridSearch({ /* ... */ });
+}
+```
+
+**Option C: New Endpoints Only** (Safest)
+
+Keep existing `/api/timeline/search` unchanged.
+New features only available at `/api/timeline/search/hybrid`.
+
+### 6.3 Monitoring Setup
+
+```bash
+# Add monitoring for:
+# - Embedding generation rate
+# - Search response times
+# - API costs (OpenAI + Anthropic)
+# - Error rates
+# - User satisfaction (track click-through rates)
+```
+
+**Key Metrics**:
+- `hybrid_search_latency_ms` (target: p95 <1000ms)
+- `embedding_coverage_percentage` (target: >95%)
+- `rag_confidence_score` (target: >0.7 average)
+- `monthly_api_cost_usd` (budget: $250-500)
+
+## Step 7: Ongoing Operations
+
+### 7.1 Automatic Embedding Generation
+
+Set up triggers to embed new entries automatically:
+
+```typescript
+// server/routes.ts
+// After creating a timeline entry:
+app.post('/api/timeline/entries', async (req, res) => {
+  const entry = await storage.createTimelineEntry(/* ... */);
+
+  // Generate embedding asynchronously (non-blocking)
+  embeddingService.embedTimelineEntry(entry.id)
+    .catch(err => console.error('Embedding generation failed:', err));
+
+  return res.json(entry);
+});
+```
+
+### 7.2 Nightly Batch Job
+
+```bash
+# Add to cron (every night at 2 AM):
+0 2 * * * cd /path/to/chittychronicle && npm run embeddings:generate >> /var/log/embeddings.log 2>&1
+```
+
+### 7.3 Cost Monitoring
+
+```bash
+# Weekly cost report
+curl "http://localhost:5000/api/admin/embeddings/coverage" | \
+  jq '.coverage.timelineEntries.embedded' | \
+  awk '{print "Approximate monthly cost: $" ($1 * 500 / 1000000 * 0.02 * 30)}'
+```
+
+### 7.4 Performance Tuning
+
+**If search is slow** (>1 second):
+
+```sql
+-- Increase IVFFlat index lists parameter
+DROP INDEX timeline_entries_content_embedding_idx;
+CREATE INDEX timeline_entries_content_embedding_idx
+ON timeline_entries
+USING ivfflat (content_embedding vector_cosine_ops)
+WITH (lists = 200); -- Increase from 100
+
+-- Run ANALYZE to update statistics
+ANALYZE timeline_entries;
+```
+
+**If embedding costs are high**:
+
+- Switch to batch processing (100+ at a time)
+- Only embed entries with substantial text (skip short descriptions)
+- Consider self-hosted Legal-BERT (Phase 2)
+
+## Step 8: User Training
+
+### 8.1 Create User Documentation
+
+Document the new capabilities:
+- **Semantic Search**: "Find documents by meaning, not just keywords"
+- **Example Queries**:
+  - "breach of duty" (finds "violation of fiduciary responsibility")
+  - "force majeure events" (finds "acts of God", "unforeseeable circumstances")
+  - "email correspondence about settlement" (finds related communications)
+
+### 8.2 Internal Demo
+
+- Show side-by-side: keyword vs semantic vs hybrid
+- Demonstrate RAG Q&A answering complex questions
+- Highlight citation accuracy
+
+### 8.3 Feedback Loop
+
+- Add "Was this helpful?" buttons to search results
+- Track which search method users prefer
+- Monitor support tickets for search-related issues
+
+## Troubleshooting
+
+### Issue: pgvector extension not found
+
+```bash
+# Verify PostgreSQL version
+psql --version  # Must be 11+
+
+# Install pgvector
+sudo apt install postgresql-14-pgvector
+
+# Restart PostgreSQL
+sudo systemctl restart postgresql
+```
+
+### Issue: OpenAI API rate limits
+
+```bash
+# Reduce batch size
+npm run embeddings:generate --batch-size=20
+
+# Add delays between batches (already implemented)
+```
+
+### Issue: Embeddings not improving search
+
+```bash
+# Verify embeddings were generated
+psql -d $DATABASE_URL -c "
+  SELECT COUNT(*) as total,
+         COUNT(content_embedding) as embedded
+  FROM timeline_entries;
+"
+
+# Check embedding dimensions
+psql -d $DATABASE_URL -c "
+  SELECT embedding_model, COUNT(*)
+  FROM timeline_entries
+  WHERE content_embedding IS NOT NULL
+  GROUP BY embedding_model;
+"
+```
+
+### Issue: RAG provides inaccurate answers
+
+- Lower temperature (already set to 0.1)
+- Increase `topK` to retrieve more context
+- Add explicit instructions to system prompt
+- Verify source citations manually
+
+## Success Criteria
+
+Phase 1 deployment is successful when:
+
+- ✅ **100% embedding coverage** on active timeline entries
+- ✅ **Search recall improved 50-70%** vs keyword-only baseline
+- ✅ **p95 response time <1 second** for hybrid search
+- ✅ **User satisfaction ≥85%** "found what I was looking for"
+- ✅ **RAG accuracy ≥80%** on evaluation dataset
+- ✅ **Monthly costs within budget** ($250-500)
+- ✅ **Zero production incidents** from new code
+
+## Next Steps
+
+After successful Phase 1 deployment:
+
+1. **Gather user feedback** (2 weeks)
+2. **Analyze metrics** (search improvement, costs, satisfaction)
+3. **Decision gate for Phase 2** (Document Classification)
+4. **Prepare Phase 2 deployment plan** if proceeding
+
+## Support
+
+- **Technical issues**: engineering@chittychronicle.com
+- **API cost questions**: finance@chittychronicle.com
+- **User feedback**: product@chittychronicle.com
+
+---
+
+**Document Version**: 1.0
+**Last Updated**: 2025-11-01
+**Next Review**: 2025-11-15 (after deployment)
diff --git a/docs/PRODUCTION_READINESS_CHECKLIST.md b/docs/PRODUCTION_READINESS_CHECKLIST.md
new file mode 100644
index 0000000..ef94399
--- /dev/null
+++ b/docs/PRODUCTION_READINESS_CHECKLIST.md
@@ -0,0 +1,416 @@
+# Phase 1 Production Readiness Checklist
+
+**Version**: 1.0
+**Target Launch**: January 20, 2026
+**Last Updated**: November 2, 2025
+
+Use this checklist to verify Phase 1 is production-ready before deploying.
+
+---
+
+## ✅ Pre-Deployment Checklist
+
+### 1. Code & Build
+
+- [ ] All Phase 1 code merged to main branch
+- [ ] TypeScript compiles without errors (`npm run check`)
+- [ ] Production build succeeds (`npm run build`)
+- [ ] No critical security vulnerabilities (`npm audit`)
+- [ ] Dependencies up to date (no outdated critical packages)
+- [ ] Git repository clean (no uncommitted changes)
+- [ ] Version tag created (e.g., `v1.1.0-phase1`)
+
+**Validation Command**:
+```bash
+npm run check && npm run build && npm audit --production
+```
+
+---
+
+### 2. Database
+
+- [ ] pgvector extension installed on production database
+- [ ] Migration `001_add_pgvector.sql` applied successfully
+- [ ] Vector columns exist on `timeline_entries` and `timeline_sources`
+- [ ] IVFFlat indexes created (verify with `\d timeline_entries`)
+- [ ] `embedding_coverage` view exists and queryable
+- [ ] `find_similar_entries()` function exists
+- [ ] Database backup completed before migration
+- [ ] Rollback plan documented
+
+**Validation Command**:
+```bash
+psql $DATABASE_URL -c "\d timeline_entries" | grep embedding
+psql $DATABASE_URL -c "SELECT * FROM embedding_coverage;"
+```
+
+---
+
+### 3. Environment Variables
+
+**Production Environment** (`.env.production` or equivalent):
+
+- [ ] `DATABASE_URL` - PostgreSQL connection string (with pgvector)
+- [ ] `OPENAI_API_KEY` - Valid OpenAI API key
+- [ ] `ANTHROPIC_API_KEY` - Valid Anthropic API key
+- [ ] `EMBEDDING_MODEL=text-embedding-3-small`
+- [ ] `EMBEDDING_DIMENSIONS=1536`
+- [ ] `ENABLE_HYBRID_SEARCH=true`
+- [ ] `ENABLE_RAG=true`
+- [ ] `NODE_ENV=production`
+- [ ] `PORT=5000` (or your production port)
+
+**Security Check**:
+- [ ] No `.env` files committed to git
+- [ ] API keys rotated from staging keys
+- [ ] Secrets stored in secure vault (not plaintext)
+
+**Validation Command**:
+```bash
+./scripts/validate-deployment.sh production
+```
+
+---
+
+### 4. Embeddings
+
+- [ ] Embedding generation tested on staging
+- [ ] Initial embedding job completed for production data
+- [ ] Embedding coverage ≥95% of active timeline entries
+- [ ] No failures in embedding generation logs
+- [ ] Cost per 1000 documents validated (~$0.01)
+- [ ] Monthly cost projection within budget ($250-500)
+
+**Validation Commands**:
+```bash
+# Check coverage
+curl http://localhost:5000/api/admin/embeddings/coverage
+
+# Or via npm script
+npm run embeddings:coverage
+
+# Generate if needed
+npm run embeddings:generate
+```
+
+**Coverage Target**: ≥95% of timeline entries should have embeddings
+
+---
+
+### 5. API Testing
+
+All endpoints tested and passing:
+
+- [ ] `GET /api/timeline/search/hybrid` - Hybrid search works
+- [ ] `GET /api/timeline/search/keyword` - Keyword fallback works
+- [ ] `GET /api/timeline/search/semantic` - Semantic search works
+- [ ] `POST /api/timeline/ask` - RAG Q&A works
+- [ ] `POST /api/timeline/ask/batch` - Batch queries work
+- [ ] `GET /api/timeline/summary/:caseId` - Summary generation works
+- [ ] `GET /api/timeline/analyze/gaps/:caseId` - Gap analysis works
+- [ ] `POST /api/admin/embeddings/generate` - Embedding job starts
+- [ ] `GET /api/admin/embeddings/coverage` - Coverage stats work
+- [ ] `POST /api/admin/embeddings/estimate-cost` - Cost estimation works
+
+**Validation Command**:
+```bash
+TEST_CASE_ID=<uuid> npm test
+```
+
+---
+
+### 6. Performance
+
+- [ ] Hybrid search p95 latency <1000ms
+- [ ] Keyword search p95 latency <500ms
+- [ ] RAG Q&A p95 latency <3000ms
+- [ ] Embedding generation handles batch of 100 without timeout
+- [ ] Database query performance acceptable (pgvector indexes working)
+- [ ] Load testing completed (if expected high traffic)
+
+**Performance Targets**:
+- Hybrid search: <1000ms p95
+- Keyword search: <500ms p95
+- RAG Q&A: <3000ms p95
+
+**Load Test** (optional):
+```bash
+# Use Apache Bench or similar
+ab -n 100 -c 10 "http://localhost:5000/api/timeline/search/hybrid?caseId=<uuid>&query=test"
+```
+
+---
+
+### 7. Error Handling
+
+- [ ] Graceful fallback when embeddings unavailable (uses keyword search)
+- [ ] Graceful fallback when OpenAI API fails
+- [ ] Graceful fallback when Anthropic API fails
+- [ ] Proper error messages returned to client (no stack traces)
+- [ ] Error logging configured
+- [ ] Sentry/error tracking integrated (optional but recommended)
+
+**Test Scenarios**:
+- Invalid case ID → 400 error with clear message
+- Empty query → 400 error
+- API key revoked → Falls back to keyword search
+- Database down → Returns 500 with generic message
+
+---
+
+### 8. Monitoring & Observability
+
+- [ ] Application logs configured (stdout/file)
+- [ ] Log rotation configured (if file-based)
+- [ ] Error alerting configured (email/Slack/PagerDuty)
+- [ ] Performance metrics dashboard (optional)
+- [ ] API usage tracking (OpenAI + Anthropic)
+- [ ] Cost monitoring dashboard
+- [ ] Uptime monitoring configured
+
+**Recommended Metrics**:
+- Request latency (p50, p95, p99)
+- Error rate (5xx responses)
+- API call count (OpenAI, Anthropic)
+- Monthly API cost ($)
+- Embedding coverage (%)
+- Search result quality (click-through rate)
+
+**Tools** (choose one or more):
+- Datadog
+- New Relic
+- Prometheus + Grafana
+- CloudWatch (if on AWS)
+- Simple logs + cron email
+
+---
+
+### 9. Backup & Recovery
+
+- [ ] Database backup strategy documented
+- [ ] Database backup tested and verified
+- [ ] Application backup (code + config) to Google Drive via rclone
+- [ ] Rollback procedure documented
+- [ ] Restore procedure tested
+- [ ] Disaster recovery runbook created
+
+**Backup Frequency**:
+- Database: Daily (automated)
+- Code: On every deployment (git tag)
+- Google Drive: Daily (via rclone script)
+
+**Recovery Time Objective**: <4 hours
+
+---
+
+### 10. Documentation
+
+- [ ] `PHASE1_DEPLOYMENT_GUIDE.md` reviewed and accurate
+- [ ] API documentation updated (OpenAPI spec or equivalent)
+- [ ] User-facing documentation created ("How to use semantic search")
+- [ ] Internal runbook created (operations team)
+- [ ] Troubleshooting guide created
+- [ ] Known issues documented
+
+**Required Docs**:
+- Deployment guide (for engineers)
+- User guide (for end users)
+- Operations runbook (for on-call team)
+- Troubleshooting FAQ
+
+---
+
+### 11. Security
+
+- [ ] API keys stored securely (vault/secrets manager)
+- [ ] Database credentials rotated
+- [ ] HTTPS enabled (TLS/SSL certificate valid)
+- [ ] Authentication required for all endpoints
+- [ ] Rate limiting configured (prevent abuse)
+- [ ] Input validation on all endpoints
+- [ ] SQL injection protection (using parameterized queries)
+- [ ] No sensitive data in logs (API keys, user data)
+- [ ] CORS configured appropriately
+- [ ] Security headers configured (CSP, X-Frame-Options, etc.)
+
+**Security Scan**:
+```bash
+npm audit --production
+# Review and fix any high/critical vulnerabilities
+```
+
+---
+
+### 12. Cost Management
+
+- [ ] Monthly budget approved ($250-500 for Phase 1)
+- [ ] Cost alerts configured (notify if >$500/month)
+- [ ] API usage limits set (prevent runaway costs)
+- [ ] Cost tracking dashboard created
+- [ ] Cost optimization reviewed (batching, caching, etc.)
+
+**Cost Breakdown** (monthly estimate):
+- OpenAI embeddings: $50-150
+- Anthropic RAG: $100-200
+- Compute/hosting: $100-150
+- **Total**: $250-500/month
+
+**Budget Alerts**:
+- Warning at $400/month
+- Critical at $600/month
+
+---
+
+### 13. User Acceptance Testing
+
+- [ ] Beta users identified and invited
+- [ ] User feedback mechanism in place
+- [ ] User satisfaction survey prepared
+- [ ] Success metrics defined (search relevance, time saved)
+- [ ] A/B testing configured (new vs old search) - optional
+- [ ] Feedback loop documented
+
+**UAT Checklist**:
+- 5-10 beta users
+- 2 weeks testing period
+- Daily feedback collection
+- Success criteria: ≥80% satisfaction
+
+---
+
+### 14. Rollout Plan
+
+- [ ] Gradual rollout strategy defined (10% → 25% → 50% → 100%)
+- [ ] Feature flag configured (`ENABLE_HYBRID_SEARCH`)
+- [ ] Rollout schedule created
+- [ ] Rollback criteria defined
+- [ ] Communication plan created (announce to users)
+
+**Rollout Schedule** (recommended):
+- Week 1: 10% of users
+- Week 2: 25% of users (if no issues)
+- Week 3: 50% of users
+- Week 4: 100% rollout
+
+**Rollback Triggers**:
+- Error rate >5%
+- Latency p95 >2000ms
+- User complaints >10%
+- API costs >$1000/month
+
+---
+
+### 15. Team Readiness
+
+- [ ] Engineering team trained on Phase 1 architecture
+- [ ] Operations team trained on deployment procedure
+- [ ] Support team trained on new features
+- [ ] On-call rotation scheduled
+- [ ] Escalation path documented
+- [ ] Post-deployment support plan
+
+**Training Materials**:
+- Architecture diagram
+- Deployment runbook
+- Troubleshooting guide
+- FAQ document
+
+---
+
+## 🚀 Pre-Launch Validation
+
+**Final Validation** (run this 24 hours before launch):
+
+```bash
+# 1. Run deployment validation script
+./scripts/validate-deployment.sh production
+
+# 2. Run integration tests
+TEST_CASE_ID=<production-case-id> npm test
+
+# 3. Check embedding coverage
+npm run embeddings:coverage
+
+# 4. Verify API keys
+curl -H "Authorization: Bearer $OPENAI_API_KEY" https://api.openai.com/v1/models
+
+# 5. Database health check
+psql $DATABASE_URL -c "SELECT * FROM embedding_coverage;"
+
+# 6. Performance spot check
+time curl "http://localhost:5000/api/timeline/search/hybrid?caseId=<uuid>&query=test"
+```
+
+**All checks must pass before proceeding with launch.**
+
+---
+
+## 📊 Success Criteria
+
+Phase 1 is successful if after 30 days:
+
+- [ ] **Search recall improved 50-70%** vs keyword-only baseline
+- [ ] **User satisfaction ≥85%** ("found what I was looking for")
+- [ ] **p95 response time <1000ms** for hybrid search
+- [ ] **RAG accuracy ≥80%** on evaluation dataset
+- [ ] **Monthly costs <$500** (within budget)
+- [ ] **Zero critical production incidents**
+- [ ] **Uptime ≥99.5%**
+
+---
+
+## 🚨 Go/No-Go Decision
+
+**Go** if:
+- ✅ All checklist items completed
+- ✅ Validation script passes without errors
+- ✅ Integration tests pass
+- ✅ Performance meets targets
+- ✅ Team trained and ready
+
+**No-Go** if:
+- ❌ Critical checklist items incomplete
+- ❌ Validation script has errors
+- ❌ Performance below targets
+- ❌ Team not ready
+- ❌ Budget not approved
+
+---
+
+## 📝 Sign-Off
+
+**Required Approvals** before production deployment:
+
+- [ ] **Engineering Lead**: Code quality, architecture, tests
+- [ ] **DevOps Lead**: Infrastructure, deployment, monitoring
+- [ ] **Product Manager**: Features complete, user impact understood
+- [ ] **Security Team**: Security review passed
+- [ ] **Finance**: Budget approved
+
+**Signatures**:
+
+| Role | Name | Date | Signature |
+|------|------|------|-----------|
+| Engineering Lead | __________ | ______ | _________ |
+| DevOps Lead | __________ | ______ | _________ |
+| Product Manager | __________ | ______ | _________ |
+| Security Team | __________ | ______ | _________ |
+| Finance | __________ | ______ | _________ |
+
+---
+
+## 📅 Launch Timeline
+
+**T-7 days**: Final code freeze, begin final testing
+**T-3 days**: Complete all checklist items
+**T-1 day**: Run final validation, get approvals
+**T-0 (Launch Day)**: Deploy to production (10% rollout)
+**T+1 day**: Monitor closely, increase to 25% if stable
+**T+7 days**: 100% rollout if metrics good
+**T+30 days**: Measure success criteria, decide on Phase 2
+
+---
+
+**Document Version**: 1.0
+**Last Review**: November 2, 2025
+**Next Review**: Before production deployment
diff --git a/migrations/001_add_pgvector.sql b/migrations/001_add_pgvector.sql
new file mode 100644
index 0000000..36d669c
--- /dev/null
+++ b/migrations/001_add_pgvector.sql
@@ -0,0 +1,113 @@
+-- Migration: Add pgvector extension and vector embedding columns
+-- Phase 1: Semantic Search Foundation
+-- Date: 2025-11-01
+
+-- Enable pgvector extension
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- Add vector embedding columns to timeline_entries
+ALTER TABLE timeline_entries
+ADD COLUMN IF NOT EXISTS description_embedding vector(768),
+ADD COLUMN IF NOT EXISTS content_embedding vector(1536),
+ADD COLUMN IF NOT EXISTS embedding_model varchar(100),
+ADD COLUMN IF NOT EXISTS embedding_generated_at timestamp;
+
+-- Add vector embedding columns to timeline_sources
+ALTER TABLE timeline_sources
+ADD COLUMN IF NOT EXISTS excerpt_embedding vector(768),
+ADD COLUMN IF NOT EXISTS embedding_model varchar(100),
+ADD COLUMN IF NOT EXISTS embedding_generated_at timestamp;
+
+-- Create indexes for vector similarity search using IVFFlat
+-- IVFFlat is faster than brute force for large datasets
+-- lists = 100 is a good starting point for up to 1M vectors
+-- Adjust based on dataset size: lists ≈ sqrt(row_count)
+
+-- Index for description embeddings (Legal-BERT, 768 dimensions)
+CREATE INDEX IF NOT EXISTS timeline_entries_description_embedding_idx
+ON timeline_entries
+USING ivfflat (description_embedding vector_cosine_ops)
+WITH (lists = 100);
+
+-- Index for content embeddings (OpenAI, 1536 dimensions)
+CREATE INDEX IF NOT EXISTS timeline_entries_content_embedding_idx
+ON timeline_entries
+USING ivfflat (content_embedding vector_cosine_ops)
+WITH (lists = 100);
+
+-- Index for source excerpt embeddings
+CREATE INDEX IF NOT EXISTS timeline_sources_excerpt_embedding_idx
+ON timeline_sources
+USING ivfflat (excerpt_embedding vector_cosine_ops)
+WITH (lists = 100);
+
+-- Add index on embedding_generated_at for tracking coverage
+CREATE INDEX IF NOT EXISTS timeline_entries_embedding_generated_at_idx
+ON timeline_entries (embedding_generated_at)
+WHERE embedding_generated_at IS NOT NULL;
+
+-- Create a view for monitoring embedding coverage
+CREATE OR REPLACE VIEW embedding_coverage AS
+SELECT
+  'timeline_entries' as table_name,
+  COUNT(*) as total_records,
+  COUNT(description_embedding) as embedded_records,
+  ROUND(100.0 * COUNT(description_embedding) / NULLIF(COUNT(*), 0), 2) as coverage_percentage,
+  MAX(embedding_generated_at) as last_embedding_generated
+FROM timeline_entries
+WHERE deleted_at IS NULL
+UNION ALL
+SELECT
+  'timeline_sources' as table_name,
+  COUNT(*) as total_records,
+  COUNT(excerpt_embedding) as embedded_records,
+  ROUND(100.0 * COUNT(excerpt_embedding) / NULLIF(COUNT(*), 0), 2) as coverage_percentage,
+  MAX(embedding_generated_at) as last_embedding_generated
+FROM timeline_sources;
+
+-- Create function to get similar entries by vector similarity
+CREATE OR REPLACE FUNCTION find_similar_entries(
+  query_embedding vector(768),
+  case_filter uuid DEFAULT NULL,
+  similarity_threshold float DEFAULT 0.7,
+  max_results int DEFAULT 20
+)
+RETURNS TABLE (
+  entry_id uuid,
+  similarity float,
+  description text,
+  date date,
+  entry_type text
+) AS $$
+BEGIN
+  RETURN QUERY
+  SELECT
+    te.id,
+    1 - (te.description_embedding <=> query_embedding) as similarity,
+    te.description,
+    te.date,
+    te.entry_type::text
+  FROM timeline_entries te
+  WHERE te.deleted_at IS NULL
+    AND te.description_embedding IS NOT NULL
+    AND (case_filter IS NULL OR te.case_id = case_filter)
+    AND (1 - (te.description_embedding <=> query_embedding)) >= similarity_threshold
+  ORDER BY te.description_embedding <=> query_embedding
+  LIMIT max_results;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Comments for documentation
+COMMENT ON COLUMN timeline_entries.description_embedding IS 'Legal-BERT embedding (768-dim) for semantic search on description field';
+COMMENT ON COLUMN timeline_entries.content_embedding IS 'OpenAI embedding (1536-dim) for full content semantic search';
+COMMENT ON COLUMN timeline_entries.embedding_model IS 'Model used to generate embedding (e.g., legal-bert-base, text-embedding-3-small)';
+COMMENT ON COLUMN timeline_entries.embedding_generated_at IS 'Timestamp when embedding was generated, NULL if not yet embedded';
+
+COMMENT ON FUNCTION find_similar_entries IS 'Find semantically similar timeline entries using vector similarity search';
+COMMENT ON VIEW embedding_coverage IS 'Monitor percentage of records with embeddings generated';
+
+-- Migration complete
+-- Next steps:
+-- 1. Run embedding generation for existing records
+-- 2. Update application code to generate embeddings on insert/update
+-- 3. Deploy hybrid search API endpoints
diff --git a/package.json b/package.json
index e9555ed..45e4c9d 100644
--- a/package.json
+++ b/package.json
@@ -8,6 +8,10 @@
     "build": "vite build && esbuild server/index.ts --platform=node --packages=external --bundle --format=esm --outdir=dist",
     "start": "NODE_ENV=production node dist/index.js",
     "check": "tsc",
+    "test": "node --test tests/phase1-integration.test.ts",
+    "test:watch": "node --test --watch tests/phase1-integration.test.ts",
+    "validate:staging": "./scripts/validate-deployment.sh staging",
+    "validate:production": "./scripts/validate-deployment.sh production",
     "db:push": "drizzle-kit push",
     "registry:register": "node scripts/registry/register.js",
     "registry:local:scan": "node scripts/registry/local-scan.js",
@@ -26,7 +30,10 @@
     "cfd:validate": "bash deploy/validate-cfd.sh",
     "cfd:setup": "bash deploy/setup.sh production",
     "config:apply": "bash deploy/apply-config.sh production",
-    "evidence:organize": "tsx custom/importers/marie-kondo-evidence-importer.ts"
+    "evidence:organize": "tsx custom/importers/marie-kondo-evidence-importer.ts",
+    "embeddings:generate": "tsx scripts/generate-embeddings.ts",
+    "embeddings:coverage": "tsx scripts/generate-embeddings.ts --coverage",
+    "embeddings:case": "tsx scripts/generate-embeddings.ts --case-id"
   },
   "dependencies": {
     "@anthropic-ai/sdk": "^0.37.0",
diff --git a/scripts/backup-to-gdrive.sh b/scripts/backup-to-gdrive.sh
new file mode 100755
index 0000000..78c04ef
--- /dev/null
+++ b/scripts/backup-to-gdrive.sh
@@ -0,0 +1,168 @@
+#!/bin/bash
+#
+# ChittyChronicle Automated Backup Script
+# Syncs git repository to Google Drive using rclone
+#
+# Usage:
+#   ./backup-to-gdrive.sh          # Full sync
+#   ./backup-to-gdrive.sh --dry-run # Test without actually syncing
+#
+
+set -e  # Exit on error
+
+# Configuration
+REPO_PATH="/home/user/chittychronicle"
+BACKUP_DEST="gdrive:backups/chittychronicle"
+BUNDLE_DEST="gdrive:backups/bundles"
+LOG_FILE="/home/user/chittychronicle-backup.log"
+DATE=$(date '+%Y-%m-%d %H:%M:%S')
+DATE_SHORT=$(date +%Y%m%d)
+
+# Colors for output
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+RED='\033[0;31m'
+NC='\033[0m' # No Color
+
+# Check if rclone is installed
+if ! command -v rclone &> /dev/null; then
+    echo -e "${RED}❌ Error: rclone is not installed${NC}"
+    echo "Install with: curl https://rclone.org/install.sh | sudo bash"
+    exit 1
+fi
+
+# Check if gdrive remote is configured
+if ! rclone listremotes | grep -q "gdrive:"; then
+    echo -e "${RED}❌ Error: 'gdrive' remote not configured${NC}"
+    echo "Configure with: rclone config"
+    echo "Name it 'gdrive' when prompted"
+    exit 1
+fi
+
+# Check if repo exists
+if [ ! -d "$REPO_PATH" ]; then
+    echo -e "${RED}❌ Error: Repo not found at $REPO_PATH${NC}"
+    exit 1
+fi
+
+# Parse arguments
+DRY_RUN=""
+if [ "$1" == "--dry-run" ]; then
+    DRY_RUN="--dry-run"
+    echo -e "${YELLOW}🔍 DRY RUN MODE - No files will be modified${NC}"
+fi
+
+echo "================================================================"
+echo "  ChittyChronicle Backup to Google Drive"
+echo "================================================================"
+echo "Started: $DATE"
+echo "Repo: $REPO_PATH"
+echo "Destination: $BACKUP_DEST"
+echo "================================================================"
+echo ""
+
+# Log start
+echo "[$DATE] Backup started" >> "$LOG_FILE"
+
+# Step 1: Sync full repository with rclone
+echo -e "${YELLOW}📦 Step 1: Syncing repository files...${NC}"
+
+rclone sync "$REPO_PATH/" "$BACKUP_DEST/" \
+    --exclude='node_modules/**' \
+    --exclude='dist/**' \
+    --exclude='.next/**' \
+    --exclude='*.log' \
+    --exclude='.env' \
+    --exclude='.env.local' \
+    --progress \
+    --log-file="$LOG_FILE" \
+    --log-level=INFO \
+    $DRY_RUN
+
+if [ $? -eq 0 ]; then
+    echo -e "${GREEN}✅ Repository sync completed${NC}"
+else
+    echo -e "${RED}❌ Repository sync failed${NC}"
+    exit 1
+fi
+
+echo ""
+
+# Step 2: Create and upload git bundle (single-file backup)
+echo -e "${YELLOW}📚 Step 2: Creating git bundle...${NC}"
+
+cd "$REPO_PATH"
+
+# Check if there are uncommitted changes
+if ! git diff-index --quiet HEAD -- 2>/dev/null; then
+    echo -e "${YELLOW}⚠️  Warning: Uncommitted changes detected${NC}"
+    echo "   Bundle will only include committed changes"
+fi
+
+BUNDLE_NAME="chittychronicle-$DATE_SHORT.bundle"
+BUNDLE_PATH="/tmp/$BUNDLE_NAME"
+
+git bundle create "$BUNDLE_PATH" --all
+
+if [ $? -eq 0 ]; then
+    echo -e "${GREEN}✅ Git bundle created: $BUNDLE_NAME${NC}"
+
+    # Upload bundle
+    if [ -z "$DRY_RUN" ]; then
+        rclone copy "$BUNDLE_PATH" "$BUNDLE_DEST/" --progress
+
+        if [ $? -eq 0 ]; then
+            echo -e "${GREEN}✅ Bundle uploaded to Google Drive${NC}"
+            rm "$BUNDLE_PATH"
+        else
+            echo -e "${RED}❌ Bundle upload failed${NC}"
+            rm "$BUNDLE_PATH"
+            exit 1
+        fi
+    else
+        echo "   [DRY RUN] Would upload: $BUNDLE_NAME"
+        rm "$BUNDLE_PATH"
+    fi
+else
+    echo -e "${RED}❌ Git bundle creation failed${NC}"
+    exit 1
+fi
+
+echo ""
+
+# Step 3: Show backup info
+echo -e "${YELLOW}📊 Step 3: Backup summary${NC}"
+
+# Get current git info
+CURRENT_BRANCH=$(git rev-parse --abbrev-ref HEAD)
+CURRENT_COMMIT=$(git rev-parse --short HEAD)
+COMMIT_COUNT=$(git rev-list --count HEAD)
+
+echo "   Current branch: $CURRENT_BRANCH"
+echo "   Latest commit: $CURRENT_COMMIT"
+echo "   Total commits: $COMMIT_COUNT"
+echo ""
+
+# List recent backups
+echo "   Recent bundles in Google Drive:"
+rclone ls "$BUNDLE_DEST/" 2>/dev/null | tail -5 || echo "   (Unable to list)"
+
+echo ""
+echo "================================================================"
+echo -e "${GREEN}✅ BACKUP COMPLETED SUCCESSFULLY${NC}"
+echo "================================================================"
+echo "Finished: $(date '+%Y-%m-%d %H:%M:%S')"
+echo ""
+echo "Backup locations:"
+echo "  • Live sync: $BACKUP_DEST/"
+echo "  • Bundle: $BUNDLE_DEST/$BUNDLE_NAME"
+echo ""
+echo "To restore from bundle:"
+echo "  rclone copy $BUNDLE_DEST/$BUNDLE_NAME ./"
+echo "  git clone $BUNDLE_NAME chittychronicle-restored"
+echo "================================================================"
+
+# Log completion
+echo "[$DATE] Backup completed successfully" >> "$LOG_FILE"
+
+exit 0
diff --git a/scripts/generate-embeddings.ts b/scripts/generate-embeddings.ts
new file mode 100644
index 0000000..e4c6300
--- /dev/null
+++ b/scripts/generate-embeddings.ts
@@ -0,0 +1,151 @@
+#!/usr/bin/env tsx
+/**
+ * Batch Embedding Generation CLI Tool
+ * Phase 1: SOTA Upgrade
+ *
+ * Usage:
+ *   npm run embeddings:generate              # Generate for all cases
+ *   npm run embeddings:generate <caseId>     # Generate for specific case
+ *   npm run embeddings:coverage              # Check embedding coverage
+ *
+ * Example:
+ *   tsx scripts/generate-embeddings.ts
+ *   tsx scripts/generate-embeddings.ts --case-id=abc-123
+ *   tsx scripts/generate-embeddings.ts --coverage
+ */
+
+import { embeddingService } from "../server/embeddingService";
+
+// Parse command line arguments
+const args = process.argv.slice(2);
+const caseIdArg = args.find(arg => arg.startsWith('--case-id='));
+const coverageFlag = args.includes('--coverage');
+const helpFlag = args.includes('--help') || args.includes('-h');
+
+// Display help
+if (helpFlag) {
+  console.log(`
+📊 ChittyChronicle Embedding Generation Tool
+===========================================
+
+Usage:
+  tsx scripts/generate-embeddings.ts [options]
+
+Options:
+  --case-id=<uuid>    Generate embeddings for specific case only
+  --coverage          Show embedding coverage statistics
+  --help, -h          Show this help message
+
+Examples:
+  # Generate embeddings for all timeline entries
+  tsx scripts/generate-embeddings.ts
+
+  # Generate embeddings for a specific case
+  tsx scripts/generate-embeddings.ts --case-id=550e8400-e29b-41d4-a716-446655440000
+
+  # Check current embedding coverage
+  tsx scripts/generate-embeddings.ts --coverage
+
+Environment Variables:
+  OPENAI_API_KEY               Required for embedding generation
+  EMBEDDING_MODEL              Model to use (default: text-embedding-3-small)
+  EMBEDDING_DIMENSIONS         Embedding dimensions (default: 1536)
+
+Cost Estimation:
+  OpenAI text-embedding-3-small: $0.02 per 1M tokens
+  Average legal document: ~500 tokens
+  1000 documents ≈ 500K tokens ≈ $0.01
+`);
+  process.exit(0);
+}
+
+async function main() {
+  console.log("🚀 ChittyChronicle Embedding Generation Tool\n");
+
+  // Check for API key
+  if (!process.env.OPENAI_API_KEY) {
+    console.error("❌ Error: OPENAI_API_KEY environment variable is required");
+    console.error("   Please set it in your .env file or environment");
+    process.exit(1);
+  }
+
+  try {
+    // Show coverage if requested
+    if (coverageFlag) {
+      await showCoverage();
+      return;
+    }
+
+    // Extract case ID if provided
+    const caseId = caseIdArg ? caseIdArg.split('=')[1] : undefined;
+
+    if (caseId) {
+      console.log(`📁 Generating embeddings for case: ${caseId}\n`);
+    } else {
+      console.log("📁 Generating embeddings for ALL cases\n");
+    }
+
+    // Get initial coverage
+    console.log("📊 Initial Coverage:");
+    await showCoverage();
+    console.log();
+
+    // Confirm before proceeding
+    if (!caseId) {
+      console.log("⚠️  This will generate embeddings for ALL timeline entries without embeddings");
+      console.log("   This may take time and incur API costs");
+      console.log();
+
+      // In production, you might want to add a confirmation prompt here
+      // For now, we'll proceed automatically
+    }
+
+    // Generate embeddings
+    console.log("🔄 Starting embedding generation...\n");
+    const startTime = Date.now();
+
+    const stats = await embeddingService.embedAllMissingEntries(caseId, 100);
+
+    const duration = ((Date.now() - startTime) / 1000).toFixed(2);
+
+    console.log("\n✅ Embedding generation complete!");
+    console.log(`   Processed: ${stats.processed} entries`);
+    console.log(`   Errors: ${stats.errors} entries`);
+    console.log(`   Total tokens: ${stats.totalTokens.toLocaleString()}`);
+    console.log(`   Duration: ${duration}s`);
+
+    // Estimate cost
+    const costPer1MTokens = 0.02; // OpenAI pricing
+    const estimatedCost = (stats.totalTokens / 1000000) * costPer1MTokens;
+    console.log(`   Estimated cost: $${estimatedCost.toFixed(4)}`);
+
+    // Show final coverage
+    console.log("\n📊 Final Coverage:");
+    await showCoverage();
+
+  } catch (error) {
+    console.error("\n❌ Error:", error.message);
+    console.error(error);
+    process.exit(1);
+  }
+}
+
+async function showCoverage() {
+  const coverage = await embeddingService.getEmbeddingCoverage();
+
+  console.log("   Timeline Entries:");
+  console.log(`     Total: ${coverage.timelineEntries.total}`);
+  console.log(`     Embedded: ${coverage.timelineEntries.embedded}`);
+  console.log(`     Coverage: ${coverage.timelineEntries.percentage.toFixed(1)}%`);
+
+  console.log("\n   Timeline Sources:");
+  console.log(`     Total: ${coverage.timelineSources.total}`);
+  console.log(`     Embedded: ${coverage.timelineSources.embedded}`);
+  console.log(`     Coverage: ${coverage.timelineSources.percentage.toFixed(1)}%`);
+}
+
+// Run the script
+main().catch(error => {
+  console.error("Fatal error:", error);
+  process.exit(1);
+});
diff --git a/scripts/setup-backup.sh b/scripts/setup-backup.sh
new file mode 100755
index 0000000..ad4abf9
--- /dev/null
+++ b/scripts/setup-backup.sh
@@ -0,0 +1,123 @@
+#!/bin/bash
+#
+# Quick Setup Script for Google Drive Backups
+# Installs rclone and guides through configuration
+#
+
+set -e
+
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+echo "================================================================"
+echo "  ChittyChronicle Google Drive Backup Setup"
+echo "================================================================"
+echo ""
+
+# Step 1: Install rclone
+echo -e "${YELLOW}Step 1: Installing rclone...${NC}"
+if command -v rclone &> /dev/null; then
+    echo -e "${GREEN}✅ rclone is already installed${NC}"
+    rclone version | head -1
+else
+    echo "Installing rclone..."
+    curl -s https://rclone.org/install.sh | sudo bash
+
+    if command -v rclone &> /dev/null; then
+        echo -e "${GREEN}✅ rclone installed successfully${NC}"
+    else
+        echo -e "${RED}❌ Failed to install rclone${NC}"
+        exit 1
+    fi
+fi
+
+echo ""
+
+# Step 2: Configure Google Drive
+echo -e "${YELLOW}Step 2: Configuring Google Drive...${NC}"
+
+if rclone listremotes | grep -q "gdrive:"; then
+    echo -e "${GREEN}✅ 'gdrive' remote already configured${NC}"
+    echo ""
+    read -p "Reconfigure? (y/N): " RECONFIG
+    if [ "$RECONFIG" != "y" ] && [ "$RECONFIG" != "Y" ]; then
+        echo "Skipping configuration..."
+    else
+        echo "Deleting existing configuration..."
+        rclone config delete gdrive
+        rclone config
+    fi
+else
+    echo ""
+    echo -e "${BLUE}📝 Configure Google Drive Remote${NC}"
+    echo "Follow these steps:"
+    echo "  1. Choose: n (New remote)"
+    echo "  2. Name: gdrive"
+    echo "  3. Storage: drive (or the number for Google Drive)"
+    echo "  4. Press Enter for all other options (use defaults)"
+    echo "  5. Authenticate in browser when it opens"
+    echo ""
+    read -p "Press Enter to start configuration..."
+
+    rclone config
+fi
+
+echo ""
+
+# Step 3: Test connection
+echo -e "${YELLOW}Step 3: Testing connection...${NC}"
+
+if rclone lsd gdrive: &> /dev/null; then
+    echo -e "${GREEN}✅ Successfully connected to Google Drive${NC}"
+    echo ""
+    echo "Your Google Drive folders:"
+    rclone lsd gdrive: | head -5
+else
+    echo -e "${RED}❌ Failed to connect to Google Drive${NC}"
+    echo "Please run: rclone config"
+    exit 1
+fi
+
+echo ""
+
+# Step 4: Create backup directories
+echo -e "${YELLOW}Step 4: Creating backup directories...${NC}"
+
+rclone mkdir gdrive:backups/chittychronicle 2>/dev/null || true
+rclone mkdir gdrive:backups/bundles 2>/dev/null || true
+
+echo -e "${GREEN}✅ Backup directories created${NC}"
+
+echo ""
+
+# Step 5: Run test backup
+echo -e "${YELLOW}Step 5: Running test backup (dry-run)...${NC}"
+echo ""
+
+cd /home/user/chittychronicle
+./scripts/backup-to-gdrive.sh --dry-run
+
+echo ""
+echo "================================================================"
+echo -e "${GREEN}✅ SETUP COMPLETE!${NC}"
+echo "================================================================"
+echo ""
+echo "Next steps:"
+echo ""
+echo "  1. Run your first backup:"
+echo "     cd /home/user/chittychronicle"
+echo "     ./scripts/backup-to-gdrive.sh"
+echo ""
+echo "  2. Check backups in Google Drive:"
+echo "     rclone ls gdrive:backups/"
+echo ""
+echo "  3. Set up automated backups (optional):"
+echo "     crontab -e"
+echo "     # Add: 0 2 * * * /home/user/chittychronicle/scripts/backup-to-gdrive.sh"
+echo ""
+echo "  4. Read full documentation:"
+echo "     cat docs/BACKUP_SETUP_GUIDE.md"
+echo ""
+echo "================================================================"
diff --git a/scripts/validate-deployment.sh b/scripts/validate-deployment.sh
new file mode 100755
index 0000000..c235620
--- /dev/null
+++ b/scripts/validate-deployment.sh
@@ -0,0 +1,325 @@
+#!/bin/bash
+#
+# Phase 1 Deployment Validation Script
+# Validates that Phase 1 is ready for production deployment
+#
+# Usage:
+#   ./scripts/validate-deployment.sh staging
+#   ./scripts/validate-deployment.sh production
+#
+
+set -e
+
+ENV=${1:-staging}
+ERRORS=0
+WARNINGS=0
+
+# Colors
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+echo "═══════════════════════════════════════════════════════════"
+echo "  Phase 1 Deployment Validation"
+echo "  Environment: $ENV"
+echo "═══════════════════════════════════════════════════════════"
+echo ""
+
+# Load environment variables
+if [ -f ".env.$ENV" ]; then
+    source ".env.$ENV"
+elif [ -f ".env" ]; then
+    source ".env"
+else
+    echo -e "${YELLOW}⚠️  Warning: No .env file found${NC}"
+    WARNINGS=$((WARNINGS + 1))
+fi
+
+# Helper functions
+check_required() {
+    local var_name=$1
+    local var_value=${!var_name}
+
+    if [ -z "$var_value" ]; then
+        echo -e "${RED}❌ FAIL: $var_name is not set${NC}"
+        ERRORS=$((ERRORS + 1))
+        return 1
+    else
+        echo -e "${GREEN}✅ PASS: $var_name is set${NC}"
+        return 0
+    fi
+}
+
+check_optional() {
+    local var_name=$1
+    local var_value=${!var_name}
+
+    if [ -z "$var_value" ]; then
+        echo -e "${YELLOW}⚠️  WARN: $var_name is not set (optional)${NC}"
+        WARNINGS=$((WARNINGS + 1))
+        return 1
+    else
+        echo -e "${GREEN}✅ PASS: $var_name is set${NC}"
+        return 0
+    fi
+}
+
+test_endpoint() {
+    local url=$1
+    local expected_status=${2:-200}
+
+    if command -v curl &> /dev/null; then
+        local status=$(curl -s -o /dev/null -w "%{http_code}" "$url")
+        if [ "$status" == "$expected_status" ]; then
+            echo -e "${GREEN}✅ PASS: $url ($status)${NC}"
+            return 0
+        else
+            echo -e "${RED}❌ FAIL: $url (got $status, expected $expected_status)${NC}"
+            ERRORS=$((ERRORS + 1))
+            return 1
+        fi
+    else
+        echo -e "${YELLOW}⚠️  WARN: curl not installed, skipping endpoint test${NC}"
+        WARNINGS=$((WARNINGS + 1))
+        return 1
+    fi
+}
+
+# Section 1: Environment Variables
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "1. Environment Variables"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+check_required "DATABASE_URL"
+check_required "OPENAI_API_KEY"
+check_required "ANTHROPIC_API_KEY"
+check_optional "ENABLE_HYBRID_SEARCH"
+check_optional "ENABLE_RAG"
+check_optional "EMBEDDING_MODEL"
+check_optional "EMBEDDING_DIMENSIONS"
+
+# Section 2: Database Checks
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "2. Database Checks"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+if command -v psql &> /dev/null && [ -n "$DATABASE_URL" ]; then
+    # Check pgvector extension
+    echo "Checking pgvector extension..."
+    if psql "$DATABASE_URL" -t -c "SELECT 1 FROM pg_extension WHERE extname = 'vector';" | grep -q 1; then
+        echo -e "${GREEN}✅ PASS: pgvector extension installed${NC}"
+    else
+        echo -e "${RED}❌ FAIL: pgvector extension not installed${NC}"
+        echo "   Run: psql -d \$DATABASE_URL -c 'CREATE EXTENSION vector;'"
+        ERRORS=$((ERRORS + 1))
+    fi
+
+    # Check for embedding columns
+    echo "Checking vector columns..."
+    if psql "$DATABASE_URL" -t -c "\d timeline_entries" | grep -q "content_embedding"; then
+        echo -e "${GREEN}✅ PASS: Vector columns exist${NC}"
+    else
+        echo -e "${RED}❌ FAIL: Vector columns missing${NC}"
+        echo "   Run migration: psql -d \$DATABASE_URL -f migrations/001_add_pgvector.sql"
+        ERRORS=$((ERRORS + 1))
+    fi
+
+    # Check embedding coverage
+    echo "Checking embedding coverage..."
+    coverage=$(psql "$DATABASE_URL" -t -c "SELECT coverage_percentage FROM embedding_coverage WHERE table_name = 'timeline_entries';" | tr -d ' ')
+    if [ -n "$coverage" ]; then
+        echo -e "${BLUE}ℹ️  INFO: Embedding coverage: ${coverage}%${NC}"
+        if (( $(echo "$coverage < 50" | bc -l) )); then
+            echo -e "${YELLOW}⚠️  WARN: Low embedding coverage (<50%)${NC}"
+            echo "   Run: npm run embeddings:generate"
+            WARNINGS=$((WARNINGS + 1))
+        fi
+    else
+        echo -e "${YELLOW}⚠️  WARN: Could not check embedding coverage${NC}"
+        WARNINGS=$((WARNINGS + 1))
+    fi
+else
+    echo -e "${YELLOW}⚠️  WARN: psql not installed or DATABASE_URL not set, skipping DB checks${NC}"
+    WARNINGS=$((WARNINGS + 1))
+fi
+
+# Section 3: Dependencies
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "3. Dependencies"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+if [ -f "package.json" ]; then
+    # Check if node_modules exists
+    if [ -d "node_modules" ]; then
+        echo -e "${GREEN}✅ PASS: node_modules directory exists${NC}"
+    else
+        echo -e "${RED}❌ FAIL: node_modules not found${NC}"
+        echo "   Run: npm install"
+        ERRORS=$((ERRORS + 1))
+    fi
+
+    # Check for required packages
+    if grep -q '"openai"' package.json; then
+        echo -e "${GREEN}✅ PASS: openai package in package.json${NC}"
+    else
+        echo -e "${RED}❌ FAIL: openai package missing${NC}"
+        ERRORS=$((ERRORS + 1))
+    fi
+
+    if grep -q '"@anthropic-ai/sdk"' package.json; then
+        echo -e "${GREEN}✅ PASS: @anthropic-ai/sdk package in package.json${NC}"
+    else
+        echo -e "${RED}❌ FAIL: @anthropic-ai/sdk package missing${NC}"
+        ERRORS=$((ERRORS + 1))
+    fi
+fi
+
+# Section 4: File Structure
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "4. File Structure"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+check_file() {
+    if [ -f "$1" ]; then
+        echo -e "${GREEN}✅ PASS: $1 exists${NC}"
+        return 0
+    else
+        echo -e "${RED}❌ FAIL: $1 missing${NC}"
+        ERRORS=$((ERRORS + 1))
+        return 1
+    fi
+}
+
+check_file "migrations/001_add_pgvector.sql"
+check_file "server/embeddingService.ts"
+check_file "server/hybridSearchService.ts"
+check_file "server/ragService.ts"
+check_file "server/sotaRoutes.ts"
+check_file "scripts/generate-embeddings.ts"
+check_file "docs/PHASE1_DEPLOYMENT_GUIDE.md"
+
+# Section 5: Build Check
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "5. Build Check"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+if command -v npm &> /dev/null; then
+    echo "Running TypeScript type check..."
+    if npm run check &> /dev/null; then
+        echo -e "${GREEN}✅ PASS: TypeScript compiles without errors${NC}"
+    else
+        echo -e "${RED}❌ FAIL: TypeScript compilation errors${NC}"
+        echo "   Run: npm run check"
+        ERRORS=$((ERRORS + 1))
+    fi
+else
+    echo -e "${YELLOW}⚠️  WARN: npm not installed, skipping build check${NC}"
+    WARNINGS=$((WARNINGS + 1))
+fi
+
+# Section 6: API Endpoints (if server is running)
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "6. API Endpoints (if server running)"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+BASE_URL=${BASE_URL:-http://localhost:5000}
+
+echo "Testing endpoints at: $BASE_URL"
+echo "(Server must be running for these tests)"
+echo ""
+
+# Test if server is running
+if test_endpoint "$BASE_URL" 200; then
+    # Test SOTA endpoints
+    echo "Testing SOTA endpoints..."
+
+    # These will return 400 without proper params, which is expected
+    test_endpoint "$BASE_URL/api/admin/embeddings/coverage" 200
+
+    echo ""
+    echo -e "${BLUE}ℹ️  INFO: For full endpoint testing, run integration tests:${NC}"
+    echo "   TEST_CASE_ID=<uuid> npm test"
+else
+    echo -e "${YELLOW}⚠️  WARN: Server not running, skipping endpoint tests${NC}"
+    echo "   Start server: npm run dev"
+    WARNINGS=$((WARNINGS + 1))
+fi
+
+# Section 7: API Key Validation
+echo ""
+echo "─────────────────────────────────────────────────────────"
+echo "7. API Key Validation"
+echo "─────────────────────────────────────────────────────────"
+echo ""
+
+if [ -n "$OPENAI_API_KEY" ]; then
+    echo "Testing OpenAI API key..."
+    if curl -s -H "Authorization: Bearer $OPENAI_API_KEY" \
+        https://api.openai.com/v1/models | grep -q "gpt"; then
+        echo -e "${GREEN}✅ PASS: OpenAI API key is valid${NC}"
+    else
+        echo -e "${RED}❌ FAIL: OpenAI API key is invalid${NC}"
+        ERRORS=$((ERRORS + 1))
+    fi
+fi
+
+if [ -n "$ANTHROPIC_API_KEY" ]; then
+    echo "Testing Anthropic API key..."
+    if curl -s -H "x-api-key: $ANTHROPIC_API_KEY" \
+        -H "anthropic-version: 2023-06-01" \
+        -H "content-type: application/json" \
+        -d '{"model":"claude-sonnet-4-20250514","max_tokens":10,"messages":[{"role":"user","content":"Hi"}]}' \
+        https://api.anthropic.com/v1/messages | grep -q "content"; then
+        echo -e "${GREEN}✅ PASS: Anthropic API key is valid${NC}"
+    else
+        echo -e "${RED}❌ FAIL: Anthropic API key is invalid${NC}"
+        ERRORS=$((ERRORS + 1))
+    fi
+fi
+
+# Summary
+echo ""
+echo "═══════════════════════════════════════════════════════════"
+echo "  Validation Summary"
+echo "═══════════════════════════════════════════════════════════"
+echo ""
+
+if [ $ERRORS -eq 0 ] && [ $WARNINGS -eq 0 ]; then
+    echo -e "${GREEN}✅ ALL CHECKS PASSED!${NC}"
+    echo ""
+    echo "Phase 1 is ready for $ENV deployment!"
+    echo ""
+    exit 0
+elif [ $ERRORS -eq 0 ]; then
+    echo -e "${YELLOW}⚠️  PASSED WITH WARNINGS${NC}"
+    echo ""
+    echo "Errors: $ERRORS"
+    echo "Warnings: $WARNINGS"
+    echo ""
+    echo "Phase 1 can be deployed to $ENV, but review warnings above."
+    echo ""
+    exit 0
+else
+    echo -e "${RED}❌ VALIDATION FAILED${NC}"
+    echo ""
+    echo "Errors: $ERRORS"
+    echo "Warnings: $WARNINGS"
+    echo ""
+    echo "Fix errors above before deploying to $ENV."
+    echo ""
+    exit 1
+fi
diff --git a/server/embeddingService.ts b/server/embeddingService.ts
new file mode 100644
index 0000000..a4c6e2d
--- /dev/null
+++ b/server/embeddingService.ts
@@ -0,0 +1,400 @@
+/**
+ * Embedding Service for Semantic Search
+ * Phase 1: SOTA Upgrade - Semantic Search Foundation
+ *
+ * Generates vector embeddings for legal documents using:
+ * - OpenAI text-embedding-3-small (1536 dimensions, general-purpose)
+ * - Future: Legal-BERT (768 dimensions, legal-specific)
+ */
+
+import OpenAI from "openai";
+import { db } from "./db";
+import { timelineEntries, timelineSources } from "@shared/schema";
+import { eq, isNull, sql } from "drizzle-orm";
+
+// Initialize OpenAI client
+const openai = new OpenAI({
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
+// Configuration
+const EMBEDDING_CONFIG = {
+  model: process.env.EMBEDDING_MODEL || "text-embedding-3-small",
+  dimensions: parseInt(process.env.EMBEDDING_DIMENSIONS || "1536"),
+  batchSize: 100, // Process this many texts at once
+  maxTokens: 8000, // OpenAI limit per request
+  enableLegalBert: process.env.ENABLE_LEGAL_BERT === "true",
+};
+
+export interface EmbeddingResult {
+  embedding: number[];
+  model: string;
+  dimensions: number;
+  tokensUsed: number;
+}
+
+export interface BatchEmbeddingResult {
+  embeddings: number[][];
+  model: string;
+  totalTokens: number;
+  processedCount: number;
+}
+
+/**
+ * Generate embedding for a single text using OpenAI
+ */
+export async function generateEmbedding(
+  text: string,
+  model: string = EMBEDDING_CONFIG.model
+): Promise<EmbeddingResult> {
+
+  if (!text || text.trim().length === 0) {
+    throw new Error("Text cannot be empty for embedding generation");
+  }
+
+  // Truncate if too long (OpenAI has token limits)
+  const truncatedText = text.substring(0, 32000); // Approx 8000 tokens
+
+  try {
+    const response = await openai.embeddings.create({
+      model,
+      input: truncatedText,
+      encoding_format: "float",
+    });
+
+    return {
+      embedding: response.data[0].embedding,
+      model: response.model,
+      dimensions: response.data[0].embedding.length,
+      tokensUsed: response.usage.total_tokens,
+    };
+  } catch (error) {
+    console.error("Error generating embedding:", error);
+    throw new Error(`Failed to generate embedding: ${error.message}`);
+  }
+}
+
+/**
+ * Generate embeddings for multiple texts in batch
+ * More efficient for processing many documents
+ */
+export async function generateBatchEmbeddings(
+  texts: string[],
+  model: string = EMBEDDING_CONFIG.model
+): Promise<BatchEmbeddingResult> {
+
+  if (texts.length === 0) {
+    return {
+      embeddings: [],
+      model,
+      totalTokens: 0,
+      processedCount: 0,
+    };
+  }
+
+  // Filter out empty texts
+  const validTexts = texts
+    .map(t => t?.trim() || "")
+    .filter(t => t.length > 0)
+    .map(t => t.substring(0, 32000)); // Truncate
+
+  if (validTexts.length === 0) {
+    throw new Error("No valid texts to embed");
+  }
+
+  try {
+    const response = await openai.embeddings.create({
+      model,
+      input: validTexts,
+      encoding_format: "float",
+    });
+
+    return {
+      embeddings: response.data.map(d => d.embedding),
+      model: response.model,
+      totalTokens: response.usage.total_tokens,
+      processedCount: validTexts.length,
+    };
+  } catch (error) {
+    console.error("Error generating batch embeddings:", error);
+    throw new Error(`Failed to generate batch embeddings: ${error.message}`);
+  }
+}
+
+/**
+ * Generate embedding for a timeline entry's description
+ */
+export async function embedTimelineEntry(entryId: string): Promise<void> {
+  // Fetch the entry
+  const entries = await db
+    .select()
+    .from(timelineEntries)
+    .where(eq(timelineEntries.id, entryId))
+    .limit(1);
+
+  if (entries.length === 0) {
+    throw new Error(`Timeline entry ${entryId} not found`);
+  }
+
+  const entry = entries[0];
+
+  // Prepare text for embedding
+  // Combine description and detailed notes for richer semantic representation
+  const textToEmbed = [
+    entry.description,
+    entry.detailedNotes,
+    // Include tags for additional context
+    entry.tags?.join(", "),
+  ]
+    .filter(Boolean)
+    .join("\n\n");
+
+  if (!textToEmbed.trim()) {
+    console.warn(`Entry ${entryId} has no text to embed`);
+    return;
+  }
+
+  // Generate embedding
+  const result = await generateEmbedding(textToEmbed);
+
+  // Convert embedding array to PostgreSQL vector format
+  const vectorString = `[${result.embedding.join(",")}]`;
+
+  // Update the entry with embedding
+  await db
+    .update(timelineEntries)
+    .set({
+      contentEmbedding: vectorString,
+      embeddingModel: result.model,
+      embeddingGeneratedAt: new Date(),
+    })
+    .where(eq(timelineEntries.id, entryId));
+
+  console.log(
+    `Generated embedding for entry ${entryId} (${result.dimensions}D, ${result.tokensUsed} tokens)`
+  );
+}
+
+/**
+ * Generate embeddings for all timeline entries that don't have them yet
+ * Processes in batches for efficiency
+ */
+export async function embedAllMissingEntries(
+  caseId?: string,
+  batchSize: number = EMBEDDING_CONFIG.batchSize
+): Promise<{
+  processed: number;
+  totalTokens: number;
+  errors: number;
+}> {
+  let stats = {
+    processed: 0,
+    totalTokens: 0,
+    errors: 0,
+  };
+
+  console.log("Finding timeline entries without embeddings...");
+
+  // Find entries without embeddings
+  let whereConditions = [
+    isNull(timelineEntries.contentEmbedding),
+    isNull(timelineEntries.deletedAt),
+  ];
+
+  if (caseId) {
+    whereConditions.push(eq(timelineEntries.caseId, caseId));
+  }
+
+  const entriesToEmbed = await db
+    .select({
+      id: timelineEntries.id,
+      description: timelineEntries.description,
+      detailedNotes: timelineEntries.detailedNotes,
+      tags: timelineEntries.tags,
+    })
+    .from(timelineEntries)
+    .where(sql`${sql.join(whereConditions, sql` AND `)}`);
+
+  console.log(`Found ${entriesToEmbed.length} entries to embed`);
+
+  if (entriesToEmbed.length === 0) {
+    return stats;
+  }
+
+  // Process in batches
+  for (let i = 0; i < entriesToEmbed.length; i += batchSize) {
+    const batch = entriesToEmbed.slice(i, i + batchSize);
+
+    console.log(
+      `Processing batch ${Math.floor(i / batchSize) + 1}/${Math.ceil(entriesToEmbed.length / batchSize)}...`
+    );
+
+    try {
+      // Prepare texts
+      const texts = batch.map(entry =>
+        [entry.description, entry.detailedNotes, entry.tags?.join(", ")]
+          .filter(Boolean)
+          .join("\n\n")
+      );
+
+      // Generate embeddings
+      const result = await generateBatchEmbeddings(texts);
+      stats.totalTokens += result.totalTokens;
+
+      // Update entries
+      for (let j = 0; j < batch.length; j++) {
+        const entry = batch[j];
+        const embedding = result.embeddings[j];
+        const vectorString = `[${embedding.join(",")}]`;
+
+        try {
+          await db
+            .update(timelineEntries)
+            .set({
+              contentEmbedding: vectorString,
+              embeddingModel: result.model,
+              embeddingGeneratedAt: new Date(),
+            })
+            .where(eq(timelineEntries.id, entry.id));
+
+          stats.processed++;
+        } catch (updateError) {
+          console.error(`Error updating entry ${entry.id}:`, updateError);
+          stats.errors++;
+        }
+      }
+
+      console.log(
+        `Batch complete: ${batch.length} entries, ${result.totalTokens} tokens`
+      );
+
+      // Rate limiting: wait 1 second between batches to avoid hitting API limits
+      if (i + batchSize < entriesToEmbed.length) {
+        await new Promise(resolve => setTimeout(resolve, 1000));
+      }
+
+    } catch (batchError) {
+      console.error(`Error processing batch starting at index ${i}:`, batchError);
+      stats.errors += batch.length;
+    }
+  }
+
+  console.log(
+    `Embedding generation complete: ${stats.processed} processed, ${stats.errors} errors, ${stats.totalTokens} total tokens`
+  );
+
+  return stats;
+}
+
+/**
+ * Generate embedding for a timeline source excerpt
+ */
+export async function embedTimelineSource(sourceId: string): Promise<void> {
+  const sources = await db
+    .select()
+    .from(timelineSources)
+    .where(eq(timelineSources.id, sourceId))
+    .limit(1);
+
+  if (sources.length === 0) {
+    throw new Error(`Timeline source ${sourceId} not found`);
+  }
+
+  const source = sources[0];
+
+  // Use excerpt for embedding
+  if (!source.excerpt || source.excerpt.trim().length === 0) {
+    console.warn(`Source ${sourceId} has no excerpt to embed`);
+    return;
+  }
+
+  const result = await generateEmbedding(source.excerpt);
+  const vectorString = `[${result.embedding.join(",")}]`;
+
+  await db
+    .update(timelineSources)
+    .set({
+      excerptEmbedding: vectorString,
+      embeddingModel: result.model,
+      embeddingGeneratedAt: new Date(),
+    })
+    .where(eq(timelineSources.id, sourceId));
+
+  console.log(
+    `Generated embedding for source ${sourceId} (${result.dimensions}D, ${result.tokensUsed} tokens)`
+  );
+}
+
+/**
+ * Get embedding coverage statistics
+ */
+export async function getEmbeddingCoverage(): Promise<{
+  timelineEntries: {
+    total: number;
+    embedded: number;
+    percentage: number;
+  };
+  timelineSources: {
+    total: number;
+    embedded: number;
+    percentage: number;
+  };
+}> {
+  // Query embedding coverage view (created in migration)
+  const coverageData = await db.execute(sql`
+    SELECT * FROM embedding_coverage
+  `);
+
+  const entriesCoverage = coverageData.rows.find(
+    (row: any) => row.table_name === "timeline_entries"
+  ) || { total_records: 0, embedded_records: 0, coverage_percentage: 0 };
+
+  const sourcesCoverage = coverageData.rows.find(
+    (row: any) => row.table_name === "timeline_sources"
+  ) || { total_records: 0, embedded_records: 0, coverage_percentage: 0 };
+
+  return {
+    timelineEntries: {
+      total: Number(entriesCoverage.total_records) || 0,
+      embedded: Number(entriesCoverage.embedded_records) || 0,
+      percentage: Number(entriesCoverage.coverage_percentage) || 0,
+    },
+    timelineSources: {
+      total: Number(sourcesCoverage.total_records) || 0,
+      embedded: Number(sourcesCoverage.embedded_records) || 0,
+      percentage: Number(sourcesCoverage.coverage_percentage) || 0,
+    },
+  };
+}
+
+/**
+ * Estimate cost for embedding a batch of texts
+ */
+export function estimateEmbeddingCost(
+  textCount: number,
+  avgTokensPerText: number = 500
+): {
+  estimatedTokens: number;
+  estimatedCostUSD: number;
+} {
+  const estimatedTokens = textCount * avgTokensPerText;
+
+  // OpenAI text-embedding-3-small pricing: $0.02 per 1M tokens
+  const costPer1MTokens = 0.02;
+  const estimatedCostUSD = (estimatedTokens / 1000000) * costPer1MTokens;
+
+  return {
+    estimatedTokens,
+    estimatedCostUSD: Math.round(estimatedCostUSD * 100) / 100, // Round to 2 decimals
+  };
+}
+
+export const embeddingService = {
+  generateEmbedding,
+  generateBatchEmbeddings,
+  embedTimelineEntry,
+  embedTimelineSource,
+  embedAllMissingEntries,
+  getEmbeddingCoverage,
+  estimateEmbeddingCost,
+};
diff --git a/server/hybridSearchService.ts b/server/hybridSearchService.ts
new file mode 100644
index 0000000..6e813a4
--- /dev/null
+++ b/server/hybridSearchService.ts
@@ -0,0 +1,411 @@
+/**
+ * Hybrid Search Service
+ * Phase 1: SOTA Upgrade - Semantic Search Foundation
+ *
+ * Implements hybrid search combining:
+ * 1. Keyword search (BM25-like via PostgreSQL full-text)
+ * 2. Semantic search (vector similarity using pgvector)
+ * 3. Metadata filtering (dates, types, confidence levels)
+ *
+ * Uses Reciprocal Rank Fusion (RRF) to combine results
+ */
+
+import { db } from "./db";
+import { timelineEntries, type TimelineEntry } from "@shared/schema";
+import { sql, and, or, like, isNull, desc, eq, gte, lte, inArray } from "drizzle-orm";
+import { embeddingService } from "./embeddingService";
+
+export interface HybridSearchOptions {
+  caseId: string;
+  query: string;
+  topK?: number;
+  alpha?: number; // 0 = pure keyword, 1 = pure semantic, 0.5 = balanced
+  filters?: {
+    entryType?: 'task' | 'event';
+    dateFrom?: string;
+    dateTo?: string;
+    confidenceLevel?: string[];
+    tags?: string[];
+    eventSubtype?: string;
+    taskSubtype?: string;
+  };
+}
+
+export interface SearchResult {
+  entry: TimelineEntry;
+  score: number;
+  matchType: 'keyword' | 'semantic' | 'hybrid';
+  highlights?: string[];
+  similarity?: number; // For semantic matches
+}
+
+export interface SearchResponse {
+  results: SearchResult[];
+  metadata: {
+    query: string;
+    totalResults: number;
+    searchType: 'keyword' | 'semantic' | 'hybrid';
+    executionTimeMs: number;
+    alpha: number;
+  };
+}
+
+/**
+ * Perform hybrid search on timeline entries
+ */
+export async function hybridSearch(
+  options: HybridSearchOptions
+): Promise<SearchResponse> {
+  const startTime = Date.now();
+
+  const {
+    caseId,
+    query,
+    topK = 20,
+    alpha = 0.6, // Default: 60% semantic, 40% keyword
+    filters,
+  } = options;
+
+  // Validate query
+  if (!query || query.trim().length === 0) {
+    throw new Error("Search query cannot be empty");
+  }
+
+  try {
+    // 1. Generate query embedding for semantic search
+    const queryEmbedding = await embeddingService.generateEmbedding(query);
+    const queryVector = `[${queryEmbedding.embedding.join(",")}]`;
+
+    // 2. Perform keyword search
+    const keywordResults = await keywordSearch(caseId, query, filters, topK * 2); // Get more for fusion
+
+    // 3. Perform semantic search
+    const semanticResults = await semanticSearch(
+      caseId,
+      queryVector,
+      filters,
+      topK * 2 // Get more for fusion
+    );
+
+    // 4. Fuse results using Reciprocal Rank Fusion
+    const fusedResults = reciprocalRankFusion(
+      keywordResults,
+      semanticResults,
+      alpha,
+      60 // RRF constant k
+    );
+
+    // 5. Take top K results
+    const finalResults = fusedResults.slice(0, topK);
+
+    const executionTime = Date.now() - startTime;
+
+    return {
+      results: finalResults,
+      metadata: {
+        query,
+        totalResults: finalResults.length,
+        searchType: 'hybrid',
+        executionTimeMs: executionTime,
+        alpha,
+      },
+    };
+
+  } catch (error) {
+    console.error("Error in hybrid search:", error);
+
+    // Fallback to keyword-only search if embedding fails
+    console.log("Falling back to keyword-only search");
+    const keywordResults = await keywordSearch(caseId, query, filters, topK);
+
+    return {
+      results: keywordResults,
+      metadata: {
+        query,
+        totalResults: keywordResults.length,
+        searchType: 'keyword',
+        executionTimeMs: Date.now() - startTime,
+        alpha: 0,
+      },
+    };
+  }
+}
+
+/**
+ * Keyword search using PostgreSQL LIKE (future: full-text search)
+ */
+async function keywordSearch(
+  caseId: string,
+  query: string,
+  filters: HybridSearchOptions['filters'],
+  topK: number
+): Promise<SearchResult[]> {
+
+  const whereConditions: any[] = [
+    eq(timelineEntries.caseId, caseId),
+    isNull(timelineEntries.deletedAt),
+    or(
+      like(timelineEntries.description, `%${query}%`),
+      like(timelineEntries.detailedNotes, `%${query}%`)
+    ),
+  ];
+
+  // Apply filters
+  if (filters?.entryType) {
+    whereConditions.push(eq(timelineEntries.entryType, filters.entryType));
+  }
+
+  if (filters?.dateFrom) {
+    whereConditions.push(gte(timelineEntries.date, filters.dateFrom));
+  }
+
+  if (filters?.dateTo) {
+    whereConditions.push(lte(timelineEntries.date, filters.dateTo));
+  }
+
+  if (filters?.confidenceLevel && filters.confidenceLevel.length > 0) {
+    whereConditions.push(
+      inArray(timelineEntries.confidenceLevel, filters.confidenceLevel as any[])
+    );
+  }
+
+  const results = await db
+    .select()
+    .from(timelineEntries)
+    .where(and(...whereConditions))
+    .limit(topK)
+    .orderBy(desc(timelineEntries.date));
+
+  return results.map((entry, idx) => ({
+    entry,
+    score: 1.0 / (idx + 1), // Simple scoring: 1/rank
+    matchType: 'keyword' as const,
+    highlights: extractHighlights(entry, query),
+  }));
+}
+
+/**
+ * Semantic search using pgvector similarity
+ */
+async function semanticSearch(
+  caseId: string,
+  queryVector: string,
+  filters: HybridSearchOptions['filters'],
+  topK: number
+): Promise<SearchResult[]> {
+
+  // Build WHERE clause for filters
+  let filterConditions = `
+    WHERE te.case_id = '${caseId}'
+    AND te.deleted_at IS NULL
+    AND te.content_embedding IS NOT NULL
+  `;
+
+  if (filters?.entryType) {
+    filterConditions += ` AND te.entry_type = '${filters.entryType}'`;
+  }
+
+  if (filters?.dateFrom) {
+    filterConditions += ` AND te.date >= '${filters.dateFrom}'`;
+  }
+
+  if (filters?.dateTo) {
+    filterConditions += ` AND te.date <= '${filters.dateTo}'`;
+  }
+
+  // Execute semantic search
+  const results = await db.execute(sql`
+    SELECT
+      te.*,
+      1 - (te.content_embedding <=> ${sql.raw(queryVector)}::vector) as similarity
+    FROM timeline_entries te
+    ${sql.raw(filterConditions)}
+    ORDER BY te.content_embedding <=> ${sql.raw(queryVector)}::vector
+    LIMIT ${topK}
+  `);
+
+  return results.rows.map((row: any) => ({
+    entry: row as TimelineEntry,
+    score: row.similarity || 0,
+    matchType: 'semantic' as const,
+    similarity: row.similarity || 0,
+  }));
+}
+
+/**
+ * Reciprocal Rank Fusion algorithm
+ * Combines keyword and semantic search results
+ *
+ * RRF Score = Σ 1 / (k + rank)
+ * where k is a constant (typically 60)
+ */
+function reciprocalRankFusion(
+  keywordResults: SearchResult[],
+  semanticResults: SearchResult[],
+  alpha: number,
+  k: number = 60
+): SearchResult[] {
+
+  const scoreMap = new Map<string, {
+    entry: TimelineEntry;
+    score: number;
+    matchType: 'keyword' | 'semantic' | 'hybrid';
+    highlights?: string[];
+    similarity?: number;
+  }>();
+
+  // Score keyword results with weight (1 - alpha)
+  keywordResults.forEach((result, idx) => {
+    const rrfScore = (1 - alpha) / (k + idx + 1);
+    scoreMap.set(result.entry.id, {
+      entry: result.entry,
+      score: rrfScore,
+      matchType: 'keyword',
+      highlights: result.highlights,
+    });
+  });
+
+  // Add/merge semantic results with weight alpha
+  semanticResults.forEach((result, idx) => {
+    const rrfScore = alpha / (k + idx + 1);
+    const existing = scoreMap.get(result.entry.id);
+
+    if (existing) {
+      // Entry found in both searches - combine scores
+      scoreMap.set(result.entry.id, {
+        entry: result.entry,
+        score: existing.score + rrfScore,
+        matchType: 'hybrid',
+        highlights: existing.highlights,
+        similarity: result.similarity,
+      });
+    } else {
+      // Entry only in semantic search
+      scoreMap.set(result.entry.id, {
+        entry: result.entry,
+        score: rrfScore,
+        matchType: 'semantic',
+        similarity: result.similarity,
+      });
+    }
+  });
+
+  // Convert to array and sort by combined score
+  return Array.from(scoreMap.values())
+    .sort((a, b) => b.score - a.score)
+    .map(item => ({
+      entry: item.entry,
+      score: item.score,
+      matchType: item.matchType,
+      highlights: item.highlights,
+      similarity: item.similarity,
+    }));
+}
+
+/**
+ * Extract highlighted snippets from entry matching the query
+ */
+function extractHighlights(entry: TimelineEntry, query: string): string[] {
+  const highlights: string[] = [];
+  const queryLower = query.toLowerCase();
+
+  // Extract snippets from description
+  if (entry.description?.toLowerCase().includes(queryLower)) {
+    highlights.push(createSnippet(entry.description, query, 100));
+  }
+
+  // Extract snippets from detailed notes
+  if (entry.detailedNotes?.toLowerCase().includes(queryLower)) {
+    highlights.push(createSnippet(entry.detailedNotes, query, 100));
+  }
+
+  return highlights;
+}
+
+/**
+ * Create a snippet with context around the query match
+ */
+function createSnippet(
+  text: string,
+  query: string,
+  contextChars: number = 100
+): string {
+  const queryLower = query.toLowerCase();
+  const textLower = text.toLowerCase();
+  const idx = textLower.indexOf(queryLower);
+
+  if (idx === -1) return text.substring(0, 200) + '...';
+
+  const start = Math.max(0, idx - contextChars);
+  const end = Math.min(text.length, idx + query.length + contextChars);
+
+  return (
+    (start > 0 ? '...' : '') +
+    text.substring(start, end) +
+    (end < text.length ? '...' : '')
+  );
+}
+
+/**
+ * Keyword-only search (fallback when semantic search unavailable)
+ */
+export async function keywordOnlySearch(
+  caseId: string,
+  query: string,
+  topK: number = 20,
+  filters?: HybridSearchOptions['filters']
+): Promise<SearchResponse> {
+
+  const startTime = Date.now();
+  const results = await keywordSearch(caseId, query, filters, topK);
+
+  return {
+    results,
+    metadata: {
+      query,
+      totalResults: results.length,
+      searchType: 'keyword',
+      executionTimeMs: Date.now() - startTime,
+      alpha: 0,
+    },
+  };
+}
+
+/**
+ * Semantic-only search (for testing/debugging)
+ */
+export async function semanticOnlySearch(
+  caseId: string,
+  query: string,
+  topK: number = 20,
+  filters?: HybridSearchOptions['filters']
+): Promise<SearchResponse> {
+
+  const startTime = Date.now();
+
+  try {
+    const queryEmbedding = await embeddingService.generateEmbedding(query);
+    const queryVector = `[${queryEmbedding.embedding.join(",")}]`;
+    const results = await semanticSearch(caseId, queryVector, filters, topK);
+
+    return {
+      results,
+      metadata: {
+        query,
+        totalResults: results.length,
+        searchType: 'semantic',
+        executionTimeMs: Date.now() - startTime,
+        alpha: 1.0,
+      },
+    };
+  } catch (error) {
+    console.error("Error in semantic search:", error);
+    throw error;
+  }
+}
+
+export const searchService = {
+  hybridSearch,
+  keywordOnlySearch,
+  semanticOnlySearch,
+};
diff --git a/server/index.ts b/server/index.ts
index 8bf1912..35f8fcb 100644
--- a/server/index.ts
+++ b/server/index.ts
@@ -1,5 +1,6 @@
 import express, { type Request, Response, NextFunction } from "express";
 import { registerRoutes } from "./routes";
+import { registerSOTARoutes } from "./sotaRoutes";
 import { setupVite, serveStatic, log } from "./vite";
 
 const app = express();
@@ -39,6 +40,11 @@ app.use((req, res, next) => {
 (async () => {
   const server = await registerRoutes(app);
 
+  // Register SOTA Phase 1 routes (Semantic Search Foundation)
+  if (process.env.ENABLE_HYBRID_SEARCH === 'true' || app.get("env") === "development") {
+    registerSOTARoutes(app);
+  }
+
   app.use((err: any, _req: Request, res: Response, _next: NextFunction) => {
     const status = err.status || err.statusCode || 500;
     const message = err.message || "Internal Server Error";
diff --git a/server/ragService.ts b/server/ragService.ts
new file mode 100644
index 0000000..8d63bfa
--- /dev/null
+++ b/server/ragService.ts
@@ -0,0 +1,321 @@
+/**
+ * RAG (Retrieval-Augmented Generation) Service
+ * Phase 1: SOTA Upgrade - Semantic Search Foundation
+ *
+ * Enables natural language Q&A over legal documents using:
+ * - Hybrid search for retrieval
+ * - Claude Sonnet 4 for generation
+ * - Citation tracking for auditability
+ */
+
+import Anthropic from '@anthropic-ai/sdk';
+import { searchService, type SearchResult } from './hybridSearchService';
+
+const DEFAULT_MODEL_STR = "claude-sonnet-4-20250514";
+
+const anthropic = new Anthropic({
+  apiKey: process.env.ANTHROPIC_API_KEY,
+});
+
+export interface RAGQueryOptions {
+  caseId: string;
+  question: string;
+  topK?: number; // Number of documents to retrieve
+  alpha?: number; // Search algorithm balance
+  includeMetadata?: boolean;
+}
+
+export interface RAGResponse {
+  answer: string;
+  sources: Array<{
+    entryId: string;
+    description: string;
+    date: string;
+    entryType: string;
+    relevanceScore: number;
+    citation: string; // e.g., "[1]", "[2]"
+  }>;
+  confidence: number; // 0-1 based on source relevance
+  metadata?: {
+    model: string;
+    retrievalTimeMs: number;
+    generationTimeMs: number;
+    tokensUsed: number;
+  };
+}
+
+/**
+ * Query documents using RAG
+ */
+export async function queryDocuments(
+  options: RAGQueryOptions
+): Promise<RAGResponse> {
+
+  const {
+    caseId,
+    question,
+    topK = 5,
+    alpha = 0.6,
+    includeMetadata = false,
+  } = options;
+
+  const startTime = Date.now();
+
+  // Step 1: Retrieve relevant documents using hybrid search
+  console.log(`RAG: Retrieving documents for question: "${question}"`);
+  const searchResponse = await searchService.hybridSearch({
+    caseId,
+    query: question,
+    topK,
+    alpha,
+  });
+
+  const retrievalTime = Date.now() - startTime;
+
+  if (searchResponse.results.length === 0) {
+    return {
+      answer: "I couldn't find any relevant timeline entries to answer your question. Please try rephrasing or asking about different aspects of the case.",
+      sources: [],
+      confidence: 0,
+      metadata: includeMetadata ? {
+        model: DEFAULT_MODEL_STR,
+        retrievalTimeMs: retrievalTime,
+        generationTimeMs: 0,
+        tokensUsed: 0,
+      } : undefined,
+    };
+  }
+
+  // Step 2: Format context from retrieved documents
+  const context = formatContext(searchResponse.results);
+  const sources = searchResponse.results.map((result, idx) => ({
+    entryId: result.entry.id,
+    description: result.entry.description,
+    date: result.entry.date,
+    entryType: result.entry.entryType,
+    relevanceScore: result.score,
+    citation: `[${idx + 1}]`,
+  }));
+
+  // Step 3: Generate answer using Claude
+  const generationStartTime = Date.now();
+  const answer = await generateAnswer(question, context, searchResponse.results);
+  const generationTime = Date.now() - generationStartTime;
+
+  // Step 4: Calculate confidence based on source relevance
+  const avgRelevance = searchResponse.results.reduce(
+    (sum, r) => sum + r.score,
+    0
+  ) / searchResponse.results.length;
+  const confidence = Math.min(avgRelevance * 1.2, 1.0); // Boost slightly, cap at 1.0
+
+  return {
+    answer,
+    sources,
+    confidence,
+    metadata: includeMetadata ? {
+      model: DEFAULT_MODEL_STR,
+      retrievalTimeMs: retrievalTime,
+      generationTimeMs: generationTime,
+      tokensUsed: 0, // Anthropic doesn't return token count in same format
+    } : undefined,
+  };
+}
+
+/**
+ * Format retrieved documents as context for the LLM
+ */
+function formatContext(results: SearchResult[]): string {
+  return results
+    .map((result, idx) => {
+      const entry = result.entry;
+      return `
+[${idx + 1}] Timeline Entry
+Date: ${entry.date}
+Type: ${entry.entryType}${entry.eventSubtype ? ` (${entry.eventSubtype})` : ''}${entry.taskSubtype ? ` (${entry.taskSubtype})` : ''}
+Description: ${entry.description}
+${entry.detailedNotes ? `Details: ${entry.detailedNotes}` : ''}
+${entry.tags && entry.tags.length > 0 ? `Tags: ${entry.tags.join(', ')}` : ''}
+${result.similarity !== undefined ? `Relevance: ${(result.similarity * 100).toFixed(1)}%` : ''}
+`.trim();
+    })
+    .join('\n\n---\n\n');
+}
+
+/**
+ * Generate answer using Claude Sonnet 4
+ */
+async function generateAnswer(
+  question: string,
+  context: string,
+  results: SearchResult[]
+): Promise<string> {
+
+  const systemPrompt = `You are a legal analyst assistant for ChittyChronicle, a legal timeline management system. Your role is to answer questions about case timelines based ONLY on the provided timeline entries.
+
+CRITICAL INSTRUCTIONS:
+- Answer based ONLY on the provided timeline entries
+- If the answer cannot be found in the timeline entries, explicitly state this
+- ALWAYS cite specific timeline entry numbers [1], [2], etc. in your answer
+- If information is missing, unclear, or contradictory, state that explicitly
+- Do not make assumptions beyond what's in the timeline entries
+- Highlight any contradictions or uncertainties you notice
+- Be concise but thorough
+- Use legal terminology appropriately`;
+
+  const userPrompt = `Timeline Entries:
+${context}
+
+Question: ${question}
+
+Please provide a clear, concise answer based on the timeline entries above. Remember to cite specific entries using [1], [2], etc.`;
+
+  try {
+    const response = await anthropic.messages.create({
+      model: DEFAULT_MODEL_STR,
+      max_tokens: 2000,
+      temperature: 0.1, // Low temperature for factual accuracy
+      system: systemPrompt,
+      messages: [{
+        role: 'user',
+        content: userPrompt,
+      }],
+    });
+
+    // Extract text from response
+    const textContent = response.content.find(c => c.type === 'text');
+    if (!textContent || textContent.type !== 'text') {
+      throw new Error('No text response from Claude');
+    }
+
+    return textContent.text;
+
+  } catch (error) {
+    console.error('Error generating RAG answer:', error);
+
+    // Fallback: return a summary of the sources
+    return `I encountered an error generating a detailed answer, but here are the relevant timeline entries I found:\n\n` +
+      results.map((r, idx) => `[${idx + 1}] ${r.entry.date}: ${r.entry.description}`).join('\n');
+  }
+}
+
+/**
+ * Multi-turn RAG conversation (maintains context)
+ */
+export class RAGConversation {
+  private caseId: string;
+  private conversationHistory: Array<{
+    question: string;
+    answer: string;
+    sources: RAGResponse['sources'];
+  }> = [];
+
+  constructor(caseId: string) {
+    this.caseId = caseId;
+  }
+
+  async ask(question: string, topK: number = 5): Promise<RAGResponse> {
+    const response = await queryDocuments({
+      caseId: this.caseId,
+      question,
+      topK,
+      includeMetadata: true,
+    });
+
+    // Add to conversation history
+    this.conversationHistory.push({
+      question,
+      answer: response.answer,
+      sources: response.sources,
+    });
+
+    return response;
+  }
+
+  getHistory() {
+    return this.conversationHistory;
+  }
+
+  clear() {
+    this.conversationHistory = [];
+  }
+}
+
+/**
+ * Batch query multiple questions (useful for case analysis)
+ */
+export async function batchQuery(
+  caseId: string,
+  questions: string[],
+  topK: number = 5
+): Promise<RAGResponse[]> {
+
+  const responses: RAGResponse[] = [];
+
+  for (const question of questions) {
+    try {
+      const response = await queryDocuments({
+        caseId,
+        question,
+        topK,
+      });
+      responses.push(response);
+
+      // Rate limiting: wait 1 second between questions
+      if (responses.length < questions.length) {
+        await new Promise(resolve => setTimeout(resolve, 1000));
+      }
+    } catch (error) {
+      console.error(`Error processing question "${question}":`, error);
+      responses.push({
+        answer: `Error processing question: ${error.message}`,
+        sources: [],
+        confidence: 0,
+      });
+    }
+  }
+
+  return responses;
+}
+
+/**
+ * Generate timeline summary for a case
+ */
+export async function generateTimelineSummary(
+  caseId: string
+): Promise<string> {
+
+  const response = await queryDocuments({
+    caseId,
+    question: "Provide a comprehensive chronological summary of all key events and tasks in this case.",
+    topK: 20, // Get more entries for comprehensive summary
+    alpha: 0.5, // Balanced search
+  });
+
+  return response.answer;
+}
+
+/**
+ * Identify potential issues or gaps in the timeline
+ */
+export async function analyzeTimelineGaps(
+  caseId: string
+): Promise<string> {
+
+  const response = await queryDocuments({
+    caseId,
+    question: "Identify any gaps, missing information, or potential issues in the timeline that should be addressed.",
+    topK: 20,
+    alpha: 0.6,
+  });
+
+  return response.answer;
+}
+
+export const ragService = {
+  queryDocuments,
+  batchQuery,
+  generateTimelineSummary,
+  analyzeTimelineGaps,
+  RAGConversation,
+};
diff --git a/server/sotaRoutes.ts b/server/sotaRoutes.ts
new file mode 100644
index 0000000..c41f083
--- /dev/null
+++ b/server/sotaRoutes.ts
@@ -0,0 +1,455 @@
+/**
+ * SOTA Upgrade API Routes
+ * Phase 1: Semantic Search Foundation
+ *
+ * New endpoints for:
+ * - Hybrid search (keyword + semantic)
+ * - RAG document Q&A
+ * - Embedding generation and management
+ */
+
+import type { Express } from "express";
+import { searchService } from "./hybridSearchService";
+import { ragService } from "./ragService";
+import { embeddingService } from "./embeddingService";
+
+export function registerSOTARoutes(app: Express) {
+
+  /**
+   * Enhanced Hybrid Search Endpoint
+   * GET /api/timeline/search/hybrid
+   *
+   * Query Parameters:
+   * - caseId (required): UUID of the case
+   * - query (required): Search query text
+   * - topK (optional): Number of results to return (default: 20)
+   * - alpha (optional): Search balance 0-1 (default: 0.6)
+   *   - 0 = pure keyword
+   *   - 1 = pure semantic
+   *   - 0.6 = 60% semantic, 40% keyword (recommended)
+   * - entryType (optional): 'task' or 'event'
+   * - dateFrom (optional): ISO date string
+   * - dateTo (optional): ISO date string
+   *
+   * Example: /api/timeline/search/hybrid?caseId=123&query=contract%20breach&alpha=0.6
+   */
+  app.get('/api/timeline/search/hybrid', async (req: any, res) => {
+    try {
+      const { caseId, query, topK, alpha, entryType, dateFrom, dateTo, confidenceLevel } = req.query;
+
+      if (!caseId || !query) {
+        return res.status(400).json({
+          error: "caseId and query are required",
+        });
+      }
+
+      // Parse query parameters
+      const options = {
+        caseId: caseId as string,
+        query: query as string,
+        topK: topK ? parseInt(topK as string) : 20,
+        alpha: alpha ? parseFloat(alpha as string) : 0.6,
+        filters: {
+          entryType: entryType as 'task' | 'event' | undefined,
+          dateFrom: dateFrom as string | undefined,
+          dateTo: dateTo as string | undefined,
+          confidenceLevel: confidenceLevel ? (confidenceLevel as string).split(',') : undefined,
+        },
+      };
+
+      // Validate alpha parameter
+      if (options.alpha < 0 || options.alpha > 1) {
+        return res.status(400).json({
+          error: "alpha must be between 0 and 1",
+        });
+      }
+
+      const response = await searchService.hybridSearch(options);
+
+      res.json(response);
+
+    } catch (error) {
+      console.error("Error in hybrid search:", error);
+      res.status(500).json({
+        error: "Failed to perform hybrid search",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * RAG Document Q&A Endpoint
+   * POST /api/timeline/ask
+   *
+   * Request Body:
+   * {
+   *   "caseId": "uuid",
+   *   "question": "What evidence supports the breach claim?",
+   *   "topK": 5,  // optional
+   *   "alpha": 0.6  // optional
+   * }
+   *
+   * Response:
+   * {
+   *   "answer": "Based on the timeline entries...",
+   *   "sources": [...],
+   *   "confidence": 0.85
+   * }
+   */
+  app.post('/api/timeline/ask', async (req: any, res) => {
+    try {
+      const { caseId, question, topK, alpha, includeMetadata } = req.body;
+
+      if (!caseId || !question) {
+        return res.status(400).json({
+          error: "caseId and question are required",
+        });
+      }
+
+      const response = await ragService.queryDocuments({
+        caseId,
+        question,
+        topK: topK || 5,
+        alpha: alpha || 0.6,
+        includeMetadata: includeMetadata || false,
+      });
+
+      res.json(response);
+
+    } catch (error) {
+      console.error("Error in RAG query:", error);
+      res.status(500).json({
+        error: "Failed to answer question",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Generate Timeline Summary
+   * GET /api/timeline/summary/:caseId
+   *
+   * Generates a comprehensive chronological summary of the case timeline
+   */
+  app.get('/api/timeline/summary/:caseId', async (req: any, res) => {
+    try {
+      const { caseId } = req.params;
+
+      if (!caseId) {
+        return res.status(400).json({ error: "caseId is required" });
+      }
+
+      const summary = await ragService.generateTimelineSummary(caseId);
+
+      res.json({
+        caseId,
+        summary,
+        generatedAt: new Date().toISOString(),
+      });
+
+    } catch (error) {
+      console.error("Error generating summary:", error);
+      res.status(500).json({
+        error: "Failed to generate timeline summary",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Analyze Timeline Gaps
+   * GET /api/timeline/analyze/gaps/:caseId
+   *
+   * Identifies potential gaps, missing information, or issues in the timeline
+   */
+  app.get('/api/timeline/analyze/gaps/:caseId', async (req: any, res) => {
+    try {
+      const { caseId } = req.params;
+
+      if (!caseId) {
+        return res.status(400).json({ error: "caseId is required" });
+      }
+
+      const analysis = await ragService.analyzeTimelineGaps(caseId);
+
+      res.json({
+        caseId,
+        analysis,
+        analyzedAt: new Date().toISOString(),
+      });
+
+    } catch (error) {
+      console.error("Error analyzing gaps:", error);
+      res.status(500).json({
+        error: "Failed to analyze timeline gaps",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Batch RAG Queries
+   * POST /api/timeline/ask/batch
+   *
+   * Request Body:
+   * {
+   *   "caseId": "uuid",
+   *   "questions": ["Question 1?", "Question 2?"],
+   *   "topK": 5  // optional
+   * }
+   */
+  app.post('/api/timeline/ask/batch', async (req: any, res) => {
+    try {
+      const { caseId, questions, topK } = req.body;
+
+      if (!caseId || !questions || !Array.isArray(questions)) {
+        return res.status(400).json({
+          error: "caseId and questions array are required",
+        });
+      }
+
+      if (questions.length > 10) {
+        return res.status(400).json({
+          error: "Maximum 10 questions per batch",
+        });
+      }
+
+      const responses = await ragService.batchQuery(
+        caseId,
+        questions,
+        topK || 5
+      );
+
+      res.json({
+        caseId,
+        results: responses,
+        processedAt: new Date().toISOString(),
+      });
+
+    } catch (error) {
+      console.error("Error in batch query:", error);
+      res.status(500).json({
+        error: "Failed to process batch queries",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Generate Embedding for Timeline Entry
+   * POST /api/admin/embeddings/entry/:entryId
+   *
+   * Generates or regenerates embedding for a specific timeline entry
+   */
+  app.post('/api/admin/embeddings/entry/:entryId', async (req: any, res) => {
+    try {
+      const { entryId } = req.params;
+
+      if (!entryId) {
+        return res.status(400).json({ error: "entryId is required" });
+      }
+
+      await embeddingService.embedTimelineEntry(entryId);
+
+      res.json({
+        success: true,
+        entryId,
+        message: "Embedding generated successfully",
+      });
+
+    } catch (error) {
+      console.error("Error generating embedding:", error);
+      res.status(500).json({
+        error: "Failed to generate embedding",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Generate Embeddings for All Missing Entries
+   * POST /api/admin/embeddings/generate
+   *
+   * Request Body (optional):
+   * {
+   *   "caseId": "uuid",  // Optional: limit to specific case
+   *   "batchSize": 100   // Optional: batch size for processing
+   * }
+   */
+  app.post('/api/admin/embeddings/generate', async (req: any, res) => {
+    try {
+      const { caseId, batchSize } = req.body;
+
+      // Start async job (don't wait for completion)
+      const jobPromise = embeddingService.embedAllMissingEntries(
+        caseId,
+        batchSize || 100
+      );
+
+      // Return immediately with job ID
+      res.json({
+        success: true,
+        message: "Embedding generation started",
+        caseId: caseId || "all",
+        status: "processing",
+      });
+
+      // Process in background
+      jobPromise
+        .then(stats => {
+          console.log("Embedding generation completed:", stats);
+        })
+        .catch(error => {
+          console.error("Embedding generation failed:", error);
+        });
+
+    } catch (error) {
+      console.error("Error starting embedding generation:", error);
+      res.status(500).json({
+        error: "Failed to start embedding generation",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Get Embedding Coverage Statistics
+   * GET /api/admin/embeddings/coverage
+   *
+   * Returns statistics about embedding coverage across timeline entries and sources
+   */
+  app.get('/api/admin/embeddings/coverage', async (req: any, res) => {
+    try {
+      const coverage = await embeddingService.getEmbeddingCoverage();
+
+      res.json({
+        coverage,
+        timestamp: new Date().toISOString(),
+      });
+
+    } catch (error) {
+      console.error("Error getting coverage:", error);
+      res.status(500).json({
+        error: "Failed to get embedding coverage",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Estimate Embedding Cost
+   * POST /api/admin/embeddings/estimate-cost
+   *
+   * Request Body:
+   * {
+   *   "textCount": 1000,
+   *   "avgTokensPerText": 500  // optional, defaults to 500
+   * }
+   */
+  app.post('/api/admin/embeddings/estimate-cost', async (req: any, res) => {
+    try {
+      const { textCount, avgTokensPerText } = req.body;
+
+      if (!textCount || textCount < 1) {
+        return res.status(400).json({
+          error: "textCount must be a positive number",
+        });
+      }
+
+      const estimate = embeddingService.estimateEmbeddingCost(
+        textCount,
+        avgTokensPerText || 500
+      );
+
+      res.json(estimate);
+
+    } catch (error) {
+      console.error("Error estimating cost:", error);
+      res.status(500).json({
+        error: "Failed to estimate cost",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Keyword-Only Search (Fallback)
+   * GET /api/timeline/search/keyword
+   *
+   * Provides keyword-only search without semantic capabilities
+   * Useful for testing or when embeddings are unavailable
+   */
+  app.get('/api/timeline/search/keyword', async (req: any, res) => {
+    try {
+      const { caseId, query, topK } = req.query;
+
+      if (!caseId || !query) {
+        return res.status(400).json({
+          error: "caseId and query are required",
+        });
+      }
+
+      const response = await searchService.keywordOnlySearch(
+        caseId as string,
+        query as string,
+        topK ? parseInt(topK as string) : 20
+      );
+
+      res.json(response);
+
+    } catch (error) {
+      console.error("Error in keyword search:", error);
+      res.status(500).json({
+        error: "Failed to perform keyword search",
+        message: error.message,
+      });
+    }
+  });
+
+  /**
+   * Semantic-Only Search (Testing/Debugging)
+   * GET /api/timeline/search/semantic
+   *
+   * Provides pure semantic search without keyword matching
+   * Useful for testing or comparing search strategies
+   */
+  app.get('/api/timeline/search/semantic', async (req: any, res) => {
+    try {
+      const { caseId, query, topK } = req.query;
+
+      if (!caseId || !query) {
+        return res.status(400).json({
+          error: "caseId and query are required",
+        });
+      }
+
+      const response = await searchService.semanticOnlySearch(
+        caseId as string,
+        query as string,
+        topK ? parseInt(topK as string) : 20
+      );
+
+      res.json(response);
+
+    } catch (error) {
+      console.error("Error in semantic search:", error);
+      res.status(500).json({
+        error: "Failed to perform semantic search",
+        message: error.message,
+      });
+    }
+  });
+
+  console.log("✅ SOTA Phase 1 routes registered:");
+  console.log("   - GET  /api/timeline/search/hybrid");
+  console.log("   - POST /api/timeline/ask");
+  console.log("   - GET  /api/timeline/summary/:caseId");
+  console.log("   - GET  /api/timeline/analyze/gaps/:caseId");
+  console.log("   - POST /api/timeline/ask/batch");
+  console.log("   - POST /api/admin/embeddings/entry/:entryId");
+  console.log("   - POST /api/admin/embeddings/generate");
+  console.log("   - GET  /api/admin/embeddings/coverage");
+  console.log("   - POST /api/admin/embeddings/estimate-cost");
+  console.log("   - GET  /api/timeline/search/keyword");
+  console.log("   - GET  /api/timeline/search/semantic");
+}
diff --git a/shared/schema.ts b/shared/schema.ts
index efe682b..ba3360f 100644
--- a/shared/schema.ts
+++ b/shared/schema.ts
@@ -99,6 +99,11 @@ export const timelineEntries = pgTable("timeline_entries", {
   messageSource: messageSourceEnum("message_source"),
   messageDirection: messageDirectionEnum("message_direction"),
   metadata: jsonb("metadata"),
+  // Vector embeddings for semantic search (Phase 1: SOTA Upgrade)
+  descriptionEmbedding: varchar("description_embedding"), // vector(768) - Legal-BERT
+  contentEmbedding: varchar("content_embedding"), // vector(1536) - OpenAI
+  embeddingModel: varchar("embedding_model", { length: 100 }),
+  embeddingGeneratedAt: timestamp("embedding_generated_at"),
 });
 
 // Sources table
@@ -117,6 +122,10 @@ export const timelineSources = pgTable("timeline_sources", {
   verifiedBy: varchar("verified_by", { length: 255 }),
   chittyAssetId: varchar("chitty_asset_id", { length: 255 }),
   metadata: jsonb("metadata"),
+  // Vector embeddings for semantic search (Phase 1: SOTA Upgrade)
+  excerptEmbedding: varchar("excerpt_embedding"), // vector(768) - Legal-BERT
+  embeddingModel: varchar("embedding_model", { length: 100 }),
+  embeddingGeneratedAt: timestamp("embedding_generated_at"),
 });
 
 // Contradictions table
diff --git a/tests/phase1-integration.test.ts b/tests/phase1-integration.test.ts
new file mode 100644
index 0000000..57abcd0
--- /dev/null
+++ b/tests/phase1-integration.test.ts
@@ -0,0 +1,353 @@
+/**
+ * Integration Tests for Phase 1: Semantic Search Foundation
+ * Tests all SOTA endpoints to validate functionality before production
+ *
+ * Usage:
+ *   npm test                    # Run all tests
+ *   npm test -- --grep "hybrid" # Run specific tests
+ *   npm test -- --bail          # Stop on first failure
+ */
+
+import { describe, it, before, after } from 'node:test';
+import assert from 'node:assert/strict';
+
+// Test configuration
+const BASE_URL = process.env.TEST_BASE_URL || 'http://localhost:5000';
+const TEST_CASE_ID = process.env.TEST_CASE_ID; // Must provide a real case ID
+const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
+const ANTHROPIC_API_KEY = process.env.ANTHROPIC_API_KEY;
+
+// Helper to make HTTP requests
+async function request(method: string, path: string, body?: any) {
+  const url = `${BASE_URL}${path}`;
+  const options: RequestInit = {
+    method,
+    headers: {
+      'Content-Type': 'application/json',
+    },
+  };
+
+  if (body) {
+    options.body = JSON.stringify(body);
+  }
+
+  const response = await fetch(url, options);
+  const data = response.ok ? await response.json() : null;
+
+  return {
+    status: response.status,
+    ok: response.ok,
+    data,
+  };
+}
+
+// Pre-flight checks
+describe('Pre-Flight Checks', () => {
+  it('should have required environment variables', () => {
+    assert.ok(OPENAI_API_KEY, 'OPENAI_API_KEY is required');
+    assert.ok(ANTHROPIC_API_KEY, 'ANTHROPIC_API_KEY is required');
+    assert.ok(TEST_CASE_ID, 'TEST_CASE_ID is required for integration tests');
+  });
+
+  it('should connect to server', async () => {
+    const res = await request('GET', '/');
+    assert.ok(res.ok, 'Server should be reachable');
+  });
+});
+
+// Embedding Service Tests
+describe('Embedding Service', () => {
+  it('should get embedding coverage statistics', async () => {
+    const res = await request('GET', '/api/admin/embeddings/coverage');
+    assert.ok(res.ok, 'Coverage endpoint should work');
+    assert.ok(res.data.coverage, 'Should return coverage data');
+    assert.ok('timelineEntries' in res.data.coverage, 'Should include timeline entries coverage');
+    assert.ok('timelineSources' in res.data.coverage, 'Should include timeline sources coverage');
+  });
+
+  it('should estimate embedding cost', async () => {
+    const res = await request('POST', '/api/admin/embeddings/estimate-cost', {
+      textCount: 100,
+      avgTokensPerText: 500,
+    });
+    assert.ok(res.ok, 'Cost estimation should work');
+    assert.ok(res.data.estimatedTokens, 'Should return estimated tokens');
+    assert.ok(res.data.estimatedCostUSD !== undefined, 'Should return estimated cost');
+    assert.equal(res.data.estimatedTokens, 50000, 'Should calculate correct token count');
+  });
+
+  it('should reject invalid cost estimation', async () => {
+    const res = await request('POST', '/api/admin/embeddings/estimate-cost', {
+      textCount: -1,
+    });
+    assert.equal(res.status, 400, 'Should reject negative text count');
+  });
+
+  it('should start embedding generation job', async () => {
+    const res = await request('POST', '/api/admin/embeddings/generate', {
+      caseId: TEST_CASE_ID,
+      batchSize: 10, // Small batch for testing
+    });
+    assert.ok(res.ok, 'Embedding generation should start');
+    assert.equal(res.data.status, 'processing', 'Should return processing status');
+  });
+});
+
+// Hybrid Search Tests
+describe('Hybrid Search', () => {
+  it('should perform hybrid search', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=contract&alpha=0.6`
+    );
+    assert.ok(res.ok, 'Hybrid search should work');
+    assert.ok(res.data.results, 'Should return results array');
+    assert.ok(res.data.metadata, 'Should return metadata');
+    assert.equal(res.data.metadata.searchType, 'hybrid', 'Should indicate hybrid search');
+    assert.equal(res.data.metadata.alpha, 0.6, 'Should respect alpha parameter');
+  });
+
+  it('should require caseId and query parameters', async () => {
+    const res = await request('GET', '/api/timeline/search/hybrid?query=test');
+    assert.equal(res.status, 400, 'Should reject request without caseId');
+  });
+
+  it('should validate alpha parameter range', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=test&alpha=1.5`
+    );
+    assert.equal(res.status, 400, 'Should reject alpha > 1');
+  });
+
+  it('should support metadata filtering', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=contract&entryType=event&dateFrom=2024-01-01`
+    );
+    assert.ok(res.ok, 'Should support filters');
+    // All results should match filter
+    if (res.data.results.length > 0) {
+      res.data.results.forEach((r: any) => {
+        assert.equal(r.entry.entryType, 'event', 'Results should match entry type filter');
+      });
+    }
+  });
+
+  it('should adjust balance with alpha parameter', async () => {
+    // Test pure keyword (alpha=0)
+    const keyword = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=contract&alpha=0`
+    );
+    assert.ok(keyword.ok, 'Pure keyword search should work');
+
+    // Test pure semantic (alpha=1)
+    const semantic = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=contract&alpha=1`
+    );
+    assert.ok(semantic.ok, 'Pure semantic search should work');
+  });
+});
+
+// Keyword-Only Search Tests
+describe('Keyword Search', () => {
+  it('should perform keyword-only search', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/keyword?caseId=${TEST_CASE_ID}&query=contract`
+    );
+    assert.ok(res.ok, 'Keyword search should work');
+    assert.equal(res.data.metadata.searchType, 'keyword', 'Should indicate keyword search');
+  });
+
+  it('should return results without embeddings', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/keyword?caseId=${TEST_CASE_ID}&query=test`
+    );
+    assert.ok(res.ok, 'Keyword search should work even without embeddings');
+    assert.ok(Array.isArray(res.data.results), 'Should return results array');
+  });
+});
+
+// Semantic-Only Search Tests
+describe('Semantic Search', () => {
+  it('should perform semantic-only search', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/semantic?caseId=${TEST_CASE_ID}&query=breach of contract`
+    );
+    // May fail if no embeddings exist yet
+    if (res.ok) {
+      assert.equal(res.data.metadata.searchType, 'semantic', 'Should indicate semantic search');
+      assert.equal(res.data.metadata.alpha, 1.0, 'Should use alpha=1 for pure semantic');
+    } else {
+      console.log('⚠️  Semantic search failed - embeddings may not be generated yet');
+    }
+  });
+});
+
+// RAG Q&A Tests
+describe('RAG Document Q&A', () => {
+  it('should answer questions about documents', async () => {
+    const res = await request('POST', '/api/timeline/ask', {
+      caseId: TEST_CASE_ID,
+      question: 'What are the key dates in this case?',
+      topK: 5,
+    });
+
+    if (res.ok) {
+      assert.ok(res.data.answer, 'Should return an answer');
+      assert.ok(Array.isArray(res.data.sources), 'Should return sources');
+      assert.ok(res.data.confidence !== undefined, 'Should return confidence score');
+      assert.ok(res.data.confidence >= 0 && res.data.confidence <= 1, 'Confidence should be 0-1');
+    } else {
+      console.log('⚠️  RAG Q&A failed - may need embeddings or API keys');
+    }
+  });
+
+  it('should require caseId and question', async () => {
+    const res = await request('POST', '/api/timeline/ask', {
+      question: 'test',
+    });
+    assert.equal(res.status, 400, 'Should reject request without caseId');
+  });
+
+  it('should include citations in answer', async () => {
+    const res = await request('POST', '/api/timeline/ask', {
+      caseId: TEST_CASE_ID,
+      question: 'Summarize the timeline',
+      topK: 3,
+    });
+
+    if (res.ok && res.data.answer) {
+      // Check if answer contains citation markers [1], [2], etc.
+      const hasCitations = /\[\d+\]/.test(res.data.answer);
+      assert.ok(hasCitations, 'Answer should include citation markers like [1], [2]');
+    }
+  });
+});
+
+// Batch RAG Tests
+describe('Batch RAG Queries', () => {
+  it('should process multiple questions', async () => {
+    const res = await request('POST', '/api/timeline/ask/batch', {
+      caseId: TEST_CASE_ID,
+      questions: [
+        'What is the case about?',
+        'Who are the parties?',
+        'What are the key dates?',
+      ],
+      topK: 3,
+    });
+
+    if (res.ok) {
+      assert.ok(Array.isArray(res.data.results), 'Should return results array');
+      assert.equal(res.data.results.length, 3, 'Should answer all questions');
+      res.data.results.forEach((r: any) => {
+        assert.ok(r.answer, 'Each result should have an answer');
+      });
+    }
+  });
+
+  it('should reject too many questions', async () => {
+    const res = await request('POST', '/api/timeline/ask/batch', {
+      caseId: TEST_CASE_ID,
+      questions: Array(15).fill('test question'),
+    });
+    assert.equal(res.status, 400, 'Should reject batches > 10 questions');
+  });
+});
+
+// Timeline Summary Tests
+describe('Timeline Summary', () => {
+  it('should generate case timeline summary', async () => {
+    const res = await request('GET', `/api/timeline/summary/${TEST_CASE_ID}`);
+
+    if (res.ok) {
+      assert.ok(res.data.summary, 'Should return summary');
+      assert.equal(res.data.caseId, TEST_CASE_ID, 'Should include case ID');
+      assert.ok(res.data.generatedAt, 'Should include timestamp');
+    }
+  });
+});
+
+// Gap Analysis Tests
+describe('Timeline Gap Analysis', () => {
+  it('should analyze timeline for gaps', async () => {
+    const res = await request('GET', `/api/timeline/analyze/gaps/${TEST_CASE_ID}`);
+
+    if (res.ok) {
+      assert.ok(res.data.analysis, 'Should return analysis');
+      assert.equal(res.data.caseId, TEST_CASE_ID, 'Should include case ID');
+      assert.ok(res.data.analyzedAt, 'Should include timestamp');
+    }
+  });
+});
+
+// Performance Tests
+describe('Performance', () => {
+  it('should return hybrid search results within 2 seconds', async () => {
+    const start = Date.now();
+    const res = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=contract`
+    );
+    const duration = Date.now() - start;
+
+    assert.ok(res.ok, 'Search should succeed');
+    assert.ok(duration < 2000, `Search took ${duration}ms (target: <2000ms)`);
+  });
+
+  it('should include execution time in metadata', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=test`
+    );
+
+    if (res.ok) {
+      assert.ok(
+        res.data.metadata.executionTimeMs !== undefined,
+        'Should include execution time'
+      );
+    }
+  });
+});
+
+// Error Handling Tests
+describe('Error Handling', () => {
+  it('should handle invalid case ID gracefully', async () => {
+    const res = await request(
+      'GET',
+      '/api/timeline/search/hybrid?caseId=invalid-uuid&query=test'
+    );
+    assert.ok(!res.ok, 'Should fail with invalid case ID');
+  });
+
+  it('should handle empty query gracefully', async () => {
+    const res = await request(
+      'GET',
+      `/api/timeline/search/hybrid?caseId=${TEST_CASE_ID}&query=`
+    );
+    assert.equal(res.status, 400, 'Should reject empty query');
+  });
+
+  it('should return appropriate error messages', async () => {
+    const res = await request('POST', '/api/timeline/ask', {
+      // Missing required fields
+    });
+    assert.equal(res.status, 400, 'Should return 400 for bad request');
+  });
+});
+
+// Run tests with summary
+console.log('\n═══════════════════════════════════════════════════════');
+console.log('  Phase 1 Integration Tests');
+console.log('═══════════════════════════════════════════════════════\n');
+console.log(`Base URL: ${BASE_URL}`);
+console.log(`Test Case ID: ${TEST_CASE_ID || '❌ NOT SET'}`);
+console.log(`OpenAI API Key: ${OPENAI_API_KEY ? '✅ Set' : '❌ NOT SET'}`);
+console.log(`Anthropic API Key: ${ANTHROPIC_API_KEY ? '✅ Set' : '❌ NOT SET'}`);
+console.log('\n═══════════════════════════════════════════════════════\n');