Phase 2 RAG Benchmark System - Connectome Integration Plan

Document Version: 1.0 Created: 2025-10-22 Status: Planning Phase Target: Connectome Server Deployment Integration

📋 Executive Summary

This document outlines the complete integration plan for connecting the Phase 2 RAG Benchmark System (Performance Tracking, Cost Optimization, A/B Testing) with the existing Literature Monitoring System deployed on the Connectome server.

Current State

✅ Completed Systems:

Phase 1: RAGAS Quality Metrics (Days 1-10)
- EvaluationDataset with Pydantic validation
- RAGASEvaluator (faithfulness, answer_relevancy, context_precision, context_recall)
- BaselineEvaluator with aggregation
- PrometheusMetricsExporter + Grafana dashboard
Phase 2: Performance Benchmark System (Days 1-10)
- PerformanceTracker (latency, token usage, cost estimation)
- CostOptimizer (budget tracking, optimization suggestions)
- ABTest framework (variant testing, statistical analysis)

✅ Existing Connectome Deployment:

Literature monitoring (8 ArXiv + 4 PubMed sources)
Strategic paper collection (400-600 papers/month)
Docker-based deployment (deploy_to_connectome.sh)
PostgreSQL + Redis + Celery infrastructure

❌ Gap: Phase 2 systems are standalone - not integrated into Connectome deployment or FastAPI application.

🎯 Integration Objectives

Primary Goals

Expose Phase 2 via REST API - Add evaluation endpoints to existing FastAPI app
Deploy Monitoring Stack - Add Prometheus + Grafana to docker-compose
Automate Benchmarks - Create Celery tasks for periodic RAG evaluation
Unified Dashboard - Combine literature monitoring + RAG performance metrics
Production Ready - Health checks, logging, alerts, documentation

Success Criteria

✅ All Phase 2 components accessible via /api/v1/rag-evaluation/* endpoints
✅ Grafana dashboard live at http://connectome:3000 with real-time metrics
✅ Automated daily RAG benchmarks via Celery Beat
✅ Deployment script updated with Phase 2 setup steps
✅ Zero downtime deployment (existing literature monitoring unaffected)

🏗️ System Architecture

Current Architecture (Before Integration)

┌─────────────────────────────────────────────────────────────────┐
│                    Connectome Server                            │
├─────────────────────────────────────────────────────────────────┤
│  Docker Containers                                              │
│  ├─ api (FastAPI)                                               │
│  │  └─ /api/v1/monitoring/* (literature sources/alerts)         │
│  ├─ postgres (literature metadata)                              │
│  ├─ redis (cache + broker)                                      │
│  ├─ celery-worker (paper ingestion tasks)                       │
│  └─ celery-beat (scheduled syncs)                               │
├─────────────────────────────────────────────────────────────────┤
│  Phase 2 Systems (STANDALONE - Not Integrated)                  │
│  ├─ src/services/rag/performance_tracker.py                     │
│  ├─ src/services/rag/cost_optimizer.py                          │
│  ├─ src/services/rag/ab_testing.py                              │
│  ├─ src/services/rag/evaluation_dataset.py                      │
│  ├─ src/services/rag/ragas_evaluator.py                         │
│  └─ src/services/rag/metrics_exporter.py                        │
└─────────────────────────────────────────────────────────────────┘

Target Architecture (After Integration)

┌─────────────────────────────────────────────────────────────────┐
│                    Connectome Server (Enhanced)                 │
├─────────────────────────────────────────────────────────────────┤
│  Docker Containers (Existing + New)                             │
│  ├─ api (FastAPI) ✨                                            │
│  │  ├─ /api/v1/monitoring/* (literature)                        │
│  │  └─ /api/v1/rag-evaluation/* (NEW - Phase 2 endpoints)       │
│  ├─ postgres (extended schema) ✨                               │
│  ├─ redis (cache + broker)                                      │
│  ├─ celery-worker (extended tasks) ✨                           │
│  ├─ celery-beat (extended schedule) ✨                          │
│  ├─ prometheus (NEW - metrics collection) 🆕                    │
│  └─ grafana (NEW - visualization) 🆕                            │
├─────────────────────────────────────────────────────────────────┤
│  Phase 2 Integration Layer (NEW)                                │
│  ├─ src/api/v1/rag_evaluation.py (REST endpoints)               │
│  ├─ src/tasks/rag_benchmark.py (Celery tasks)                   │
│  ├─ src/services/rag_manager.py (orchestration)                 │
│  └─ grafana/dashboards/* (monitoring UI)                        │
└─────────────────────────────────────────────────────────────────┘

📦 Integration Components

1. REST API Endpoints (`src/api/v1/rag_evaluation.py`)

NEW Endpoints:

# Performance Tracking
POST   /api/v1/rag-evaluation/performance/track
GET    /api/v1/rag-evaluation/performance/metrics
POST   /api/v1/rag-evaluation/performance/reset

# Cost Optimization
POST   /api/v1/rag-evaluation/cost/budget/create
GET    /api/v1/rag-evaluation/cost/budget/{budget_id}
POST   /api/v1/rag-evaluation/cost/optimize
GET    /api/v1/rag-evaluation/cost/suggestions

# A/B Testing
POST   /api/v1/rag-evaluation/ab-test/create
POST   /api/v1/rag-evaluation/ab-test/{test_id}/add-result
GET    /api/v1/rag-evaluation/ab-test/{test_id}/analyze
GET    /api/v1/rag-evaluation/ab-test/{test_id}/winner

# RAGAS Evaluation
POST   /api/v1/rag-evaluation/ragas/evaluate
GET    /api/v1/rag-evaluation/ragas/baseline/{dataset_id}
GET    /api/v1/rag-evaluation/ragas/metrics

# Prometheus Metrics Export
GET    /metrics  # Standard Prometheus endpoint

Integration Points:

Uses existing FastAPI app (src/main.py)
Shares database connection pool
Uses Redis for caching evaluation results
Authentication via existing JWT middleware

2. Database Schema Extensions

NEW Tables:

-- RAG Evaluation Results
CREATE TABLE rag_evaluations (
    id UUID PRIMARY KEY,
    dataset_id UUID,
    evaluation_type VARCHAR(50),  -- 'ragas', 'baseline', 'ab_test'
    metrics JSONB,
    metadata JSONB,
    created_at TIMESTAMP DEFAULT NOW()
);

-- Performance Metrics History
CREATE TABLE rag_performance_metrics (
    id UUID PRIMARY KEY,
    operation VARCHAR(100),
    latency FLOAT,
    token_usage JSONB,
    cost FLOAT,
    timestamp TIMESTAMP DEFAULT NOW()
);

-- Cost Budgets
CREATE TABLE rag_cost_budgets (
    id UUID PRIMARY KEY,
    name VARCHAR(200),
    total_budget FLOAT,
    spent FLOAT DEFAULT 0.0,
    warning_threshold FLOAT DEFAULT 0.8,
    critical_threshold FLOAT DEFAULT 0.95,
    expenses JSONB,
    created_at TIMESTAMP DEFAULT NOW()
);

-- A/B Test Configurations
CREATE TABLE rag_ab_tests (
    id UUID PRIMARY KEY,
    name VARCHAR(200),
    config JSONB,  -- variants, traffic_split
    status VARCHAR(20),  -- 'active', 'completed', 'cancelled'
    created_at TIMESTAMP DEFAULT NOW()
);

-- A/B Test Results
CREATE TABLE rag_ab_test_results (
    id UUID PRIMARY KEY,
    test_id UUID REFERENCES rag_ab_tests(id),
    variant_name VARCHAR(100),
    metrics JSONB,
    cost FLOAT,
    timestamp TIMESTAMP DEFAULT NOW()
);

Migration:

alembic revision --autogenerate -m "Add Phase 2 RAG evaluation tables"
alembic upgrade head

3. Celery Background Tasks (`src/tasks/rag_benchmark.py`)

NEW Tasks:

from celery import shared_task

@shared_task(name="rag.daily_benchmark")
def run_daily_rag_benchmark():
    """Run comprehensive RAG evaluation daily."""
    # 1. Generate test dataset
    # 2. Run RAGAS evaluation
    # 3. Track performance metrics
    # 4. Calculate costs
    # 5. Export to Prometheus
    # 6. Send alerts if quality degraded

@shared_task(name="rag.hourly_performance_snapshot")
def capture_performance_snapshot():
    """Capture RAG performance metrics hourly."""
    # 1. Get current latency statistics
    # 2. Calculate token usage
    # 3. Export to Prometheus
    # 4. Store in database

@shared_task(name="rag.weekly_cost_analysis")
def analyze_weekly_costs():
    """Weekly cost analysis and optimization suggestions."""
    # 1. Aggregate week's costs
    # 2. Generate optimization suggestions
    # 3. Check budget alerts
    # 4. Send report email

@shared_task(name="rag.ab_test_evaluation")
def evaluate_ab_test(test_id: str):
    """Evaluate A/B test and declare winner if ready."""
    # 1. Get test configuration
    # 2. Analyze results
    # 3. Calculate statistical significance
    # 4. Declare winner if confidence > 95%

Celery Beat Schedule:

# src/core/celery_config.py
CELERY_BEAT_SCHEDULE = {
    'daily-rag-benchmark': {
        'task': 'rag.daily_benchmark',
        'schedule': crontab(hour=2, minute=0),  # 2 AM daily
    },
    'hourly-performance-snapshot': {
        'task': 'rag.hourly_performance_snapshot',
        'schedule': crontab(minute=0),  # Every hour
    },
    'weekly-cost-analysis': {
        'task': 'rag.weekly_cost_analysis',
        'schedule': crontab(day_of_week=1, hour=9, minute=0),  # Monday 9 AM
    },
}

4. Docker Compose Extensions

NEW Services:

# docker-compose.yml additions

  # Prometheus Metrics Collection
  prometheus:
    image: prom/prometheus:latest
    container_name: ai-coscientist-prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--web.console.libraries=/usr/share/prometheus/console_libraries'
      - '--web.console.templates=/usr/share/prometheus/consoles'
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
    networks:
      - coscientist-network
    restart: unless-stopped

  # Grafana Visualization
  grafana:
    image: grafana/grafana:latest
    container_name: ai-coscientist-grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_USER=admin
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD:-admin}
      - GF_INSTALL_PLUGINS=
    volumes:
      - grafana_data:/var/lib/grafana
      - ./grafana/provisioning:/etc/grafana/provisioning
      - ./grafana/dashboards:/var/lib/grafana/dashboards
    networks:
      - coscientist-network
    restart: unless-stopped
    depends_on:
      - prometheus

volumes:
  prometheus_data:
  grafana_data:

Prometheus Configuration (prometheus/prometheus.yml):

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'ai-coscientist-api'
    static_configs:
      - targets: ['api:8000']
    metrics_path: '/metrics'

5. Grafana Dashboard Configuration

Dashboard JSON (grafana/dashboards/rag_comprehensive_dashboard.json):

{
  "dashboard": {
    "title": "AI-CoScientist RAG Comprehensive Dashboard",
    "panels": [
      {
        "id": 1,
        "title": "RAGAS Metrics - Faithfulness",
        "type": "timeseries",
        "targets": [
          {"expr": "rag_faithfulness{env=\"production\"}"}
        ]
      },
      {
        "id": 2,
        "title": "Performance - Latency (p95)",
        "type": "timeseries",
        "targets": [
          {"expr": "histogram_quantile(0.95, rate(rag_latency_bucket[5m]))"}
        ]
      },
      {
        "id": 3,
        "title": "Cost - Daily Spending",
        "type": "graph",
        "targets": [
          {"expr": "sum(rate(rag_cost_total[1d]))"}
        ]
      },
      {
        "id": 4,
        "title": "A/B Tests - Active Experiments",
        "type": "stat",
        "targets": [
          {"expr": "count(rag_ab_test_status{status=\"active\"})"}
        ]
      }
    ]
  }
}

Provisioning (grafana/provisioning/dashboards/dashboards.yml):

apiVersion: 1

providers:
  - name: 'AI-CoScientist RAG'
    orgId: 1
    folder: 'RAG Evaluation'
    type: file
    disableDeletion: false
    updateIntervalSeconds: 10
    allowUiUpdates: true
    options:
      path: /var/lib/grafana/dashboards
      foldersFromFilesStructure: true

🚀 Implementation Roadmap

Phase 2.1: API Integration (Week 1)

Day 1-2: REST API Endpoints

# Create new API router
touch src/api/v1/rag_evaluation.py

# Implement endpoints:
# - Performance tracking endpoints
# - Cost optimization endpoints
# - A/B testing endpoints
# - RAGAS evaluation endpoints

# Add to main router
# src/api/v1/__init__.py: include rag_evaluation_router

Day 3-4: Database Schema

# Create migration
alembic revision --autogenerate -m "Add Phase 2 RAG tables"

# Review and adjust migration
vim alembic/versions/[hash]_add_phase_2_rag_tables.py

# Apply migration
alembic upgrade head

# Verify schema
psql -U postgres -d ai_coscientist -c "\dt"

Day 5: Testing

# Write API endpoint tests
pytest tests/api/test_rag_evaluation.py -v

# Integration tests
pytest tests/integration/test_rag_phase2.py -v

Phase 2.2: Monitoring Stack (Week 2)

Day 1-2: Prometheus Setup

# Create Prometheus config
mkdir -p prometheus
vim prometheus/prometheus.yml

# Add Prometheus to docker-compose
vim docker-compose.yml

# Test locally
docker-compose up -d prometheus
curl http://localhost:9090/-/healthy

Day 3-4: Grafana Setup

# Create Grafana provisioning
mkdir -p grafana/{provisioning,dashboards}

# Import Phase 1 dashboard (already exists)
cp grafana/dashboards/rag_evaluation_dashboard.json \
   grafana/dashboards/rag_phase1_metrics.json

# Create comprehensive dashboard
vim grafana/dashboards/rag_comprehensive_dashboard.json

# Add Grafana to docker-compose
vim docker-compose.yml

# Test locally
docker-compose up -d grafana
open http://localhost:3000

Day 5: Integration Testing

# Start full stack
docker-compose up -d

# Verify Prometheus scraping
curl http://localhost:9090/api/v1/targets

# Verify Grafana data source
curl -u admin:admin http://localhost:3000/api/datasources

# Run end-to-end test
pytest tests/e2e/test_monitoring_stack.py -v

Phase 2.3: Background Tasks (Week 3)

Day 1-2: Celery Tasks

# Create task module
touch src/tasks/rag_benchmark.py

# Implement tasks:
# - run_daily_rag_benchmark()
# - capture_performance_snapshot()
# - analyze_weekly_costs()
# - evaluate_ab_test()

Day 3: Celery Beat Schedule

# Update Celery config
vim src/core/celery_config.py

# Add beat schedule for RAG tasks
# Test schedule
celery -A src.core.celery_app inspect scheduled

Day 4-5: Testing

# Test individual tasks
pytest tests/tasks/test_rag_benchmark.py -v

# Test beat scheduling
# (requires Redis + Celery Beat running)

Phase 2.4: Deployment Updates (Week 4)

Day 1-2: Deployment Script

# Update deploy_to_connectome.sh
vim scripts/deploy_to_connectome.sh

# Add new sections:
# - setup_rag_evaluation()
# - configure_prometheus()
# - configure_grafana()
# - verify_rag_deployment()

Day 3: Documentation

# Update deployment guide
vim claudedocs/DEPLOYMENT_GUIDE.md

# Add RAG evaluation sections:
# - Environment variables
# - Grafana access
# - Prometheus endpoints
# - Troubleshooting

Day 4-5: Integration Testing

# Test deployment script locally
./scripts/deploy_to_connectome.sh

# Verify all services
docker-compose ps

# Check health endpoints
curl http://localhost:8000/api/v1/health
curl http://localhost:9090/-/healthy
curl http://localhost:3000/api/health

# Run comprehensive test suite
pytest tests/ -v --cov=src

🔧 Configuration Management

Environment Variables

NEW Variables (.env.production):

# RAG Evaluation Settings
RAG_EVALUATION_ENABLED=true
RAG_BENCHMARK_SCHEDULE="0 2 * * *"  # Daily at 2 AM
RAG_PERFORMANCE_SNAPSHOT_INTERVAL=3600  # Hourly

# Prometheus
PROMETHEUS_PORT=9090
PROMETHEUS_RETENTION_DAYS=15

# Grafana
GRAFANA_PORT=3000
GRAFANA_ADMIN_USER=admin
GRAFANA_ADMIN_PASSWORD=${GRAFANA_PASSWORD}

# Cost Budgets
RAG_MONTHLY_BUDGET=100.0
RAG_BUDGET_WARNING_THRESHOLD=0.8
RAG_BUDGET_CRITICAL_THRESHOLD=0.95

# A/B Testing
RAG_AB_TEST_MIN_CONFIDENCE=0.95
RAG_AB_TEST_MIN_SAMPLES=100

Feature Flags

# src/core/config.py
class Settings(BaseSettings):
    # ... existing settings ...

    # Phase 2 RAG Evaluation
    rag_evaluation_enabled: bool = True
    rag_benchmark_schedule: str = "0 2 * * *"
    rag_performance_snapshot_interval: int = 3600

    # Prometheus
    prometheus_enabled: bool = True
    prometheus_port: int = 9090

    # Grafana
    grafana_enabled: bool = True
    grafana_port: int = 3000

    # Cost Management
    rag_monthly_budget: float = 100.0
    rag_budget_warning_threshold: float = 0.8

📊 Monitoring & Observability

Health Checks

Extended Health Endpoint (/api/v1/health):

@router.get("/health/detailed")
async def detailed_health():
    return {
        "status": "healthy",
        "components": {
            "database": await check_database(),
            "redis": await check_redis(),
            "prometheus": await check_prometheus(),
            "rag_evaluation": await check_rag_evaluation(),
            "celery_workers": await check_celery_workers()
        },
        "metrics": {
            "rag_evaluations_24h": await count_evaluations_24h(),
            "active_ab_tests": await count_active_ab_tests(),
            "current_budget_usage": await get_budget_usage()
        }
    }

Alerting Rules

Prometheus Alerts (prometheus/alerts.yml):

groups:
  - name: rag_quality_alerts
    interval: 5m
    rules:
      - alert: RAGFaithfulnessLow
        expr: rag_faithfulness{env="production"} < 0.7
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "RAG faithfulness score is low"
          description: "Faithfulness score {{ $value }} is below threshold (0.7)"

      - alert: RAGLatencyHigh
        expr: histogram_quantile(0.95, rate(rag_latency_bucket[5m])) > 5
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "RAG latency is high"
          description: "p95 latency {{ $value }}s exceeds threshold (5s)"

      - alert: RAGBudgetCritical
        expr: rag_budget_usage_ratio > 0.95
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "RAG budget critically low"
          description: "Budget usage {{ $value }}% exceeds critical threshold (95%)"

Logging Strategy

# src/services/rag_manager.py
import logging
import structlog

logger = structlog.get_logger(__name__)

async def run_rag_evaluation(dataset_id: str):
    logger.info(
        "rag_evaluation_started",
        dataset_id=dataset_id,
        evaluation_type="ragas"
    )

    try:
        results = await evaluator.run_evaluation(dataset_id)

        logger.info(
            "rag_evaluation_completed",
            dataset_id=dataset_id,
            metrics=results['metrics'],
            duration_seconds=results['duration']
        )

        return results

    except Exception as e:
        logger.error(
            "rag_evaluation_failed",
            dataset_id=dataset_id,
            error=str(e),
            exc_info=True
        )
        raise

🧪 Testing Strategy

Unit Tests

# tests/api/test_rag_evaluation.py
async def test_performance_tracking_endpoint():
    """Test POST /api/v1/rag-evaluation/performance/track"""
    response = await client.post(
        "/api/v1/rag-evaluation/performance/track",
        json={
            "operation": "retrieval",
            "latency": 0.5,
            "tokens": {"prompt": 100, "completion": 50},
            "model": "gpt-4"
        }
    )
    assert response.status_code == 200
    assert "metric_id" in response.json()

# tests/tasks/test_rag_benchmark.py
async def test_daily_benchmark_task():
    """Test daily RAG benchmark Celery task"""
    result = run_daily_rag_benchmark.delay()
    assert result.get(timeout=60)  # Wait max 60s

    # Verify results stored in database
    evaluations = await get_recent_evaluations(hours=1)
    assert len(evaluations) > 0

Integration Tests

# tests/integration/test_monitoring_stack.py
async def test_prometheus_metrics_export():
    """Test that Prometheus can scrape metrics from API"""
    # Trigger some RAG operations
    await run_rag_evaluation(test_dataset_id)

    # Wait for metrics export
    await asyncio.sleep(2)

    # Query Prometheus
    response = requests.get(
        "http://localhost:9090/api/v1/query",
        params={"query": "rag_faithfulness"}
    )

    assert response.status_code == 200
    data = response.json()
    assert len(data['data']['result']) > 0

async def test_grafana_dashboard_data():
    """Test that Grafana can display RAG metrics"""
    # Query Grafana API
    response = requests.get(
        "http://localhost:3000/api/datasources/proxy/1/api/v1/query",
        params={"query": "rag_faithfulness"},
        auth=("admin", "admin")
    )

    assert response.status_code == 200
    assert "data" in response.json()

E2E Tests

# tests/e2e/test_full_rag_workflow.py
async def test_complete_rag_evaluation_workflow():
    """
    End-to-end test of complete RAG evaluation workflow:
    1. Upload test data
    2. Run evaluation
    3. Track performance
    4. Calculate costs
    5. Export metrics
    6. Verify dashboard
    """
    # 1. Upload test dataset
    dataset = await upload_test_dataset()

    # 2. Run RAGAS evaluation
    evaluation = await run_evaluation(dataset.id)
    assert evaluation.metrics['faithfulness'] > 0.7

    # 3. Verify performance tracked
    metrics = await get_performance_metrics(operation="evaluation")
    assert metrics['count'] > 0

    # 4. Verify cost calculated
    cost = await get_evaluation_cost(evaluation.id)
    assert cost > 0

    # 5. Verify Prometheus metrics
    prom_metrics = await query_prometheus("rag_faithfulness")
    assert len(prom_metrics) > 0

    # 6. Verify Grafana dashboard
    dashboard = await get_grafana_dashboard("rag_comprehensive_dashboard")
    assert dashboard['meta']['slug'] == "rag_comprehensive_dashboard"

🚨 Risk Analysis & Mitigation

Technical Risks

Risk	Impact	Probability	Mitigation
Prometheus data loss	High	Low	Regular backups, retention policies
Grafana configuration drift	Medium	Medium	Version-controlled dashboards, provisioning
Celery task failures	High	Medium	Retry policies, dead letter queue, monitoring
Database migration issues	High	Low	Test migrations on staging, backup before production
API performance degradation	Medium	Medium	Load testing, caching, async operations

Operational Risks

Risk	Impact	Probability	Mitigation
Deployment downtime	High	Low	Blue-green deployment, health checks
Monitoring stack overhead	Medium	Medium	Resource limits, selective metrics
Cost budget exceeded	Medium	Medium	Alerts at 80%, automatic throttling
A/B test data loss	Medium	Low	Database backups, test snapshots

Mitigation Strategies

1. Gradual Rollout:

# Deploy to staging first
./scripts/deploy_to_connectome.sh --env staging

# Monitor for 48 hours
watch -n 60 'curl -s http://staging:8000/api/v1/health/detailed | jq .status'

# Deploy to production with canary
./scripts/deploy_to_connectome.sh --env production --canary

2. Rollback Plan:

# Tag current production state
git tag -a v2.0-pre-integration -m "Before Phase 2 integration"

# If issues detected, rollback
docker-compose down
git checkout v2.0-pre-integration
docker-compose up -d
alembic downgrade -1  # Rollback migration

3. Monitoring Safeguards:

# Resource limits in docker-compose.yml
prometheus:
  deploy:
    resources:
      limits:
        cpus: '0.5'
        memory: 512M
      reservations:
        cpus: '0.25'
        memory: 256M

grafana:
  deploy:
    resources:
      limits:
        cpus: '0.5'
        memory: 512M

📈 Success Metrics

Deployment Success Criteria

✅ All services healthy after deployment
✅ Zero errors in logs for first 24 hours
✅ Prometheus successfully scraping metrics
✅ Grafana dashboards displaying data
✅ Celery tasks executing on schedule
✅ API response times < 500ms p95
✅ Literature monitoring unaffected (existing system continues)

Performance Benchmarks

API Latency:

/api/v1/rag-evaluation/* endpoints: p95 < 500ms
Prometheus /metrics endpoint: p95 < 100ms
Database queries: p95 < 50ms

Resource Usage:

Prometheus memory: < 512 MB
Grafana memory: < 512 MB
API container CPU: < 50% (with Phase 2 active)

Quality Metrics:

RAGAS evaluation time: < 60 seconds per dataset
A/B test analysis time: < 5 seconds
Cost calculation time: < 1 second

Business Metrics

Operational Efficiency:

Automated RAG benchmarks: 1x daily
Performance snapshots: 1x hourly
Cost analysis: 1x weekly
Alert response time: < 5 minutes

Cost Savings:

Budget alerts prevent overruns: >90% effectiveness
Optimization suggestions implemented: >50%
A/B tests inform model selection: measurable cost reduction

📝 Documentation Deliverables

Technical Documentation

API Documentation (docs/API_RAG_EVALUATION.md)
- Complete endpoint reference
- Request/response schemas
- Authentication requirements
- Example cURL commands
Deployment Guide (claudedocs/DEPLOYMENT_GUIDE_PHASE2.md)
- Step-by-step deployment instructions
- Environment variable configuration
- Docker compose updates
- Migration procedures
Operations Manual (claudedocs/OPERATIONS_RAG_EVALUATION.md)
- Monitoring procedures
- Alert response playbooks
- Backup and recovery
- Troubleshooting guide
Developer Guide (claudedocs/DEVELOPER_GUIDE_PHASE2.md)
- Local development setup
- Testing procedures
- Code contribution guidelines
- Architecture decisions

User Documentation

Grafana Dashboard Guide (grafana/README_DASHBOARDS.md)
- Dashboard navigation
- Metric interpretations
- Alert configurations
- Custom queries
Cost Management Guide (docs/COST_MANAGEMENT.md)
- Budget setup
- Optimization workflows
- Report interpretation
- Cost reduction strategies
A/B Testing Guide (docs/AB_TESTING_GUIDE.md)
- Test setup
- Result interpretation
- Statistical significance
- Best practices

🎯 Next Steps

Immediate Actions (This Week)

Review & Approve Plan - Stakeholder sign-off on integration approach
Environment Setup - Prepare staging environment for testing
Resource Allocation - Assign developers to implementation tasks
Timeline Confirmation - Finalize 4-week implementation schedule

Phase 2.1 Kickoff (Week 1)

API Development - Begin REST endpoint implementation
Database Migration - Create and test schema extensions
Code Review - Establish review process for Phase 2 code
CI/CD Updates - Configure automated testing for new endpoints

Ongoing Communication

Daily Standups - Progress tracking and blocker resolution
Weekly Demos - Stakeholder demonstrations of completed features
Bi-weekly Retrospectives - Process improvement and lessons learned
Milestone Reports - End-of-phase summaries and metrics

📞 Support & Contact

Technical Lead: [Name] DevOps Engineer: [Name] Product Owner: [Name]

Communication Channels:

Slack: #ai-coscientist-phase2
Email: team@transconnectome.org
GitHub Issues: https://github.com/Transconnectome/AI-CoScientist/issues

Emergency Contact: [On-call rotation]

📚 References

Internal Documentation

Phase 1 Implementation: claudedocs/PHASE1_COMPLETE.md
Phase 2 Development: claudedocs/PHASE2_COMPLETE.md
Existing Deployment: scripts/deploy_to_connectome.sh
Monitoring Strategy: claudedocs/MONITORING_STRATEGY.md

External Resources

Prometheus Documentation: https://prometheus.io/docs/
Grafana Provisioning: https://grafana.com/docs/grafana/latest/administration/provisioning/
FastAPI Best Practices: https://fastapi.tiangolo.com/
Celery Documentation: https://docs.celeryq.dev/

Document Status: ✅ Ready for Review Next Review Date: 2025-10-29 Version History:

v1.0 (2025-10-22): Initial integration plan created

FilesExpand file tree

PHASE2_INTEGRATION_PLAN.md

Latest commit

History