# VertiGuard Implementation Summary

## 🎉 What Was Built

A comprehensive **LLM observability framework** that goes beyond traditional monitoring to provide **semantic-level insights** into AI agent behavior.

---

## 📦 Components Implemented

### 1. **Core Framework** (`src/vertiguard/`)

#### Client & Initialization
- **`client.py`**: Main VertiGuard client with decorator methods
- **`__init__.py`**: Clean public API exports
- Singleton pattern with module-level decorators

#### Decorators (`decorators/`)
- **`trace.py`**: Wrapper decorators for functions
- Supports: `@trace`, `@workflow`, `@llm`, `@task`, `@agent`
- Automatic input/output capture
- Async and sync function support

#### Configuration (`config/`)
- **`schema.py`**: Pydantic models for type-safe config
- **`loader.py`**: YAML loading with env variable expansion
- **`defaults.py`**: Default configurations
- Validates on load, fail-fast on errors

#### Evaluation (`evaluation/`)
- **`engine.py`**: Multi-phase evaluation pipeline
  - Phase 1: Rule-based checks (fast)
  - Phase 2: Security scans
  - Phase 3: LLM-based semantic evaluation
- **`gemini_judge.py`**: Gemini-powered behavior evaluation
- **`rules.py`**: Fast rule-based validators
- **`classifiers.py`**: Failure type classification
- **`prompts.py`**: Evaluation prompt templates

#### Security (`security/`)
- **`scanners.py`**: Detection engines
  - PII scanner (email, phone, SSN, credit cards)
  - Prompt injection detector
  - Sensitive content scanner
- **`signals.py`**: Security signal aggregation

#### Telemetry (`telemetry/`)
- **`datadog_client.py`**: Unified Datadog API client
  - Metrics submission (gauge, count, distribution)
  - Event creation
  - Monitor management
  - Dashboard creation
  - Incident management
- **`llmobs_bridge.py`**: LLM Observability integration
- **`metrics.py`**: Metric utilities
- **`events.py`**: Event formatting
- **`traces.py`**: Trace utilities
- **`logs.py`**: Structured logging

#### Detection (`detection/`)
- **`monitors.py`**: Monitor creation and management
- **`rules.py`**: Detection rule definitions
- **`templates.py`**: Monitor templates
  - Adherence score monitors
  - Latency monitors
  - Security monitors
  - Token usage monitors

#### Actions (`actions/`)
- **`incidents.py`**: Automatic incident creation
  - Severity-based escalation (SEV-1 to SEV-4)
  - Context-rich incident data
- **`cases.py`**: Case management system
- **`notifications.py`**: Multi-channel alerting
  - Slack webhooks
  - PagerDuty integration
  - Custom webhooks
- **`alerts.py`**: Alert handling

#### Dashboard (`dashboard/`)
- **`builder.py`**: Widget builder utilities
  - Query value widgets
  - Timeseries graphs
  - Heatmaps
  - Event streams
  - Monitor summaries
- **`templates.py`**: Pre-built dashboard templates

#### Optimization (`optimization/`) **NEW**
- **`dspy_optimizer.py`**: DSPy-based prompt optimization
  - Analyzes failing prompts
  - Generates improved versions
  - Few-shot example generation
  - Failure pattern analysis
- **`root_cause.py`**: LLM-powered root cause analysis
  - Analyzes errors with full context
  - Suggests specific fixes
  - Identifies files to check
  - Provides debug steps

#### Utilities (`utils/`)
- **`retry.py`**: Retry logic with exponential backoff
- **`serialization.py`**: JSON utilities

---

### 2. **Example Application** (`examples/legal_analyzer/`)

#### Legal Document Analyzer
- **`app.py`**: Complete working example
  - Entity extraction from legal documents
  - Document summarization
  - Question answering with citations
  - Full VertiGuard integration
- **`vertiguard.yaml`**: Production-ready configuration
  - 3 traced nodes
  - Expected/unexpected behaviors defined
  - Security checks configured
  - Custom alerts defined

---

### 3. **Scripts** (`scripts/`)

#### Traffic Generator **NEW**
- **`traffic_generator.py`**: Comprehensive traffic generation
  - 60% normal requests (should pass)
  - 15% semantic violations (hallucinations)
  - 10% PII exposure attempts
  - 10% format violations
  - 5% high latency scenarios
  - Real-time statistics
  - Configurable request count and delay

#### Datadog Exporter **NEW**
- **`export_datadog_configs.py`**: Configuration export
  - Exports all monitors as JSON
  - Exports dashboards with full definitions
  - Exports SLOs (if configured)
  - Creates summary file
  - Generates README for imports

#### E2E Tests **NEW**
- **`test_e2e.py`**: Comprehensive test suite
  - Tests initialization
  - Tests decorators
  - Tests evaluation
  - Tests Datadog integration
  - Tests monitors and dashboards
  - Tests incident creation
  - Tests root cause analysis

---

### 4. **Tests** (`tests/`)

- **`test_config.py`**: Configuration loading tests
- **`test_evaluation.py`**: Evaluation engine tests
- **`test_security.py`**: Security scanner tests
- **`test_telemetry.py`**: Datadog client tests
- **`test_actions.py`**: Incident/alert tests
- **`test_utils.py`**: Utility function tests
- **`conftest.py`**: Pytest fixtures

---

### 5. **Deployment** **NEW**

#### Docker
- **`Dockerfile`**: Multi-stage production build
- **`docker-compose.yml`**: Multi-service orchestration
- **`.dockerignore`**: Build optimization

#### Cloud Platforms
- **`railway.json`**: Railway configuration
- **`render.yaml`**: Render deployment config

#### Documentation
- **`DEPLOYMENT.md`**: Complete deployment guide
- **`SUBMISSION_CHECKLIST.md`**: Challenge submission guide
- **`LICENSE`**: MIT License

---

## 🚀 Key Features

### Semantic Monitoring
- **Behavior-based evaluation**: Define expected/unexpected behaviors
- **Gemini-powered analysis**: Uses LLM to understand semantic drift
- **Adherence scoring**: 0-1 score for output quality

### Multi-Layer Defense
1. **Rule-based checks** (fast, deterministic)
2. **Security scanning** (PII, injection, sensitive data)
3. **LLM evaluation** (semantic analysis)

### Intelligent Optimization
- **DSPy integration**: Automatic prompt improvement
- **Root cause analysis**: LLM analyzes errors and suggests fixes
- **Failure pattern detection**: Identifies recurring issues

### Drop-in Integration
```python
import vertiguard

vg = vertiguard.init("vertiguard.yaml")

@vg.trace("extract_entities")
async def extract_entities(doc):
    return await llm.complete(prompt)
```

### Context-Rich Incidents
Every alert includes:
- Full trace context
- Input/output data
- Failed behavior checks
- Security issues
- Suggested remediation

---

## 📊 Datadog Integration

### LLM Observability
- Automatic trace submission via ddtrace
- Input/output annotation
- Token usage tracking
- Cost metrics

### Custom Metrics
- `vertiguard.node.adherence_score`
- `vertiguard.node.flagged`
- `vertiguard.node.latency_ms`
- `vertiguard.node.calls`
- `vertiguard.security.pii_detected`
- `vertiguard.eval.tokens_used`

### Monitors (8 default)
1. Adherence Warning (< 0.85)
2. Adherence Critical (< 0.70)
3. Flag Rate (> 10%)
4. Latency Warning (> 3000ms)
5. Latency Critical (> 10000ms)
6. Error Rate (> 5%)
7. Security Issues
8. Token Usage Warning

### Dashboard Widgets
- Health overview (adherence score, flag rate)
- Adherence trend over time
- Flags by category
- Call volume
- Latency distribution
- Monitor status
- Actionable items

---

## 🎯 Innovation Highlights

### 1. Semantic-First Monitoring
Goes beyond latency/errors to monitor **meaning**. Detects when LLM outputs semantically drift from expectations.

### 2. Configurable Behaviors
Define expected/unexpected behaviors in YAML:
```yaml
nodes:
  extract_entities:
    expected_behaviors:
      - "Must return valid JSON"
      - "Party names must be from document"
    unexpected_behaviors:
      - "Hallucinated entities"
      - "Fabricated dates"
```

### 3. Intelligent Remediation
- **DSPy optimizer** suggests improved prompts
- **Root cause analyzer** explains errors and suggests fixes
- **Failure classifier** categorizes issues

### 4. Library Architecture
Not a standalone tool - integrates into **any** LLM application:
- FastAPI apps
- Background workers
- CLI tools
- Jupyter notebooks

### 5. Multi-Channel Alerting
- Slack for team notifications
- PagerDuty for on-call escalation
- Datadog Incidents for tracking
- Custom webhooks for integrations

---

## 📈 Metrics & Observability

### What We Track
- **Adherence scores**: How well outputs match expectations
- **Flag rate**: Percentage of outputs flagged
- **Security issues**: PII exposure, injection attempts
- **Latency**: P50, P95, P99 response times
- **Token usage**: Cost tracking
- **Error rate**: Application-level errors

### What We Alert On
- Low adherence scores (quality degradation)
- High flag rates (systematic issues)
- Security violations (critical)
- High latency (performance)
- Token budget exceeded (cost)

---

## 🔧 How to Use

### 1. Install
```bash
pip install -e .
# Or with optimization features:
pip install -e ".[optimization]"
```

### 2. Configure
Create `vertiguard.yaml`:
```yaml
app_name: my-llm-app
datadog:
  api_key: ${DD_API_KEY}
  app_key: ${DD_APP_KEY}
nodes:
  my_node:
    expected_behaviors:
      - "Returns valid JSON"
    unexpected_behaviors:
      - "Hallucinations"
```

### 3. Integrate
```python
import vertiguard

vg = vertiguard.init("vertiguard.yaml")

@vg.trace("my_node")
async def my_llm_call(input_text):
    return await llm.complete(prompt)
```

### 4. Monitor
- View dashboard in Datadog
- Receive alerts when issues occur
- Investigate with full trace context

---

## 🎬 Next Steps for Challenge

1. **Deploy application** (Railway/Render)
2. **Generate traffic** (`python scripts/traffic_generator.py`)
3. **Export configs** (`python scripts/export_datadog_configs.py`)
4. **Capture screenshots** (dashboard, incidents)
5. **Record video** (3-minute walkthrough)
6. **Submit** (repo URL + video + hosted URL)

---

## 📝 Technical Highlights

### Async-First Design
- All I/O operations are async
- Concurrent evaluation possible
- Non-blocking telemetry submission

### Type Safety
- Pydantic models for all configs
- Mypy type checking
- Runtime validation

### Error Resilience
- Retry logic with exponential backoff
- Graceful degradation (evaluation fails → continues)
- Circuit breaker patterns

### Performance
- Rule-based checks short-circuit expensive LLM calls
- Evaluation caching
- Batched metric submission

---

## 🌟 What Makes This Special

1. **First semantic firewall for LLMs**: Not just monitoring requests, monitoring **meaning**

2. **Proactive optimization**: Doesn't just alert - suggests fixes via DSPy

3. **Context everywhere**: Every alert has full context and remediation steps

4. **Drop-in library**: Single decorator, full observability

5. **Production-ready**: Error handling, retries, graceful degradation, comprehensive logging

---

**Built for the Datadog Challenge 2024** 🚀
