# Jira Webhook LLM

## Langfuse Integration

### Overview
The application integrates with Langfuse for observability and analytics of LLM usage and webhook events. This integration provides detailed tracking of:
- Webhook events
- LLM model usage
- Error tracking
- Performance metrics

### Configuration
Langfuse configuration is managed through both `application.yml` and environment variables.

#### application.yml
```yaml
langfuse:
  enabled: true
  public_key: "pk-lf-..."
  secret_key: "sk-lf-..."
  host: "https://cloud.langfuse.com"
```

#### Environment Variables
```bash
LANGFUSE_ENABLED=true
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_HOST="https://cloud.langfuse.com"
```

### Enable/Disable
To disable Langfuse integration:
1. Set `langfuse.enabled: false` in `application.yml`
2. Or set `LANGFUSE_ENABLED=false` in your environment

### Tracking Details
The following events are tracked:
1. Webhook events
   - Input payload
   - Timestamps
   - Issue metadata
2. LLM processing
   - Model used
   - Input/output
   - Processing time
3. Errors
   - Webhook processing errors
   - LLM processing errors
   - Validation errors

### Viewing Data
Visit your Langfuse dashboard to view the collected metrics and traces.

## Deployment Guide

### Redis Configuration
The application requires Redis for caching and queue management. Configure Redis in `application.yml`:

```yaml
redis:
  host: "localhost"
  port: 6379
  password: ""
  db: 0
```

Environment variables can also be used:
```bash
REDIS_HOST="localhost"
REDIS_PORT=6379
REDIS_PASSWORD=""
REDIS_DB=0
```

### Worker Process Management
The application uses Celery for background task processing. Configure workers in `application.yml`:

```yaml
celery:
  workers: 4
  concurrency: 2
  max_tasks_per_child: 100
```

Start workers with:
```bash
celery -A jira-webhook-llm worker --loglevel=info
```

### Monitoring Setup
The application provides Prometheus metrics endpoint at `/metrics`. Configure monitoring:

1. Add Prometheus scrape config:
```yaml
scrape_configs:
  - job_name: 'jira-webhook-llm'
    static_configs:
      - targets: ['localhost:8000']
```

2. Set up Grafana dashboard using the provided template

### Rate Limiting
Rate limiting is configured in `application.yml`:

```yaml
rate_limiting:
  enabled: true
  requests_per_minute: 60
  burst_limit: 100
```

### Health Check Endpoint
The application provides a health check endpoint at `/health` that returns:

```json
{
  "status": "OK",
  "timestamp": "2025-07-14T01:59:42Z",
  "components": {
    "database": "OK",
    "redis": "OK",
    "celery": "OK"
  }
}
```

### System Requirements
Minimum system requirements:

- Python 3.9+
- Redis 6.0+
- 2 CPU cores
- 4GB RAM
- 10GB disk space

Required Python packages are listed in `requirements.txt`