# PATAS API Quickstart

**Quick start in 5 minutes**

---

## Base URL

```
http://localhost:8000/api/v1
```

For production deployments, replace with your PATAS server URL.

---

## Main Endpoint: `/api/v1/analyze`

The `/api/v1/analyze` endpoint bundles the complete workflow:
1. Ingest messages
2. Run pattern mining (optional)
3. Evaluate rules (optional)
4. Export rules (optional)

**Perfect for**: Small-medium batch analysis, RapidAPI integration, quick prototyping.

### Basic Example

**Request:**
```bash
POST /api/v1/analyze
Content-Type: application/json

{
  "messages": [
    {
      "id": "msg_001",
      "text": "Buy now! http://spam.com",
      "is_spam": true
    }
  ],
  "run_mining": true
}
```

**Response:**
```json
{
  "patterns": [
    {
      "id": 1,
      "type": "url",
      "description": "URL pattern: http://spam.com",
      "group_size": 2,
      "similarity_reason": "Messages contain the same suspicious URL: http://spam.com",
      "sql_query": "SELECT id FROM reports WHERE message_content LIKE '%http://spam.com%'"
    }
  ],
  "rules": [...],
  "meta": {
    "ingested_count": 1,
    "patterns_created": 1,
    "timings": {...}
  }
}
```

---

## Code Examples

### Python

```python
import requests

response = requests.post(
    'http://localhost:8000/api/v1/analyze',
    json={
        'messages': [
            {
                'id': 'msg_001',
                'text': 'Buy now! http://spam.com',
                'is_spam': True
            }
        ],
        'run_mining': True,
        'run_evaluation': True,
        'export_backend': 'sql'
    }
)

result = response.json()
print(f"Discovered {len(result['patterns'])} patterns")

# Access SQL queries
for pattern in result['patterns']:
    print(f"SQL: {pattern['sql_query']}")
```

### cURL

```bash
curl -X POST http://localhost:8000/api/v1/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"id": "1", "text": "Buy now! http://spam.com", "is_spam": true}
    ],
    "run_mining": true
  }'
```

### JavaScript

```javascript
const response = await fetch('http://localhost:8000/api/v1/analyze', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    messages: [
      { id: '1', text: 'Buy now! http://spam.com', is_spam: true }
    ],
    run_mining: true
  })
});

const result = await response.json();
console.log(`Discovered ${result.patterns.length} patterns`);
```

---

## Pattern Groups and SQL Output

Each pattern in the response includes:

- **`group_size`**: Number of messages in this pattern group
- **`sources_count`**: Number of unique sources
- **`senders_count`**: Number of unique senders
- **`similarity_reason`**: Explanation of why messages are similar
- **`sql_query`**: SQL query for blocking

**Example SQL query:**
```sql
SELECT id, message_content, sender, source 
FROM reports 
WHERE LOWER(message_content) LIKE '%http://spam.com%'
```

This query can be used to:
- Identify all messages matching the pattern
- Apply blocking rules in your system
- Generate reports

---

## Lower-Level Endpoints

For large datasets, use lower-level endpoints:

1. **Ingest**: `POST /api/v1/messages/ingest`
2. **Mine**: `POST /api/v1/patterns/mine`
3. **Evaluate**: `POST /api/v1/rules/eval-shadow`
4. **Promote**: `POST /api/v1/rules/promote`
5. **Export**: `GET /api/v1/rules/export`

See [API Reference](API_REFERENCE_FULL) for details.

---

## Error Handling

```python
try:
    response = requests.post('http://localhost:8000/api/v1/analyze', json=data)
    response.raise_for_status()
    result = response.json()
except requests.exceptions.HTTPError as e:
    error_detail = e.response.json().get("detail", "Unknown error")
    print(f"Error: {error_detail}")
```

---

## Next Steps

- [API Reference](API_REFERENCE_FULL) — complete documentation of all endpoints
- [Examples](EXAMPLES) — practical examples
- [Integration Guide](INTEGRATION_GUIDE) — integration guide