Release v0.7.6: The 0.7.6 Update

- Updated version to 0.7.6
- Added comprehensive demo and release notes
- Updated all documentation
- Update the veriosn in Dockerfile to 0.7.6

docs/releases_review/demo_v0.7.6.py

@@ -0,0 +1,359 @@
+#!/usr/bin/env python3
+"""
+Crawl4AI v0.7.6 Release Demo
+============================
+
+This demo showcases the major feature in v0.7.6:
+**Webhook Support for Docker Job Queue API**
+
+Features Demonstrated:
+1. Asynchronous job processing with webhook notifications
+2. Webhook support for /crawl/job endpoint
+3. Webhook support for /llm/job endpoint
+4. Notification-only vs data-in-payload modes
+5. Custom webhook headers for authentication
+6. Structured extraction with JSON schemas
+7. Exponential backoff retry for reliable delivery
+
+Prerequisites:
+- Crawl4AI Docker container running on localhost:11235
+- Flask installed: pip install flask requests
+- LLM API key configured (for LLM examples)
+
+Usage:
+python docs/releases_review/demo_v0.7.6.py
+"""
+
+import requests
+import json
+import time
+from flask import Flask, request, jsonify
+from threading import Thread
+
+# Configuration
+CRAWL4AI_BASE_URL = "http://localhost:11235"
+WEBHOOK_BASE_URL = "http://localhost:8080"
+
+# Flask app for webhook receiver
+app = Flask(__name__)
+received_webhooks = []
+
+
+@app.route('/webhook', methods=['POST'])
+def webhook_handler():
+    """Universal webhook handler for both crawl and LLM extraction jobs."""
+    payload = request.json
+    task_id = payload['task_id']
+    task_type = payload['task_type']
+    status = payload['status']
+
+    print(f"\n{'='*70}")
+    print(f"📬 Webhook Received!")
+    print(f"   Task ID: {task_id}")
+    print(f"   Task Type: {task_type}")
+    print(f"   Status: {status}")
+    print(f"   Timestamp: {payload['timestamp']}")
+
+    if status == 'completed':
+        if 'data' in payload:
+            print(f"   ✅ Data included in webhook")
+            if task_type == 'crawl':
+                results = payload['data'].get('results', [])
+                print(f"   📊 Crawled {len(results)} URL(s)")
+            elif task_type == 'llm_extraction':
+                extracted = payload['data'].get('extracted_content', {})
+                print(f"   🤖 Extracted: {json.dumps(extracted, indent=6)}")
+        else:
+            print(f"   📥 Notification only (fetch data separately)")
+    elif status == 'failed':
+        print(f"   ❌ Error: {payload.get('error', 'Unknown')}")
+
+    print(f"{'='*70}\n")
+    received_webhooks.append(payload)
+
+    return jsonify({"status": "received"}), 200
+
+
+def start_webhook_server():
+    """Start Flask webhook server in background."""
+    app.run(host='0.0.0.0', port=8080, debug=False, use_reloader=False)
+
+
+def demo_1_crawl_webhook_notification_only():
+    """Demo 1: Crawl job with webhook notification (data fetched separately)."""
+    print("\n" + "="*70)
+    print("DEMO 1: Crawl Job - Webhook Notification Only")
+    print("="*70)
+    print("Submitting crawl job with webhook notification...")
+
+    payload = {
+        "urls": ["https://example.com"],
+        "browser_config": {"headless": True},
+        "crawler_config": {"cache_mode": "bypass"},
+        "webhook_config": {
+            "webhook_url": f"{WEBHOOK_BASE_URL}/webhook",
+            "webhook_data_in_payload": False,
+            "webhook_headers": {
+                "X-Demo": "v0.7.6",
+                "X-Type": "crawl"
+            }
+        }
+    }
+
+    response = requests.post(f"{CRAWL4AI_BASE_URL}/crawl/job", json=payload)
+    if response.ok:
+        task_id = response.json()['task_id']
+        print(f"✅ Job submitted: {task_id}")
+        print("⏳ Webhook will notify when complete...")
+        return task_id
+    else:
+        print(f"❌ Failed: {response.text}")
+        return None
+
+
+def demo_2_crawl_webhook_with_data():
+    """Demo 2: Crawl job with full data in webhook payload."""
+    print("\n" + "="*70)
+    print("DEMO 2: Crawl Job - Webhook with Full Data")
+    print("="*70)
+    print("Submitting crawl job with data included in webhook...")
+
+    payload = {
+        "urls": ["https://www.python.org"],
+        "browser_config": {"headless": True},
+        "crawler_config": {"cache_mode": "bypass"},
+        "webhook_config": {
+            "webhook_url": f"{WEBHOOK_BASE_URL}/webhook",
+            "webhook_data_in_payload": True,
+            "webhook_headers": {
+                "X-Demo": "v0.7.6",
+                "X-Type": "crawl-with-data"
+            }
+        }
+    }
+
+    response = requests.post(f"{CRAWL4AI_BASE_URL}/crawl/job", json=payload)
+    if response.ok:
+        task_id = response.json()['task_id']
+        print(f"✅ Job submitted: {task_id}")
+        print("⏳ Webhook will include full results...")
+        return task_id
+    else:
+        print(f"❌ Failed: {response.text}")
+        return None
+
+
+def demo_3_llm_webhook_notification_only():
+    """Demo 3: LLM extraction with webhook notification (NEW in v0.7.6!)."""
+    print("\n" + "="*70)
+    print("DEMO 3: LLM Extraction - Webhook Notification Only (NEW!)")
+    print("="*70)
+    print("Submitting LLM extraction job with webhook notification...")
+
+    payload = {
+        "url": "https://www.example.com",
+        "q": "Extract the main heading and description from this page",
+        "provider": "openai/gpt-4o-mini",
+        "cache": False,
+        "webhook_config": {
+            "webhook_url": f"{WEBHOOK_BASE_URL}/webhook",
+            "webhook_data_in_payload": False,
+            "webhook_headers": {
+                "X-Demo": "v0.7.6",
+                "X-Type": "llm"
+            }
+        }
+    }
+
+    response = requests.post(f"{CRAWL4AI_BASE_URL}/llm/job", json=payload)
+    if response.ok:
+        task_id = response.json()['task_id']
+        print(f"✅ Job submitted: {task_id}")
+        print("⏳ Webhook will notify when LLM extraction completes...")
+        return task_id
+    else:
+        print(f"❌ Failed: {response.text}")
+        return None
+
+
+def demo_4_llm_webhook_with_schema():
+    """Demo 4: LLM extraction with JSON schema and data in webhook (NEW in v0.7.6!)."""
+    print("\n" + "="*70)
+    print("DEMO 4: LLM Extraction - Schema + Full Data in Webhook (NEW!)")
+    print("="*70)
+    print("Submitting LLM extraction with JSON schema...")
+
+    schema = {
+        "type": "object",
+        "properties": {
+            "title": {"type": "string", "description": "Page title"},
+            "description": {"type": "string", "description": "Page description"},
+            "main_topics": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "Main topics covered"
+            }
+        },
+        "required": ["title"]
+    }
+
+    payload = {
+        "url": "https://www.python.org",
+        "q": "Extract the title, description, and main topics from this website",
+        "schema": json.dumps(schema),
+        "provider": "openai/gpt-4o-mini",
+        "cache": False,
+        "webhook_config": {
+            "webhook_url": f"{WEBHOOK_BASE_URL}/webhook",
+            "webhook_data_in_payload": True,
+            "webhook_headers": {
+                "X-Demo": "v0.7.6",
+                "X-Type": "llm-with-schema"
+            }
+        }
+    }
+
+    response = requests.post(f"{CRAWL4AI_BASE_URL}/llm/job", json=payload)
+    if response.ok:
+        task_id = response.json()['task_id']
+        print(f"✅ Job submitted: {task_id}")
+        print("⏳ Webhook will include structured extraction results...")
+        return task_id
+    else:
+        print(f"❌ Failed: {response.text}")
+        return None
+
+
+def demo_5_global_webhook_config():
+    """Demo 5: Using global webhook configuration from config.yml."""
+    print("\n" + "="*70)
+    print("DEMO 5: Global Webhook Configuration")
+    print("="*70)
+    print("💡 You can configure a default webhook URL in config.yml:")
+    print("""
+    webhooks:
+      enabled: true
+      default_url: "https://myapp.com/webhooks/default"
+      data_in_payload: false
+      retry:
+        max_attempts: 5
+        initial_delay_ms: 1000
+        max_delay_ms: 32000
+        timeout_ms: 30000
+    """)
+    print("Then submit jobs WITHOUT webhook_config - they'll use the default!")
+    print("This is useful for consistent webhook handling across all jobs.")
+
+
+def demo_6_webhook_retry_logic():
+    """Demo 6: Webhook retry mechanism with exponential backoff."""
+    print("\n" + "="*70)
+    print("DEMO 6: Webhook Retry Logic")
+    print("="*70)
+    print("🔄 Webhook delivery uses exponential backoff retry:")
+    print("   • Max attempts: 5")
+    print("   • Delays: 1s → 2s → 4s → 8s → 16s")
+    print("   • Timeout: 30s per attempt")
+    print("   • Retries on: 5xx errors, network errors, timeouts")
+    print("   • No retry on: 4xx client errors")
+    print("\nThis ensures reliable webhook delivery even with temporary failures!")
+
+
+def print_summary():
+    """Print demo summary and results."""
+    print("\n" + "="*70)
+    print("📊 DEMO SUMMARY")
+    print("="*70)
+    print(f"Total webhooks received: {len(received_webhooks)}")
+
+    crawl_webhooks = [w for w in received_webhooks if w['task_type'] == 'crawl']
+    llm_webhooks = [w for w in received_webhooks if w['task_type'] == 'llm_extraction']
+
+    print(f"\nBreakdown:")
+    print(f"  🕷️  Crawl jobs: {len(crawl_webhooks)}")
+    print(f"  🤖 LLM extraction jobs: {len(llm_webhooks)}")
+
+    print(f"\nDetails:")
+    for i, webhook in enumerate(received_webhooks, 1):
+        icon = "🕷️" if webhook['task_type'] == 'crawl' else "🤖"
+        print(f"  {i}. {icon} {webhook['task_id']}: {webhook['status']}")
+
+    print("\n" + "="*70)
+    print("✨ v0.7.6 KEY FEATURES DEMONSTRATED:")
+    print("="*70)
+    print("✅ Webhook support for /crawl/job")
+    print("✅ Webhook support for /llm/job (NEW!)")
+    print("✅ Notification-only mode (fetch data separately)")
+    print("✅ Data-in-payload mode (get full results in webhook)")
+    print("✅ Custom headers for authentication")
+    print("✅ JSON schema for structured LLM extraction")
+    print("✅ Exponential backoff retry for reliable delivery")
+    print("✅ Global webhook configuration support")
+    print("✅ Universal webhook handler for both job types")
+    print("\n💡 Benefits:")
+    print("   • No more polling - get instant notifications")
+    print("   • Better resource utilization")
+    print("   • Reliable delivery with automatic retries")
+    print("   • Consistent API across crawl and LLM jobs")
+    print("   • Production-ready webhook infrastructure")
+
+
+def main():
+    """Run all demos."""
+    print("\n" + "="*70)
+    print("🚀 Crawl4AI v0.7.6 Release Demo")
+    print("="*70)
+    print("Feature: Webhook Support for Docker Job Queue API")
+    print("="*70)
+
+    # Check if server is running
+    try:
+        health = requests.get(f"{CRAWL4AI_BASE_URL}/health", timeout=5)
+        print(f"✅ Crawl4AI server is running")
+    except:
+        print(f"❌ Cannot connect to Crawl4AI at {CRAWL4AI_BASE_URL}")
+        print("Please start Docker container:")
+        print("  docker run -d -p 11235:11235 --env-file .llm.env unclecode/crawl4ai:0.7.6")
+        return
+
+    # Start webhook server
+    print(f"\n🌐 Starting webhook server at {WEBHOOK_BASE_URL}...")
+    webhook_thread = Thread(target=start_webhook_server, daemon=True)
+    webhook_thread.start()
+    time.sleep(2)
+
+    # Run demos
+    demo_1_crawl_webhook_notification_only()
+    time.sleep(5)
+
+    demo_2_crawl_webhook_with_data()
+    time.sleep(5)
+
+    demo_3_llm_webhook_notification_only()
+    time.sleep(5)
+
+    demo_4_llm_webhook_with_schema()
+    time.sleep(5)
+
+    demo_5_global_webhook_config()
+    demo_6_webhook_retry_logic()
+
+    # Wait for webhooks
+    print("\n⏳ Waiting for all webhooks to arrive...")
+    time.sleep(30)
+
+    # Print summary
+    print_summary()
+
+    print("\n" + "="*70)
+    print("✅ Demo completed!")
+    print("="*70)
+    print("\n📚 Documentation:")
+    print("   • deploy/docker/WEBHOOK_EXAMPLES.md")
+    print("   • docs/examples/docker_webhook_example.py")
+    print("\n🔗 Upgrade:")
+    print("   docker pull unclecode/crawl4ai:0.7.6")
+
+
+if __name__ == "__main__":
+    main()