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
This commit is contained in:
ntohidi
@@ -1,7 +1,7 @@
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FROM python:3.12-slim-bookworm AS build
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# C4ai version
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ARG C4AI_VER=0.7.0-r1
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ARG C4AI_VER=0.7.6
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ENV C4AI_VERSION=$C4AI_VER
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LABEL c4ai.version=$C4AI_VER
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@@ -27,13 +27,13 @@
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Crawl4AI turns the web into clean, LLM ready Markdown for RAG, agents, and data pipelines. Fast, controllable, battle tested by a 50k+ star community.
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[✨ Check out latest update v0.7.5](#-recent-updates)
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[✨ Check out latest update v0.7.6](#-recent-updates)
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✨ New in v0.7.5: Docker Hooks System with function-based API for pipeline customization, Enhanced LLM Integration with custom providers, HTTPS Preservation, and multiple community-reported bug fixes. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.5.md)
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✨ **New in v0.7.6**: Complete Webhook Infrastructure for Docker Job Queue API! Real-time notifications for both `/crawl/job` and `/llm/job` endpoints with exponential backoff retry, custom headers, and flexible delivery modes. No more polling! [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.6.md)
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✨ Recent v0.7.4: Revolutionary LLM Table Extraction with intelligent chunking, enhanced concurrency fixes, memory management refactor, and critical stability improvements. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.4.md)
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✨ Recent v0.7.5: Docker Hooks System with function-based API for pipeline customization, Enhanced LLM Integration with custom providers, HTTPS Preservation, and multiple community-reported bug fixes. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.5.md)
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✨ Previous v0.7.3: Undetected Browser Support, Multi-URL Configurations, Memory Monitoring, Enhanced Table Extraction, GitHub Sponsors. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.3.md)
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✨ Previous v0.7.4: Revolutionary LLM Table Extraction with intelligent chunking, enhanced concurrency fixes, memory management refactor, and critical stability improvements. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.4.md)
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<details>
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<summary>🤓 <strong>My Personal Story</strong></summary>
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@@ -1,7 +1,7 @@
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# crawl4ai/__version__.py
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# This is the version that will be used for stable releases
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__version__ = "0.7.5"
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__version__ = "0.7.6"
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# For nightly builds, this gets set during build process
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__nightly_version__ = None
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@@ -59,15 +59,13 @@ Pull and run images directly from Docker Hub without building locally.
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#### 1. Pull the Image
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Our latest release candidate is `0.7.0-r1`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
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> ⚠️ **Important Note**: The `latest` tag currently points to the stable `0.6.0` version. After testing and validation, `0.7.0` (without -r1) will be released and `latest` will be updated. For now, please use `0.7.0-r1` to test the new features.
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Our latest stable release is `0.7.6`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
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```bash
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# Pull the release candidate (for testing new features)
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docker pull unclecode/crawl4ai:0.7.0-r1
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# Pull the latest stable version (0.7.6)
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docker pull unclecode/crawl4ai:0.7.6
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# Or pull the current stable version (0.6.0)
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# Or use the latest tag (points to 0.7.6)
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docker pull unclecode/crawl4ai:latest
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```
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@@ -102,7 +100,7 @@ EOL
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-p 11235:11235 \
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--name crawl4ai \
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--shm-size=1g \
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unclecode/crawl4ai:0.7.0-r1
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unclecode/crawl4ai:0.7.6
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```
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* **With LLM support:**
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@@ -113,7 +111,7 @@ EOL
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--name crawl4ai \
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--env-file .llm.env \
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--shm-size=1g \
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unclecode/crawl4ai:0.7.0-r1
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unclecode/crawl4ai:0.7.6
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```
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> The server will be available at `http://localhost:11235`. Visit `/playground` to access the interactive testing interface.
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@@ -186,7 +184,7 @@ The `docker-compose.yml` file in the project root provides a simplified approach
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```bash
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# Pulls and runs the release candidate from Docker Hub
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# Automatically selects the correct architecture
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IMAGE=unclecode/crawl4ai:0.7.0-r1 docker compose up -d
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IMAGE=unclecode/crawl4ai:0.7.6 docker compose up -d
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```
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* **Build and Run Locally:**
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314
docs/blog/release-v0.7.6.md
Normal file
314
docs/blog/release-v0.7.6.md
Normal file
@@ -0,0 +1,314 @@
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# Crawl4AI v0.7.6 Release Notes
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*Release Date: October 22, 2025*
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I'm excited to announce Crawl4AI v0.7.6, featuring a complete webhook infrastructure for the Docker job queue API! This release eliminates polling and brings real-time notifications to both crawling and LLM extraction workflows.
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## 🎯 What's New
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### Webhook Support for Docker Job Queue API
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The headline feature of v0.7.6 is comprehensive webhook support for asynchronous job processing. No more constant polling to check if your jobs are done - get instant notifications when they complete!
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**Key Capabilities:**
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- ✅ **Universal Webhook Support**: Both `/crawl/job` and `/llm/job` endpoints now support webhooks
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- ✅ **Flexible Delivery Modes**: Choose notification-only or include full data in the webhook payload
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- ✅ **Reliable Delivery**: Exponential backoff retry mechanism (5 attempts: 1s → 2s → 4s → 8s → 16s)
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- ✅ **Custom Authentication**: Add custom headers for webhook authentication
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- ✅ **Global Configuration**: Set default webhook URL in `config.yml` for all jobs
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- ✅ **Task Type Identification**: Distinguish between `crawl` and `llm_extraction` tasks
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### How It Works
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Instead of constantly checking job status:
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**OLD WAY (Polling):**
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```python
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# Submit job
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response = requests.post("http://localhost:11235/crawl/job", json=payload)
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task_id = response.json()['task_id']
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# Poll until complete
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while True:
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status = requests.get(f"http://localhost:11235/crawl/job/{task_id}")
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if status.json()['status'] == 'completed':
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break
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time.sleep(5) # Wait and try again
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```
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**NEW WAY (Webhooks):**
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```python
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# Submit job with webhook
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payload = {
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"urls": ["https://example.com"],
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"webhook_config": {
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"webhook_url": "https://myapp.com/webhook",
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"webhook_data_in_payload": True
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}
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}
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response = requests.post("http://localhost:11235/crawl/job", json=payload)
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# Done! Webhook will notify you when complete
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# Your webhook handler receives the results automatically
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```
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### Crawl Job Webhooks
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```bash
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curl -X POST http://localhost:11235/crawl/job \
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-H "Content-Type: application/json" \
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-d '{
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"urls": ["https://example.com"],
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"browser_config": {"headless": true},
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"crawler_config": {"cache_mode": "bypass"},
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"webhook_config": {
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"webhook_url": "https://myapp.com/webhooks/crawl-complete",
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"webhook_data_in_payload": false,
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"webhook_headers": {
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"X-Webhook-Secret": "your-secret-token"
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}
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}
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}'
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```
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### LLM Extraction Job Webhooks (NEW!)
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```bash
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curl -X POST http://localhost:11235/llm/job \
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-H "Content-Type: application/json" \
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-d '{
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"url": "https://example.com/article",
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"q": "Extract the article title, author, and publication date",
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"schema": "{\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\"}}}",
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"provider": "openai/gpt-4o-mini",
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"webhook_config": {
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"webhook_url": "https://myapp.com/webhooks/llm-complete",
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"webhook_data_in_payload": true
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}
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}'
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```
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### Webhook Payload Structure
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**Success (with data):**
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```json
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{
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"task_id": "llm_1698765432",
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"task_type": "llm_extraction",
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"status": "completed",
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"timestamp": "2025-10-22T10:30:00.000000+00:00",
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"urls": ["https://example.com/article"],
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"data": {
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"extracted_content": {
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"title": "Understanding Web Scraping",
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"author": "John Doe",
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"date": "2025-10-22"
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}
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}
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}
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```
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**Failure:**
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```json
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{
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"task_id": "crawl_abc123",
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"task_type": "crawl",
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"status": "failed",
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"timestamp": "2025-10-22T10:30:00.000000+00:00",
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"urls": ["https://example.com"],
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"error": "Connection timeout after 30s"
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}
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```
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### Simple Webhook Handler Example
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```python
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from flask import Flask, request, jsonify
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app = Flask(__name__)
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@app.route('/webhook', methods=['POST'])
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def handle_webhook():
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payload = request.json
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task_id = payload['task_id']
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task_type = payload['task_type']
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status = payload['status']
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if status == 'completed':
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if 'data' in payload:
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# Process data directly
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data = payload['data']
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else:
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# Fetch from API
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endpoint = 'crawl' if task_type == 'crawl' else 'llm'
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response = requests.get(f'http://localhost:11235/{endpoint}/job/{task_id}')
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data = response.json()
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# Your business logic here
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print(f"Job {task_id} completed!")
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elif status == 'failed':
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error = payload.get('error', 'Unknown error')
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print(f"Job {task_id} failed: {error}")
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return jsonify({"status": "received"}), 200
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app.run(port=8080)
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```
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## 📊 Performance Improvements
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- **Reduced Server Load**: Eliminates constant polling requests
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- **Lower Latency**: Instant notification vs. polling interval delay
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- **Better Resource Usage**: Frees up client connections while jobs run in background
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- **Scalable Architecture**: Handles high-volume crawling workflows efficiently
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## 🐛 Bug Fixes
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- Fixed webhook configuration serialization for Pydantic HttpUrl fields
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- Improved error handling in webhook delivery service
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- Enhanced Redis task storage for webhook config persistence
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## 🌍 Expected Real-World Impact
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### For Web Scraping Workflows
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- **Reduced Costs**: Less API calls = lower bandwidth and server costs
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- **Better UX**: Instant notifications improve user experience
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- **Scalability**: Handle 100s of concurrent jobs without polling overhead
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### For LLM Extraction Pipelines
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- **Async Processing**: Submit LLM extraction jobs and move on
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- **Batch Processing**: Queue multiple extractions, get notified as they complete
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- **Integration**: Easy integration with workflow automation tools (Zapier, n8n, etc.)
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### For Microservices
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- **Event-Driven**: Perfect for event-driven microservice architectures
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- **Decoupling**: Decouple job submission from result processing
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- **Reliability**: Automatic retries ensure webhooks are delivered
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## 🔄 Breaking Changes
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**None!** This release is fully backward compatible.
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- Webhook configuration is optional
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- Existing code continues to work without modification
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- Polling is still supported for jobs without webhook config
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## 📚 Documentation
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### New Documentation
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- **[WEBHOOK_EXAMPLES.md](../deploy/docker/WEBHOOK_EXAMPLES.md)** - Comprehensive webhook usage guide
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- **[docker_webhook_example.py](../docs/examples/docker_webhook_example.py)** - Working code examples
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### Updated Documentation
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- **[Docker README](../deploy/docker/README.md)** - Added webhook sections
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- API documentation with webhook examples
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## 🛠️ Migration Guide
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No migration needed! Webhooks are opt-in:
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1. **To use webhooks**: Add `webhook_config` to your job payload
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2. **To keep polling**: Continue using your existing code
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### Quick Start
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```python
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# Just add webhook_config to your existing payload
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payload = {
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# Your existing configuration
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"urls": ["https://example.com"],
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"browser_config": {...},
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"crawler_config": {...},
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# NEW: Add webhook configuration
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"webhook_config": {
|
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"webhook_url": "https://myapp.com/webhook",
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"webhook_data_in_payload": True
|
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}
|
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}
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```
|
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|
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## 🔧 Configuration
|
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|
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### Global Webhook Configuration (config.yml)
|
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|
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```yaml
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webhooks:
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enabled: true
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default_url: "https://myapp.com/webhooks/default" # Optional
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data_in_payload: false
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retry:
|
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max_attempts: 5
|
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initial_delay_ms: 1000
|
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max_delay_ms: 32000
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timeout_ms: 30000
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headers:
|
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User-Agent: "Crawl4AI-Webhook/1.0"
|
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```
|
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|
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## 🚀 Upgrade Instructions
|
||||
|
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### Docker
|
||||
|
||||
```bash
|
||||
# Pull the latest image
|
||||
docker pull unclecode/crawl4ai:0.7.6
|
||||
|
||||
# Or use latest tag
|
||||
docker pull unclecode/crawl4ai:latest
|
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|
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# Run with webhook support
|
||||
docker run -d \
|
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-p 11235:11235 \
|
||||
--env-file .llm.env \
|
||||
--name crawl4ai \
|
||||
unclecode/crawl4ai:0.7.6
|
||||
```
|
||||
|
||||
### Python Package
|
||||
|
||||
```bash
|
||||
pip install --upgrade crawl4ai
|
||||
```
|
||||
|
||||
## 💡 Pro Tips
|
||||
|
||||
1. **Use notification-only mode** for large results - fetch data separately to avoid large webhook payloads
|
||||
2. **Set custom headers** for webhook authentication and request tracking
|
||||
3. **Configure global default webhook** for consistent handling across all jobs
|
||||
4. **Implement idempotent webhook handlers** - same webhook may be delivered multiple times on retry
|
||||
5. **Use structured schemas** with LLM extraction for predictable webhook data
|
||||
|
||||
## 🎬 Demo
|
||||
|
||||
Try the release demo:
|
||||
|
||||
```bash
|
||||
python docs/releases_review/demo_v0.7.6.py
|
||||
```
|
||||
|
||||
This comprehensive demo showcases:
|
||||
- Crawl job webhooks (notification-only and with data)
|
||||
- LLM extraction webhooks (with JSON schema support)
|
||||
- Custom headers for authentication
|
||||
- Webhook retry mechanism
|
||||
- Real-time webhook receiver
|
||||
|
||||
## 🙏 Acknowledgments
|
||||
|
||||
Thank you to the community for the feedback that shaped this feature! Special thanks to everyone who requested webhook support for asynchronous job processing.
|
||||
|
||||
## 📞 Support
|
||||
|
||||
- **Documentation**: https://docs.crawl4ai.com
|
||||
- **GitHub Issues**: https://github.com/unclecode/crawl4ai/issues
|
||||
- **Discord**: https://discord.gg/crawl4ai
|
||||
|
||||
---
|
||||
|
||||
**Happy crawling with webhooks!** 🕷️🪝
|
||||
|
||||
*- unclecode*
|
||||
@@ -20,6 +20,23 @@ Ever wondered why your AI coding assistant struggles with your library despite c
|
||||
|
||||
## Latest Release
|
||||
|
||||
### [Crawl4AI v0.7.6 – The Webhook Infrastructure Update](../blog/release-v0.7.6.md)
|
||||
*October 22, 2025*
|
||||
|
||||
Crawl4AI v0.7.6 introduces comprehensive webhook support for the Docker job queue API, bringing real-time notifications to both crawling and LLM extraction workflows. No more polling!
|
||||
|
||||
Key highlights:
|
||||
- **🪝 Complete Webhook Support**: Real-time notifications for both `/crawl/job` and `/llm/job` endpoints
|
||||
- **🔄 Reliable Delivery**: Exponential backoff retry mechanism (5 attempts: 1s → 2s → 4s → 8s → 16s)
|
||||
- **🔐 Custom Authentication**: Add custom headers for webhook authentication
|
||||
- **📊 Flexible Delivery**: Choose notification-only or include full data in payload
|
||||
- **⚙️ Global Configuration**: Set default webhook URL in config.yml for all jobs
|
||||
- **🎯 Zero Breaking Changes**: Fully backward compatible, webhooks are opt-in
|
||||
|
||||
[Read full release notes →](../blog/release-v0.7.6.md)
|
||||
|
||||
## Recent Releases
|
||||
|
||||
### [Crawl4AI v0.7.5 – The Docker Hooks & Security Update](../blog/release-v0.7.5.md)
|
||||
*September 29, 2025*
|
||||
|
||||
|
||||
314
docs/md_v2/blog/releases/0.7.6.md
Normal file
314
docs/md_v2/blog/releases/0.7.6.md
Normal file
@@ -0,0 +1,314 @@
|
||||
# Crawl4AI v0.7.6 Release Notes
|
||||
|
||||
*Release Date: October 22, 2025*
|
||||
|
||||
I'm excited to announce Crawl4AI v0.7.6, featuring a complete webhook infrastructure for the Docker job queue API! This release eliminates polling and brings real-time notifications to both crawling and LLM extraction workflows.
|
||||
|
||||
## 🎯 What's New
|
||||
|
||||
### Webhook Support for Docker Job Queue API
|
||||
|
||||
The headline feature of v0.7.6 is comprehensive webhook support for asynchronous job processing. No more constant polling to check if your jobs are done - get instant notifications when they complete!
|
||||
|
||||
**Key Capabilities:**
|
||||
|
||||
- ✅ **Universal Webhook Support**: Both `/crawl/job` and `/llm/job` endpoints now support webhooks
|
||||
- ✅ **Flexible Delivery Modes**: Choose notification-only or include full data in the webhook payload
|
||||
- ✅ **Reliable Delivery**: Exponential backoff retry mechanism (5 attempts: 1s → 2s → 4s → 8s → 16s)
|
||||
- ✅ **Custom Authentication**: Add custom headers for webhook authentication
|
||||
- ✅ **Global Configuration**: Set default webhook URL in `config.yml` for all jobs
|
||||
- ✅ **Task Type Identification**: Distinguish between `crawl` and `llm_extraction` tasks
|
||||
|
||||
### How It Works
|
||||
|
||||
Instead of constantly checking job status:
|
||||
|
||||
**OLD WAY (Polling):**
|
||||
```python
|
||||
# Submit job
|
||||
response = requests.post("http://localhost:11235/crawl/job", json=payload)
|
||||
task_id = response.json()['task_id']
|
||||
|
||||
# Poll until complete
|
||||
while True:
|
||||
status = requests.get(f"http://localhost:11235/crawl/job/{task_id}")
|
||||
if status.json()['status'] == 'completed':
|
||||
break
|
||||
time.sleep(5) # Wait and try again
|
||||
```
|
||||
|
||||
**NEW WAY (Webhooks):**
|
||||
```python
|
||||
# Submit job with webhook
|
||||
payload = {
|
||||
"urls": ["https://example.com"],
|
||||
"webhook_config": {
|
||||
"webhook_url": "https://myapp.com/webhook",
|
||||
"webhook_data_in_payload": True
|
||||
}
|
||||
}
|
||||
response = requests.post("http://localhost:11235/crawl/job", json=payload)
|
||||
|
||||
# Done! Webhook will notify you when complete
|
||||
# Your webhook handler receives the results automatically
|
||||
```
|
||||
|
||||
### Crawl Job Webhooks
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:11235/crawl/job \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"urls": ["https://example.com"],
|
||||
"browser_config": {"headless": true},
|
||||
"crawler_config": {"cache_mode": "bypass"},
|
||||
"webhook_config": {
|
||||
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
|
||||
"webhook_data_in_payload": false,
|
||||
"webhook_headers": {
|
||||
"X-Webhook-Secret": "your-secret-token"
|
||||
}
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
### LLM Extraction Job Webhooks (NEW!)
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:11235/llm/job \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"url": "https://example.com/article",
|
||||
"q": "Extract the article title, author, and publication date",
|
||||
"schema": "{\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\"}}}",
|
||||
"provider": "openai/gpt-4o-mini",
|
||||
"webhook_config": {
|
||||
"webhook_url": "https://myapp.com/webhooks/llm-complete",
|
||||
"webhook_data_in_payload": true
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
### Webhook Payload Structure
|
||||
|
||||
**Success (with data):**
|
||||
```json
|
||||
{
|
||||
"task_id": "llm_1698765432",
|
||||
"task_type": "llm_extraction",
|
||||
"status": "completed",
|
||||
"timestamp": "2025-10-22T10:30:00.000000+00:00",
|
||||
"urls": ["https://example.com/article"],
|
||||
"data": {
|
||||
"extracted_content": {
|
||||
"title": "Understanding Web Scraping",
|
||||
"author": "John Doe",
|
||||
"date": "2025-10-22"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Failure:**
|
||||
```json
|
||||
{
|
||||
"task_id": "crawl_abc123",
|
||||
"task_type": "crawl",
|
||||
"status": "failed",
|
||||
"timestamp": "2025-10-22T10:30:00.000000+00:00",
|
||||
"urls": ["https://example.com"],
|
||||
"error": "Connection timeout after 30s"
|
||||
}
|
||||
```
|
||||
|
||||
### Simple Webhook Handler Example
|
||||
|
||||
```python
|
||||
from flask import Flask, request, jsonify
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
@app.route('/webhook', methods=['POST'])
|
||||
def handle_webhook():
|
||||
payload = request.json
|
||||
|
||||
task_id = payload['task_id']
|
||||
task_type = payload['task_type']
|
||||
status = payload['status']
|
||||
|
||||
if status == 'completed':
|
||||
if 'data' in payload:
|
||||
# Process data directly
|
||||
data = payload['data']
|
||||
else:
|
||||
# Fetch from API
|
||||
endpoint = 'crawl' if task_type == 'crawl' else 'llm'
|
||||
response = requests.get(f'http://localhost:11235/{endpoint}/job/{task_id}')
|
||||
data = response.json()
|
||||
|
||||
# Your business logic here
|
||||
print(f"Job {task_id} completed!")
|
||||
|
||||
elif status == 'failed':
|
||||
error = payload.get('error', 'Unknown error')
|
||||
print(f"Job {task_id} failed: {error}")
|
||||
|
||||
return jsonify({"status": "received"}), 200
|
||||
|
||||
app.run(port=8080)
|
||||
```
|
||||
|
||||
## 📊 Performance Improvements
|
||||
|
||||
- **Reduced Server Load**: Eliminates constant polling requests
|
||||
- **Lower Latency**: Instant notification vs. polling interval delay
|
||||
- **Better Resource Usage**: Frees up client connections while jobs run in background
|
||||
- **Scalable Architecture**: Handles high-volume crawling workflows efficiently
|
||||
|
||||
## 🐛 Bug Fixes
|
||||
|
||||
- Fixed webhook configuration serialization for Pydantic HttpUrl fields
|
||||
- Improved error handling in webhook delivery service
|
||||
- Enhanced Redis task storage for webhook config persistence
|
||||
|
||||
## 🌍 Expected Real-World Impact
|
||||
|
||||
### For Web Scraping Workflows
|
||||
- **Reduced Costs**: Less API calls = lower bandwidth and server costs
|
||||
- **Better UX**: Instant notifications improve user experience
|
||||
- **Scalability**: Handle 100s of concurrent jobs without polling overhead
|
||||
|
||||
### For LLM Extraction Pipelines
|
||||
- **Async Processing**: Submit LLM extraction jobs and move on
|
||||
- **Batch Processing**: Queue multiple extractions, get notified as they complete
|
||||
- **Integration**: Easy integration with workflow automation tools (Zapier, n8n, etc.)
|
||||
|
||||
### For Microservices
|
||||
- **Event-Driven**: Perfect for event-driven microservice architectures
|
||||
- **Decoupling**: Decouple job submission from result processing
|
||||
- **Reliability**: Automatic retries ensure webhooks are delivered
|
||||
|
||||
## 🔄 Breaking Changes
|
||||
|
||||
**None!** This release is fully backward compatible.
|
||||
|
||||
- Webhook configuration is optional
|
||||
- Existing code continues to work without modification
|
||||
- Polling is still supported for jobs without webhook config
|
||||
|
||||
## 📚 Documentation
|
||||
|
||||
### New Documentation
|
||||
- **[WEBHOOK_EXAMPLES.md](../deploy/docker/WEBHOOK_EXAMPLES.md)** - Comprehensive webhook usage guide
|
||||
- **[docker_webhook_example.py](../docs/examples/docker_webhook_example.py)** - Working code examples
|
||||
|
||||
### Updated Documentation
|
||||
- **[Docker README](../deploy/docker/README.md)** - Added webhook sections
|
||||
- API documentation with webhook examples
|
||||
|
||||
## 🛠️ Migration Guide
|
||||
|
||||
No migration needed! Webhooks are opt-in:
|
||||
|
||||
1. **To use webhooks**: Add `webhook_config` to your job payload
|
||||
2. **To keep polling**: Continue using your existing code
|
||||
|
||||
### Quick Start
|
||||
|
||||
```python
|
||||
# Just add webhook_config to your existing payload
|
||||
payload = {
|
||||
# Your existing configuration
|
||||
"urls": ["https://example.com"],
|
||||
"browser_config": {...},
|
||||
"crawler_config": {...},
|
||||
|
||||
# NEW: Add webhook configuration
|
||||
"webhook_config": {
|
||||
"webhook_url": "https://myapp.com/webhook",
|
||||
"webhook_data_in_payload": True
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🔧 Configuration
|
||||
|
||||
### Global Webhook Configuration (config.yml)
|
||||
|
||||
```yaml
|
||||
webhooks:
|
||||
enabled: true
|
||||
default_url: "https://myapp.com/webhooks/default" # Optional
|
||||
data_in_payload: false
|
||||
retry:
|
||||
max_attempts: 5
|
||||
initial_delay_ms: 1000
|
||||
max_delay_ms: 32000
|
||||
timeout_ms: 30000
|
||||
headers:
|
||||
User-Agent: "Crawl4AI-Webhook/1.0"
|
||||
```
|
||||
|
||||
## 🚀 Upgrade Instructions
|
||||
|
||||
### Docker
|
||||
|
||||
```bash
|
||||
# Pull the latest image
|
||||
docker pull unclecode/crawl4ai:0.7.6
|
||||
|
||||
# Or use latest tag
|
||||
docker pull unclecode/crawl4ai:latest
|
||||
|
||||
# Run with webhook support
|
||||
docker run -d \
|
||||
-p 11235:11235 \
|
||||
--env-file .llm.env \
|
||||
--name crawl4ai \
|
||||
unclecode/crawl4ai:0.7.6
|
||||
```
|
||||
|
||||
### Python Package
|
||||
|
||||
```bash
|
||||
pip install --upgrade crawl4ai
|
||||
```
|
||||
|
||||
## 💡 Pro Tips
|
||||
|
||||
1. **Use notification-only mode** for large results - fetch data separately to avoid large webhook payloads
|
||||
2. **Set custom headers** for webhook authentication and request tracking
|
||||
3. **Configure global default webhook** for consistent handling across all jobs
|
||||
4. **Implement idempotent webhook handlers** - same webhook may be delivered multiple times on retry
|
||||
5. **Use structured schemas** with LLM extraction for predictable webhook data
|
||||
|
||||
## 🎬 Demo
|
||||
|
||||
Try the release demo:
|
||||
|
||||
```bash
|
||||
python docs/releases_review/demo_v0.7.6.py
|
||||
```
|
||||
|
||||
This comprehensive demo showcases:
|
||||
- Crawl job webhooks (notification-only and with data)
|
||||
- LLM extraction webhooks (with JSON schema support)
|
||||
- Custom headers for authentication
|
||||
- Webhook retry mechanism
|
||||
- Real-time webhook receiver
|
||||
|
||||
## 🙏 Acknowledgments
|
||||
|
||||
Thank you to the community for the feedback that shaped this feature! Special thanks to everyone who requested webhook support for asynchronous job processing.
|
||||
|
||||
## 📞 Support
|
||||
|
||||
- **Documentation**: https://docs.crawl4ai.com
|
||||
- **GitHub Issues**: https://github.com/unclecode/crawl4ai/issues
|
||||
- **Discord**: https://discord.gg/crawl4ai
|
||||
|
||||
---
|
||||
|
||||
**Happy crawling with webhooks!** 🕷️🪝
|
||||
|
||||
*- unclecode*
|
||||
@@ -65,13 +65,13 @@ Pull and run images directly from Docker Hub without building locally.
|
||||
|
||||
#### 1. Pull the Image
|
||||
|
||||
Our latest release is `0.7.3`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
|
||||
Our latest release is `0.7.6`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
|
||||
|
||||
> 💡 **Note**: The `latest` tag points to the stable `0.7.3` version.
|
||||
> 💡 **Note**: The `latest` tag points to the stable `0.7.6` version.
|
||||
|
||||
```bash
|
||||
# Pull the latest version
|
||||
docker pull unclecode/crawl4ai:0.7.3
|
||||
docker pull unclecode/crawl4ai:0.7.6
|
||||
|
||||
# Or pull using the latest tag
|
||||
docker pull unclecode/crawl4ai:latest
|
||||
@@ -143,7 +143,7 @@ docker stop crawl4ai && docker rm crawl4ai
|
||||
#### Docker Hub Versioning Explained
|
||||
|
||||
* **Image Name:** `unclecode/crawl4ai`
|
||||
* **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.3`)
|
||||
* **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.6`)
|
||||
* `LIBRARY_VERSION`: The semantic version of the core `crawl4ai` Python library
|
||||
* `SUFFIX`: Optional tag for release candidates (``) and revisions (`r1`)
|
||||
* **`latest` Tag:** Points to the most recent stable version
|
||||
|
||||
359
docs/releases_review/demo_v0.7.6.py
Normal file
359
docs/releases_review/demo_v0.7.6.py
Normal file
@@ -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()
|
||||
Reference in New Issue
Block a user