G.O.D. Framework

Introduction

The ai_crawling_data_retrieval.py script is an essential module in the data automation pipeline of the G.O.D. framework. This script is responsible for crawling various online and offline data sources to extract structured and unstructured data for downstream processing.

Purpose

• Automated Data Retrieval: Simplifies the process of collecting data from multiple sources (e.g., APIs, websites, databases).
• Efficient Crawling: Ensures efficient data collection using multi-threaded crawling techniques.
• Data Structuring: Structures raw data into meaningful formats for further processing.
• Scalability: Enables large-scale data retrieval for machine learning and AI workflows.

Key Features

• Customizable Crawling: Configurable crawling logic based on user-defined settings for targeted data collection.
• Threaded Execution: Supports multi-threaded crawling to handle multiple sources efficiently.
• Real-Time Logging: Monitors crawling progress and logs errors or dropped connections.
• Integration-Friendly: Can be seamlessly integrated into other G.O.D. data pipeline modules like ai_automated_data_pipeline.py or ai_data_registry.py.

Logic and Implementation

The script is built to follow a robust crawling process to gather and process data from multiple sources. The generalized workflow is:

1. Receive crawling configuration such as URLs, API endpoints, or database credentials.
2. Initialize parallel threads for fetching data from these sources iteratively.
3. Extract and refine the retrieved content using parsing techniques like filtering or regular expressions.
4. Store structured and raw data outputs into designated storage or return them for advanced processing.

            import requests
            from threading import Thread

            class DataCrawler:
                def __init__(self, urls):
                    self.urls = urls
                    self.data = {}

                def crawl_url(self, url):
                    """
                    Fetches data from the given URL and stores results in the data dictionary.
                    :param url: The target URL to crawl.
                    """
                    try:
                        response = requests.get(url)
                        if response.status_code == 200:
                            self.data[url] = response.text
                            print(f"Data Retrieved: {url}")
                        else:
                            print(f"Failed to fetch {url} - Status Code: {response.status_code}")
                    except Exception as e:
                        print(f"Error while crawling {url}: {str(e)}")

                def start_crawling(self):
                    """
                    Starts the threaded crawling process.
                    """
                    threads = []
                    for url in self.urls:
                        thread = Thread(target=self.crawl_url, args=(url,))
                        threads.append(thread)
                        thread.start()

                    for thread in threads:
                        thread.join()
                    print("Crawling completed.")

            if __name__ == "__main__":
                # Example URLs (Can be replaced with any accessible endpoints)
                website_urls = ["https://example.com/data", "https://api.sample.org/resource"]
                crawler = DataCrawler(website_urls)
                crawler.start_crawling()
                print(f"Retrieved Data: {crawler.data}")
            

Dependencies

The script relies on the following Python libraries:

• requests: For HTTP requests to retrieve data from web sources.
• threading: Enables multi-threaded crawling for improved efficiency.

How to Use This Script

1. Define a list of target URLs or endpoints for the crawling process.
2. Instantiate the DataCrawler class, passing the list of URLs to its constructor.
3. Call the start_crawling() method to begin data fetching from all specified sources.
4. Access the collected data via the data property of the DataCrawler instance.

            # Example usage
            data_sources = [
                "https://jsonplaceholder.typicode.com/posts",
                "https://jsonplaceholder.typicode.com/comments"
            ]
            crawler = DataCrawler(data_sources)
            crawler.start_crawling()
            print("Retrieved Data:", crawler.data)
            

Role in the G.O.D. Framework

• Automated Data Pipeline: Works in conjunction with ai_automated_data_pipeline.py for clean data ingestion.
• Data Registry: Contributes raw and processed data to the central repository (see ai_data_registry.py).
• Error Logs: Sends failed crawl reports for logging and resolution via ai_error_tracker.py.
• Scalable Crawling: Facilitates large-scale web indexing for AI analytics engines.

Future Enhancements

• Support for Authenticated APIs: Add functionality for API calls requiring authentication tokens or keys.
• Data Parsing Enhancements: Incorporate advanced parsers like BeautifulSoup for HTML data.
• Distributed Crawling: Utilize libraries like asyncio for more scalable, asynchronous crawling.