coding

building the best web scraper with GPT4 with JSON and Pydantic

Max Scholar

Mar 31, 2024 — 13 min read

Unleashing the Power of GPT-4 for Web Scraping: A Python and Pydantic Approach

In the digital age, web scraping has become an indispensable tool for data extraction, market research, and competitive analysis. With the advent of advanced AI models like GPT-4, the landscape of web scraping is undergoing a revolutionary transformation. This blog post delves into the cutting-edge realm of building the ultimate web scraper using GPT-4, JSON, and Pydantic, promising to elevate data harvesting to new heights of efficiency and intelligence.

The Evolution of Web Scraping Tools

Web scraping has traditionally been about writing custom code to extract data from web pages. However, the process can be cumbersome, often requiring a deep understanding of HTML, CSS, and XPath. Enter the era of AI-powered web scraping tools, which aim to simplify and automate this process. Libraries like scrapeghost and tools like the GPT Crawler are at the forefront of this innovation, offering Python-based schema definitions, HTML cleaning, and CSS and XPath selectors to streamline the scraping process [1].

GPT-4: The Game Changer

GPT-4, the latest iteration of OpenAI's Generative Pre-trained Transformer models, has taken the AI world by storm. Its ability to understand and generate human-like text has opened up new possibilities for web scraping. By integrating GPT-4 into web scraping libraries, developers can now leverage its natural language processing capabilities to extract data more effectively and with greater context understanding [1].

Python and Pydantic: A Synergistic Duo

Python remains the language of choice for web scraping due to its simplicity and the vast ecosystem of libraries available. Pydantic, a Python library for data validation and settings management using Python type annotations, complements Python's capabilities by ensuring that the data scraped is structured and validated, reducing the likelihood of errors and inconsistencies [1].

Building the Best Web Scraper with GPT-4

Preprocessing and Postprocessing

The key to an effective web scraper lies in its ability to preprocess and postprocess data. Preprocessing involves cleaning HTML to reduce API request size and cost, while postprocessing includes validating the scraped JSON data against a schema. Libraries like scrapeghost offer these features, ensuring that the data you extract is clean and usable [1].

Cost Controls and Efficiency

One of the challenges of using AI models like GPT-4 is managing costs. Advanced web scraping libraries address this by providing cost controls, such as budget settings and token tracking. This allows users to keep their scraping activities within budget without sacrificing the quality of the data extracted [1].

Hallucination Checks

AI models can sometimes "hallucinate," generating data that seems plausible but is actually incorrect. To combat this, modern web scraping tools incorporate hallucination checks to ensure the integrity of the data [1].

Support for Various GPT Models

While GPT-4 is the latest and most advanced model, it's essential to have fallback options. Libraries that support various GPT models, including GPT-3.5-Turbo and GPT-4, provide flexibility and ensure that your scraping tool remains functional even if one model becomes unavailable [1].

The Future of Web Scraping

The integration of GPT-4 with web scraping tools is just the beginning. As AI continues to evolve, we can expect even more sophisticated features, such as automated content analysis, natural language querying, and real-time data extraction. The potential for these tools to transform industries is immense, from providing up-to-date market intelligence to powering content aggregation platforms.

Conclusion

The fusion of GPT-4, JSON, and Pydantic represents the pinnacle of web scraping technology. By harnessing the power of AI, developers can create web scrapers that are not only efficient and cost-effective but also intelligent and adaptable. As we continue to push the boundaries of what's possible with AI, the future of web scraping looks brighter than ever, promising to unlock new insights and opportunities across various domains. Whether you're a seasoned developer or a business analyst, the time to embrace AI-powered web scraping is now.

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resources

[1] scrapeghost

⚡An experimental library for web scraping using OpenAI's GPT models.
🎯To provide a convenient interface for web scraping by leveraging the capabilities of GPT models.
💡Python-based schema definition, HTML cleaning, CSS and XPath selectors, Auto-splitting for large pages, JSON and schema validation, Hallucination checks, Cost controls with budget settings and token tracking.
📝scrapeghost is a library for web scraping using GPT.
scrapeghost includes features to simplify scraping with GPT.
The library allows for Python-based schema definitions.
It offers HTML cleaning to reduce API request size and cost.
CSS and XPath selectors can pre-filter HTML.
HTML can be auto-split into multiple calls for larger pages.
JSON validation ensures responses are in the correct format.
Pydantic schema validation is also supported.
The library checks for hallucinated data that may not exist on the page.
Cost controls help manage the budget and track token usage.
Scrapers can use GPT-3.5-Turbo with a fallback to GPT-4 if necessary.
The scraper stops when the set budget is exceeded.
🔑Python, OpenAI GPT, HTML, CSS, XPath, pydantic, JSON

[2] scrapeghost

[3] scrapeghost

[4] flyscrape

⚡A standalone and scriptable web scraper that combines Go's speed with JavaScript's flexibility.
🎯To simplify the web scraping process by providing a flexible scraping tool that can handle dynamic JavaScript heavy pages and is configurable via JavaScript.
💡Includes standalone binary execution, jQuery-like HTML data extraction, scriptable with JavaScript, extensive configuration options, and browser mode for rendering dynamic content.
📝flyscrape is a web scraper that operates standalone.
It utilizes Go for performance and JavaScript for flexibility.
Data can be extracted from HTML using a jQuery-like API.
The scraper is scriptable, allowing users to define their data extraction logic.
There are over 20 features to tailor the scraping behavior to user needs.
A headless browser mode is available for scraping JavaScript heavy pages.
The tool is distributed as a single binary executable for ease of deployment.
flyscrape has a depth following feature for pagination, capable of going 5 levels deep.
It comes with an easy installation script and support for Homebrew, as well as pre-compiled binaries.
The project is open to issues and suggestions on its GitHub repository.
🔑Go, JavaScript, jQuery, Headless Browser

[5] epg

⚡A Python3 and Django4 based EPG (Electronic Program Guide) data scraping and publishing system.
🎯To scrape various online sources of TV program listings and generate xmltv format files for apps like Perfect Player to load EPG information.
💡Features include scraping EPG data from multiple sources, backend channel configuration, auto source switching on failure, API for EPG data publishing, and robust performance tested on standard office computers with high request volumes.
🔑Python3, Django4, Nginx, uWSGI, MySQL, SQLite3, requests, BeautifulSoup

[6] tvbox

⚡A repository containing links to various streaming sources and updates on access to these resources.
🎯The purpose of the code is to provide updated JSON lists of streaming sources for tv shows and documentaries.
💡The main feature of tvbox is to aggregate and update lists of sources for streaming content. It tracks changes in availability and provides alternate links when sources go down or require verification.
🔑JSON, Hosting Platforms, Web Scraping

[7] crawlProject

⚡A practice project for web crawling and scraping techniques with various difficulty levels.
🎯To provide a comprehensive educational resource for learning web scraping and crawling techniques with incremental difficulty levels.
💡The project includes beginner to advanced crawling techniques, automated web interactions, captcha solving, data parsing, async operations, and JS reverse engineering.
📝The project serves as a hands-on educational resource.
It offers a range of crawling and scraping challenges from beginner to advanced levels.
The project is continually updated with new content.
It includes detailed explanations and practice exercises for various web scraping techniques.
Advanced topics such as JS reverse engineering and captcha solving are covered.
The repository is for educational purposes and not for commercial use.
🔑Python, requests, lxml, selenium, playwright, Scrapy, feapder, JavaScript, Node.js, ddddocr, curl_cffi, pycryptodome, pyexecjs2, m3u8, prettytable, tqdm, loguru, retrying, crypto-js, jsdom, tough-cookie

[8] gpt-crawler

⚡A tool to crawl websites and generate knowledge files for creating custom GPT models.
🎯The code is intended to automate the process of extracting information from websites and structuring it in a format suitable for training custom GPT models with OpenAI's platform.
💡The GPT Crawler can start from a given URL, follow links that match a pattern, extract text using a selector, and output the data to a JSON file. It supports configuration options like maximum pages to crawl, file size limits, and token count limits. The project includes methods to run locally, with Docker, or as a CLI, and instructions to upload data to OpenAI for creating custom GPTs or assistants.
🔑Node.js, TypeScript, Docker, OpenAI API

[9] gpt-crawler

[10] scrapeghost

[11] highest-paying-software-companies

⚡A rough list of 500 companies with relatively high pay for software engineer roles.
🎯To provide a rough ranking of companies based on median total compensation for software engineering roles.
💡The project scrapes median total compensation data for software engineers from levels.fyi and ranks companies accordingly, helping job seekers identify potential high-paying employers.
📝The project is a non-serious analysis providing a rough list of high-paying companies.
Accuracy or sample size of self-reports is not considered.
The list does not account for variables like seniority or location.
Companies lacking sufficient data on levels.fyi are excluded.
The list's order offers low signal due to inconsistent data points.
The median software engineer total compensation data is sourced from levels.fyi as of December 1, 2023.
The project includes a disclaimer advising to take the information with a grain of salt.
🔑Python, Web Scraping, Data Analysis, levels.fyi API

[12] gpt4V-scraper

⚡An AI-powered web scraping tool that captures full-page screenshots and extracts data using GPT-4V and Puppeteer.
🎯To automate the process of web scraping by taking screenshots, performing OCR, and interacting with web pages using natural language prompts.
💡Captures full-page screenshots with Puppeteer, employs stealth to bypass anti-bot measures, automates login for private content access, converts images to text with GPT-4V, and interacts with Bing search via chat.
📝The GPT-4V Web Agent is a tool designed for automated web scraping.
It utilizes Puppeteer with a stealth plugin to avoid detection.
The tool allows for customizable timeout settings for efficiency.
Dependencies are installed using npm, and an environment variable for the OpenAI API key is set.
Browser configuration is required for authenticated website access.
The tool supports screenshot capturing and subsequent text extraction from images.
Python environment setup and package installation are part of the process.
The user can prompt the GPT-4V API for specific scraping tasks.
The tool provides a real-time chat interface for interacting with Bing search.
It can identify trending cryptocurrencies and provide market insights.
🔑Node.js, Puppeteer, GPT-4V, Python, OpenAI API, Chrome Canary, Bing API

[13] IntelliScraper

⚡An advanced Python web scraping tool for efficient HTML content parsing and information extraction.
🎯To facilitate precise HTML content parsing and feature matching for extracting key information from web pages.
💡Offers high customization for targeted data extraction, intelligent matching using cosine similarity algorithms, user-friendly interface, flexibility in fetching HTML content, and extendable core functionality.
📝IntelliScraper is an advanced Python web scraping project.
It is designed for precise HTML content parsing and feature matching.
The project utilizes BeautifulSoup and scikit-learn libraries.
IntelliScraper is efficient and flexible for scraping and processing web data.
It can extract data for analysis and market research.
The tool monitors changes in website content.
It is useful for automated testing of web content and layout.
The scraper provides high customization and intelligent matching.
Despite its complexity, it is user-friendly and simple to use.
It supports HTML fetching directly via URL or from existing content.
The core functionality is implemented in a class for easy extension.
IntelliScraper uses advanced technology for efficient data extraction.
It is adaptable to various web structures, including dynamic websites.
The setup is easy, and it requires only a few lines of code to operate.
It offers higher accuracy and efficiency than static rule-based scrapers.
IntelliScraper is suitable for business analysis, content monitoring, and development testing.
🔑Python, BeautifulSoup, scikit-learn

[14] gpt-crawler

[15] chatgpt

⚡A collection of AI-related resources and personal reflections on content creation.
🎯The code is intended to curate a list of AI tools and resources, and to share the creator's journey and thoughts on content creation.
💡The project features a curated list of AI websites and tools, categorized by functionality such as AI chatbots, AI art, AI writing, and more. It also includes the creator's personal story of growth and learning in content creation.
📝The creator experienced a hiatus from video creation and is reflecting on the direction of future content.
There is an intent to focus on AI-related videos moving forward.
A list of AI resources is provided, tested and compiled by the creator.
The AI list includes various categories such as mobile versions of ChatGPT, alternatives to ChatGPT, mirror sites, original platforms, advanced tools, AI art, papers, databases, QA systems, text content tools, video content understanding, translation services, and other miscellaneous tools.
The creator emphasizes that the shared resources are advertisement-free and there is no profit made from them.
Updates on the AI list will continue without any hidden agendas or advertisements.
🔑ChatGPT, AI, GPT-4, AI Art Generation, AI Writing Assistance, AI Video Summarization, AI Translation

[16] hrequests

⚡A Python library providing a feature-rich replacement for the requests library with additional browser automation capabilities.
🎯To facilitate seamless integration of HTTP requests and headless browser automation with advanced features for network concurrency, realistic browser header generation, and fast HTML parsing.
💡HTTP and headless browser switching, fast HTML parsing, network concurrency with goroutines and gevent, TLS fingerprint replication, JavaScript rendering, HTTP/2 support, realistic browser header generation, fast JSON serialization, cursor movement and typing emulation, extension support, full page screenshots, CORS restriction avoidance, threadsafe operation, minimal standard library dependence, high performance.
🔑Python, Playwright, goroutines, gevent, Go, selectolax, HTML parsing, JavaScript, TLS fingerprinting, HTTP/2

[17] JungleGym

⚡An open-source platform for developing and testing autonomous web agents with datasets and APIs.
🎯To provide tools and datasets for developers to build and test autonomous web agents, and to aid in DOM parsing with LLMs.
💡Includes datasets such as Mind2Web, WebArena, and AgentInstruct for broad and deep agent testing, as well as TreeVoyager, an LLM-based DOM parser tool. Offers APIs and playgrounds for interactive experimentation and development.
🔑Python, JavaScript, GPT-4 Turbo, APIs, Streamlit, HTML/DOM parsing, LLMs

[18] chatgpt_system_prompt

⚡A repository containing a variety of system prompts for ChatGPT and custom GPTs to enhance the understanding of prompt engineering.
🎯To provide educational resources on creating effective system prompts and custom GPTs, as well as to share knowledge on prompt injection security.
💡This project includes a comprehensive list of system prompts, instructions on how to access GPT's system prompts and knowledge files, methods to protect GPT instructions, and learning resources for prompt engineering. It's useful for learning to write better system prompts and understanding the security aspects of prompt injections.
🔑GitHub Actions, Markdown, Python

[19] sweep

⚡An AI junior developer that transforms GitHub issues into code changes.
🎯To automate the process of code modifications like adding typehints or improving test coverage, and creating pull requests from GitHub issues.
💡Sweep turns GitHub issues into pull requests, understands codebase dependencies, runs unit tests and auto-formatters, and applies Sweep Rules for code improvements.
🔑Docker, GPT-4, Python, GitHub Actions, Text and Vector Search, Code Chunking, Retrieval-Augmented-Generation, GitHub App

[20] BambooAI

⚡An experimental library to facilitate intuitive data analysis through natural language interaction using Large Language Models.
🎯To enable users to perform data analysis and visualization by conversing in natural language with their datasets, without the need to write complex code.
💡Support for both internet-sourced and user-supplied data, integration with external APIs, debugging, execution and error correction capabilities, ranking of responses, and a knowledge base built using a vector database.
🔑Python, Pandas, Large Language Models (LLMs), GPT-3.5, GPT-4, Pinecone, Serper API, Huggingface