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I am a Journalist-turned-Software Engineer. I love coding and the associated grind of learning every day. A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from. This is my contribution back to the community.
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Video https://www.youtube.com/watch?v=4y8j-LD8IK0 The Goal Welcome to today's post, where we'll dive into building a web scraping agent using Firecrawl, an open-source library with a paid service option. This guide will show you how to use FireCra...
📖 Introduction In modern AI applications, communication between clients and tools isn’t always as simple as calling an API. The Model Context Protocol (MCP) provides a standardized way for clients to exchange information, invoke tools, and maintain ...
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Video https://www.youtube.com/watch?v=UPBMkFSJdBI The Problem at Hand Uber's data platform processes approximately 1.2 million interactive queries monthly, with 36% of these coming from the operations organization. This group—comprising engineers...
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I am Zahiruddin Tavargere (Zahere). A social-learner, here to learn, share and grow with the tech community.
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I am Zahiruddin Tavargere (Zahere). A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from - this is my contribution back to the community.
Keeping track of fluctuating product prices across e-commerce platforms can be a daunting task.
Whether you're tracking a personal wishlist or monitoring competitors' pricing for your business, automating this process can save time and effort.
In this guide, we’ll explore how to build a price monitoring agent using the Pydantic AI framework—a robust agentic framework from the creators of Pantic, a popular data validation library.
This tutorial is part one of a series. Today, we’ll focus on building a scraper agent to extract key product details like title, description, price, and more.
In the next part, we’ll expand this agent to store data in a database and send notifications for price changes.
Pydantic AI is revolutionizing the way developers build applications that leverage Generative AI. As a Python Agent Framework, it simplifies the creation of production-grade applications by integrating robust data validation with the power of LLMs. Here’s why Pydantic AI stands out:
Built on Proven Foundations: Developed by the creators of Pydantic, which is widely used in various AI frameworks like OpenAI and LangChain, Pydantic AI inherits a strong legacy of type safety and structured data management.
Model-Agnostic Flexibility: Currently supporting models like OpenAI, Gemini, and Groq, Pydantic AI allows developers to easily implement support for additional models through a simple interface. This flexibility ensures that your application can adapt to various AI technologies without significant overhead.
Enhanced Developer Experience: With features like vanilla Python control flow and a novel dependency injection system, Pydantic AI empowers developers to apply familiar coding practices. This leads to more maintainable code and a smoother development process.
Streamlined Response Validation: The framework not only validates incoming data but also ensures that responses from LLMs are structured and validated, enhancing reliability in application behavior.
Our agent will:
Scrape product details (title, description, price, currency, and image URL) from a given URL.
Parse the information into a structured format.
Prepare for database storage and notification handling (to be implemented in part two).
Here’s how the process works (diagram above)
Input: Product page URL
Scraper Tool: Extracts structured data using Beautiful Soup and Markdownify.
Agent: Processes the scraped data using Pydantic AI for type-safe responses.