Vibe Coding

In the 20th AI Talks meeting we implemented an AI-powered web data extraction and question-answering system that was entirely generated through LLM interactions. The system combines several modern AI and data processing technologies to create a comprehensive solution for web data analysis. At its core, it uses firecrawl.dev for efficient web crawling, Qdrant for vector storage, and OpenAI's GPT-4-mini with LangChain for natural language processing, all wrapped in a user-friendly Streamlit interface.

The architecture is well-structured and modular, with clear separation of concerns across different components: a crawler module for data extraction, a database module for vector storage, a QA module for natural language processing, and a UI module for user interaction. The system can be operated both through a web interface and command-line interface, offering flexibility in how users interact with it. It's designed to crawl websites, store the extracted data in a vector database, and then allow users to query this data using natural language.

What makes this project particularly interesting is that it represents a complete end-to-end system that was generated through AI interactions, demonstrating the capability of LLMs to create complex, functional applications. The codebase includes proper documentation, dependency management, and testing components, showing that the AI was able to consider not just the core functionality but also important aspects of software engineering like maintainability and usability. The system successfully integrates multiple AI and data processing technologies while maintaining a clean, organized structure that follows good software engineering practices.

Link to Codes