Welcome to the Web Scraping with Langsmith project!🚀
This project is built using Jupyter Notebook, making it accessible and easy to follow. The core functionality revolves around web scraping, where we extract relevant data from targeted websites. Leveraging OPENAI embeddings, FAISS, and retrievers, the extracted data and turned it into a responsive, intelligent system that can handle natural language queries with ease.
- Web Scraping: Efficiently scrape and process data from various web sources using Python.
- Langsmith Tracking: Seamlessly track and log the progress of your scraping tasks with Langsmith, ensuring that you have full visibility over your data extraction pipeline.
- AI-Enhanced Retrieval: Use OPENAI embeddings to transform the scraped data into meaningful vectors, allowing for sophisticated querying and information retrieval.
- FAISS Integration: With FAISS (Facebook AI Similarity Search), rapidly search and retrieve relevant data points, making the system both fast and scalable.
- Contextual Responders: Implement retrievers that ensure your queries are met with accurate and contextually relevant responses, making this tool ideal for AI-driven applications.