This repository demonstrates practical applications of large language models through LangChain experiments. It showcases creating searchable databases from YouTube video transcripts, performing similarity search using FAISS, and building LLM-powered applications that combine models with custom data sources. The notebooks explore LangChain’s core components including models, prompts, memory, chains, and agents.