In this video, Sam Witteveen demonstrates how to use Ollama models with LangChain to perform various tasks locally. He begins by explaining how to set up the environment using VS Code and Python, and how to load the Ollama model locally. The video covers basic tasks such as loading the model and running simple prompts using LangChain’s pre-made LLM for Ollama. Sam shows how to set up a basic chain to generate interesting facts about a topic and how to use the model to scrape and extract information from web pages. The final example involves using a web-based loader to load a webpage, split the content, and store it in a Chroma DB. Sam demonstrates how to set up a retrieval QA chain to query the stored data and retrieve headlines from TechCrunch. He emphasizes the ease of automating such tasks using local LLMs and highlights the potential for setting up a full local RAG (Retrieval-Augmented Generation) system for documents. The video concludes with an invitation for viewers to ask questions and suggest further topics for exploration.