Here's a hands-on guide to building a custom app using LangChain, a RAG system, and local LLMs with Ollama — perfect for beginners.

Atomic Object is a software consultancy specializing in custom software development and product design. Readers can learn about software development methodologies, user-centered design principles, and agile practices. With case studies, blog posts, and technical insights, Atomic Object provides  guidance and expertise for building innovative and user-friendly software products.

Atomic Spin

A beginner-friendly walkthrough for building a RAG (Retrieval-Augmented Generation) application using LangChain, Ollama, and Python. Covers setting up a local LLM with Ollama, configuring a Jupyter Notebook environment in VS Code, installing LangChain, creating prompt templates and output parsers, loading a PDF with PyPDFLoader, and wiring everything into a chain that answers questions based strictly on the document's content. Also explains how to switch seamlessly between OpenAI's GPT API and locally running models like Llama2 or Mixtral without changing application code.

How to Build a RAG App for Beginners Using LangChain