Over the past nine years, HelloGitHub has amassed a treasure trove of over 3,000 open-source projects. Yet, as our collection grows, we’ve been hear

My First RAG Chatbot: Here’s how I built it

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2025-01-17 20:00:08

Over the past nine years, HelloGitHub has amassed a treasure trove of over 3,000 open-source projects. Yet, as our collection grows, we’ve been hearing from users that the search function isn’t cutting it. They tell us, “We can’t find the projects we’re looking for!” This feedback hit home, and I realized that simply listing projects isn’t enough. We need to enable our users to discover the open-source projects that truly spark their interest in a smarter way. That’s why I began to think about how to solve this problem using RAG technology.

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model (LLM) by retrieving relevant information from an external knowledge base before generating a response.

I’ve been intrigued by RAG for a while, but I was unsure where to start. While there are plenty of user-friendly RAG low-code tools out there, I didn’t want to just scratch the surface of “how to use it.” I craved a deeper understanding of its inner workings, or else I wouldn’t feel confident deploying it in a production environment. However, the idea of building a RAG system from scratch using LangChain and Ollama does give me some pause.