Build Your Own RAG App: A Step-by-Step Guide to Setup LLM locally using Ollama, Python, and ChromaDB

Machine Learning Tech Brief By HackerNoon

HackerNoon에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 HackerNoon 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

12M ago 11:33

MP3•에피소드 홈

This story was originally published on HackerNoon at: https://hackernoon.com/build-your-own-rag-app-a-step-by-step-guide-to-setup-llm-locally-using-ollama-python-and-chromadb.
In an era where data privacy is paramount, setting up your own local language model (LLM) provides a crucial solution for companies and individuals alike.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #rag-architecture, #ollama, #python, #ai, #local-large-language-model, #hackernoon-top-story, #build-rag-app, #retrieval-augmented-generation, and more.
This story was written by: @nassermaronie. Learn more about this writer by checking @nassermaronie's about page, and for more stories, please visit hackernoon.com.
This tutorial will guide you through the process of creating a custom chatbot using [Ollama], [Python 3, and [ChromaDB] Hosting your own Retrieval-Augmented Generation (RAG) application locally means you have complete control over the setup and customization.