A Tutorial On How to Build Your Own RAG and How to Run It Locally: Langchain + Ollama + Streamlit

Machine Learning Tech Brief By HackerNoon

This story was originally published on HackerNoon at: https://hackernoon.com/a-tutorial-on-how-to-build-your-own-rag-and-how-to-run-it-locally-langchain-ollama-streamlit.
Let's simplify RAG and LLM application development. This post guides you on how to build your own RAG-enabled LLM application and run it locally.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #machine-learning, #artificial-intelligence, #chatbot, #open-source-llm, #rag-architecture, #langchain-tutuorial, #how-to-set-up-ollama, #hackernoon-top-story, #hackernoon-es, #hackernoon-hi, #hackernoon-zh, #hackernoon-fr, #hackernoon-bn, #hackernoon-ru, #hackernoon-vi, #hackernoon-pt, #hackernoon-ja, #hackernoon-de, #hackernoon-ko, #hackernoon-tr, and more.
This story was written by: @vndee. Learn more about this writer by checking @vndee's about page, and for more stories, please visit hackernoon.com.
To become familiar with RAG, I recommend going through these articles. This post, however, will skip the basics and guide you directly on building your own RAG application that can run locally on your laptop without any worries about data privacy and token cost. We will build an application that is something similar to ChatPDF but simpler. Where users can upload a PDF document and ask questions through a straightforward UI. Our tech stack is super easy with Langchain, Ollama, and Streamlit.