How I Built a RAG System as a Python Beginner

Building a Basic RAG System with LLM Using Python
This document describes how I built a basic Retrieval-Augmented Generation (RAG) system using an LLM with simple Python code for AI engineering beginners.
What is RAG?
RAG (Retrieval-Augmented Generation) allows an LLM to access external data sources beyond its original training knowledge.
It works like an external knowledge database connected to the LLM. This database can contain private information stored locally or in the cloud, such as documents, company data, research papers, or other custom information.
Instead of retraining the LLM, RAG allows the model to retrieve relevant information from external sources and use it to generate better answers.
For this beginner project, I built a simple RAG application by loading information from a URL. I also added a Gradio interface to make the system easier to interact with.

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Basic RAG Workflow
A basic RAG system consists of five main steps:

1. Load the Document Using: WebBaseLoader The system loads information from a URL as the knowledge source. RAG can also support other document types such as PDF, DOCX, and more. Different document types may require different loaders, but the overall workflow remains the same. ________________________________________
2. Split the Document into Small Chunks Large documents are divided into smaller pieces for easier processing. chunk = splitter.split_documents(documents) ________________________________________
3. Convert Text into Embeddings The text is converted into numerical representations that capture its meaning. embedding_model = HuggingFaceEmbeddings( model_name="all-MiniLM-L6-v2" ) These embeddings allow the system to search information based on meaning rather than only keywords. ________________________________________
4. Store the Information in a Vector Database The document chunks are stored in a vector database. vectorestore = Chroma.from_documents( documents=chunk, embedding=embedding_model ) The vector database allows the system to quickly find relevant information when a question is asked. ________________________________________
5. Retrieve Information and Generate Answers The retriever searches for relevant information: vectorestore.as_retriever(search_kwargs={"k":3}) The retrieved information is then provided to the LLM, which generates the final answer. ________________________________________ What Fascinated Me About This Project
6. Building Powerful AI Applications with Simple Python With relatively simple Python code, we can already build powerful AI applications by combining LLMs with external knowledge sources. RAG makes AI application development more accessible.
7. Understanding How AI Applications Work After building this project, many concepts behind applications like ChatGPT become clearer. Modern AI systems are not only about the LLM itself, but also about how data, retrieval, and external tools are connected together.
8. LangChain + RAG as a Starting Point for AI Engineering I believe LangChain combined with RAG is a good starting point for beginners who want to understand AI engineering. It provides hands-on experience with important concepts such as: • Data loading • Embeddings • Vector databases • Retrieval systems • LLM integration This project is a simple first step toward building more advanced AI applications.