Retrieval-Augmented Generation (RAG) System with Python & Ollama 🚀

Overview

This repository contains a Jupyter Notebook that demonstrates how to build a Retrieval-Augmented Generation (RAG) system using:

• Python 🐍
• Ollama (for running LLM locally)
• LangChain (for retrieval & orchestration)
• ChromaDB (for vector storage)
• SentenceTransformers (for text embeddings)

🔹 What You'll Learn

✅ What is RAG, and how does it work?
✅ How to extract text from PDFs
✅ How to create embeddings and store them in ChromaDB
✅ How to retrieve relevant information
✅ How to use Ollama’s LLM to generate responses

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📌 Installation

1️⃣ Clone this repository:

git clone https://github.com/yourusername/rag-system.git
cd rag-system

3️⃣ Create a virtual environment & install dependencies:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install langchain chromadb sentence-transformers pypdf ollama

4️⃣ Pull LLM Model:

ollama pull llama3.2

🛠 Usage

Run the Jupyter Notebook (rag_system.ipynb) step-by-step to:

✅ Load and process PDF documents
✅ Generate text embeddings using SentenceTransformers
✅ Store embeddings in ChromaDB for efficient retrieval
✅ Query and retrieve relevant documents
✅ Use Ollama’s LLM to generate responses