RetrievalAugmentedGeneration-Tutorial

Starter Guide for RAG (Retrieval Augmented Generation) using local models with Ollama and Chroma.

This Jupyter Notebook guides you through building a Retrieval Augmented Generation (RAG) pipeline using Chroma for vector storage and Ollama for embeddings + LLM generation.

Expected Data-Structure

TicketId,Project,Question,Answer
1001,CRM Suite,Cannot log into the CRM; getting 'invalid credentials' even though my password is correct.,"We reset the user's password, cleared browser cache, and verified SSO token freshness. Issue resolved."

Prerequisites

• Install & run Ollama: ollama serve and pull models (e.g., ollama pull llama3.1, ollama pull nomic-embed-text).
• Install Python packages: pip install chromadb pandas ollama.
• Place your CSV file (e.g., tickets.csv) in the same working directory as this notebook.