This repository contains notebooks, prompt templates, and experiments focused on building, testing, and optimizing Retrieval-Augmented Generation (RAG) pipelines using OpenAI models and LangChain.
This project explores how to combine:
- 🔍 RAG architecture — retrieving relevant documents to ground LLM responses
- 🧱 LangChain — for orchestration, retrieval, and tool integration
- 🤖 OpenAI models (GPT-4o, 3.5) — for high-quality natural language generation
- 🧠 Prompt Engineering — to control tone, role, structure, and output formats
Whether you're prototyping a chatbot, customer support assistant, or document summarizer, this repo gives you a strong starting point.
| Folder / File | Description |
|---|---|
notebooks/ |
Jupyter/Colab notebooks with step-by-step RAG + prompt engineering tests |
prompt_templates.py |
Reusable prompt structures for role-based, few-shot, and formatting control |
env/ |
.env file template for API key management (excluded from repo) |
README.md |
You’re here. Project description and setup guide |
git clone https://github.com/your-username/RAG-Langchain.git
cd RAG-Langchain