A hands-on implementation of a Retrieval-Augmented Generation (RAG) Agent. This project walks through the fundamentals of building intelligent AI systems that combine information retrieval + LLM reasoning.
This repository contains step-by-step implementations of a RAG-based AI agent.
RAG (Retrieval-Augmented Generation) enhances LLMs by:
Retrieving relevant context from external data Generating more accurate, grounded responses Reducing hallucinations in AI outputs
What You’ll Learn Fundamentals of RAG pipelines Working with embeddings & vector databases Building LLM-powered agents Context-aware question answering systems Structuring real-world AI workflows
rag-agent/ │ ├── DAY_1/ # Introduction to RAG & setup ├── DAY_2/ # Embeddings & retrieval ├── DAY_3/ # Agent + advanced workflows ├── Workbooks/ # Practice notebooks ├── requirements.txt # Dependencies └── README.md
- Python
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Vector Databases (ChromaDB)
- Clone the repository git clone "repo link" cd rag
- Install dependencies pip install -r requirements.txt
- Run the notebooks / scripts
User Query → Convert query into embeddings → Retrieve relevant documents → Pass context + query to LLM → Generate accurate response ✨ Features Modular learning-based structure Beginner-friendly implementation Real-world AI system design
Extensible for: Chatbots Document QA Knowledge assistants
Multi-document support Memory-enabled agents Advanced retrieval (Hybrid search, reranking)
Contributions are welcome!
Fork the repo Create a new branch Commit your changes Submit a PR