https://github.com/abhinavsharma07/rag

https://github.com/abhinavsharma07/rag

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/abhinavsharma07/rag
• Owner: AbhinavSharma07
• License: mit
• Created: 2025-07-08T12:15:28.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-07-16T17:04:13.000Z (3 months ago)
• Last Synced: 2025-07-17T13:54:09.641Z (3 months ago)
• Language: Python
• Size: 65.4 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🚀✨ Retrieval-Augmented Generation (RAG) 

 

Welcome to your **go-to boilerplate for building powerful Retrieval-Augmented Generation systems**! This repo equips you with the essential tools to combine cutting-edge language models with robust document retrieval — delivering answers that are **accurate**, **context-aware**, and **grounded** in your data.

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## 🔍 What is RAG?

RAG (Retrieval-Augmented Generation) is a breakthrough approach that combines:

- 🔎 **Retriever:** Searches your knowledge base (documents, PDFs, databases) to find the most relevant context.

- 🤖 **Generator:** Uses Large Language Models (LLMs) to generate responses grounded in the retrieved information.

This hybrid method bridges **raw language generation** and **fact-based retrieval** — perfect for applications needing trustworthy AI answers.

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## ⚡ Features

- 📂 Seamless document ingestion & chunking (PDFs, text, more)

- 🧠 Powerful embeddings with OpenAI or Hugging Face models

- 🎯 Fast similarity search via FAISS, optionally ElasticSearch

- 💬 Flexible prompt generation & LLM integration (OpenAI GPT, etc.)

- 📚 Chunk-level citations & source attribution

- 🛠️ Modular, extensible architecture ready for production

- 🐳 Optional Docker support for easy deployment

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## 🛠️ Tech Stack Overview

| Layer          | Technology                        |

|----------------|----------------------------------|

| Embeddings     | OpenAI API / Hugging Face Models |

| Vector Search  | FAISS (high-performance)          |

| Backend        | Python + FastAPI / Flask           |

| Generation     | OpenAI GPT / Other LLMs            |

| Storage        | In-memory or persistent DB         |

| Deployment     | Docker, Kubernetes (optional)      |

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