☁️ Cloud RAG Chatbot

Live Demo: View on Render (Note: Replace this URL with your actual Render link!)

A modern, web-based Retrieval-Augmented Generation (RAG) chatbot that allows users to upload PDF documents and instantly chat with them. Powered by OpenAI's GPT-3.5-Turbo, this application is designed for seamless cloud deployment on Render or Vercel.

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✨ Features

• Cloud Deployment Ready: Optimized for one-click deployment on Render with Docker.
• In-Memory Vector Search: Uses FAISS to run 100% in memory, completely bypassing Read-Only File System errors on cloud platforms.
• OpenAI Integration: Leverages GPT-3.5-Turbo and OpenAI Embeddings for blazing fast, highly accurate responses.
• Modern RAG Pipeline: Built utilizing LangChain's advanced create_retrieval_chain.
• Beautiful UI: Features a sleek Glassmorphism design, typing indicators, toast notifications, and auto-scrolling.
• Markdown Support: The bot dynamically renders code blocks, lists, and formatted text.

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🏗️ Architecture

1. Document Ingestion: PDFs are temporarily uploaded and parsed using PyPDFLoader.
2. Chunking: Text is split using RecursiveCharacterTextSplitter to maintain contextual boundaries.
3. Embedding: OpenAIEmbeddings convert text chunks into high-dimensional vectors.
4. Vector Store: FAISS stores the vectors entirely in-memory for instant retrieval.
5. Generation: GPT-3.5-Turbo synthesizes the retrieved context into a natural, conversational answer.

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🚀 Quick Start (Local Development)

1. Clone the repository

git clone https://github.com/SrajalSingh/ragbot.git
cd ragbot

2. Set up Environment

Create a virtual environment and install the lightweight cloud dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

3. Add your API Key

Set your OpenAI API key in your terminal before running:

export OPENAI_API_KEY="sk-your-secret-key"

4. Run the Application

python app.py

The app will be available at http://localhost:8000

Screenshot

Upload a PDF, pick your local model, and start chatting.

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Quick Start

Prerequisites

• Python 3.10+
• A GGUF model file (e.g., Qwen2.5-3B-Instruct-Q8) or a HuggingFace model ID

Installation

# Clone the repo
git clone https://github.com/SrajalSingh/ragbot.git
cd ragbot

# Install dependencies
pip install fastapi uvicorn langchain langchain-classic langchain-community langchain-huggingface langchain-text-splitters chromadb sentence-transformers llama-cpp-python torch transformers