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RAG System

The RAG System is a multi-modal Retrieval-Augmented Generation (RAG) application. It allows users to upload Request for Proposal (RFP) documents and generates responses using OpenAI's GPT models. The system consists of a backend powered by FastAPI and a frontend built with Next.js.

RAG System UI

Features

  • Upload RFP Documents: Users can upload RFP documents in various formats.
  • Generate Responses: The system processes the uploaded documents and generates responses using OpenAI's GPT-4 model.
  • Modern UI: Sleek and responsive user interface for seamless interaction.

Backend

Setup

  1. Install dependencies:

    pip install -r requirements.txt

  2. Set up your environment variables in a .env file:

    OPENAI_API_KEY=your_openai_api_key_here

  3. Run the FastAPI server:

    uvicorn app.main:app --reload

Endpoints

  • POST /api/upload: Upload a file and receive a generated text response.
  • GET /api/retrieve-docs: Retrieve documents based on a query.

Frontend

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.