RAG Retrieval-Augmented Generation Codes
I published my first RAG boilerplate — frontend + backend
# Code GeneratorWhat is RAG Retrieval-Augmented Generation Codes?
Hey friends👋, I just finished building my first RAG boilerplate — frontend + backend all set up. It lets you: - Upload multiple PDF and chat with it like “Chat with PDF” - Use a Next.js frontend + FastAPI backend - Connect to Chroma vector DB and Gemini API
Problem
Developers building AI-driven applications spend significant time manually setting up frontend and backend infrastructure for RAG (Retrieval-Augmented Generation) systems, leading to slower development cycles and integration complexity.
Solution
A pre-configured RAG boilerplate (code template) enabling developers to integrate Next.js frontend, FastAPI backend, Chroma vector DB, and Gemini API, allowing instant deployment of PDF-based chatbots with document upload and chat functionality.
Customers
AI developers, engineers, or startups building document analysis tools, custom chatbots, or knowledge-base solutions requiring RAG infrastructure.
Unique Features
Combines specific technologies (Next.js + FastAPI + Chroma + Gemini) in a single package, streamlining end-to-end RAG implementation with PDF parsing and chat functionality pre-built.
User Comments
Saves weeks of setup time
Easy to customize for specific use cases
Efficient PDF-to-chat workflow
Clean code structure
Reduces dependency on third-party services
Traction
Recently launched (first release), shared on ProductHunt; no explicit revenue or user metrics provided.
Market Size
The global AI developer tools market is projected to reach $1.5 billion by 2025 (MarketsandMarkets), with RAG adoption accelerating in document-intensive industries.
