FastRAG
Hey HN, I built this because I was tired of setting up the same RAG pipeline for every side project. The Problem: Most RAG tutorials give you an answer but don't show where it came from. Hallucinations are a dealbreaker for real apps. * The Solution:* I built a pipeline using Pinecone metadata to map vector chunks back to the original PDF page/text. The UI highlights the source snippet when the AI answers. Stack: Next.js 14 (App Router) Pinecone (Serverless) LangChain (Streaming) Supabase (Auth) Demo: https://www.fastrag.live Happy to answer questions about the chunking…
FastRAG launched on December 19, 2025 with 2 votes, #5,292 of 11,628 launches that month.
What it does
Stop fighting with LangChain. FastRAG is a production-ready Next.js boilerplate that handles the boring parts of building AI apps: vector ingestion, embeddings, and retrieval. Features: ✅ Citation Highlighting (UI shows exact sources) ✅ Drag & Drop PDF Ingestion ✅ Markdown & Code Block Support ✅ Stack: Next.js 16, Pinecone, Tailwind Save 40+ hours of setup. Ship your AI SaaS this weekend.
FastRAG is the production-ready RAG starter kit for Next.js developers. Ingest PDFs & URLs in seconds, chat with your documents using Claude, and deploy to Vercel in minutes. Pay once, own forever.
The production-ready RAG starter kit for Next.js developers. Ingest PDFs & URLs in seconds, chat with your documents using Claude Haiku and Voyage AI embeddings, and deploy to Vercel in minutes — not weeks. 1 // Scrape any URL — Cheerio, no headless browser needed 2 const text = await scrapeUrl ( 'https: //docs.example.com' ); 3 const chunks = await chunkText (text); 4 const embeddings = await embedBatch (chunks); // voyage-3.5 5 6 // Store in Pinecone under a namespace 7 await upsertVectors (embeddings, namespace); 8 9 // Stream Claude Haiku responses with source citations 10 const stream = anthropic . messages . stream ({ 11 model: 'claude-haiku-4-5-20251001' , 12 system:…from fastrag.live
In the maker’s words, at launch
Hey HN, I built this because I was tired of setting up the same RAG pipeline for every side project. The Problem: Most RAG tutorials give you an answer but don't show where it came from. Hallucinations are a dealbreaker for real apps. * The Solution:* I built a pipeline using Pinecone metadata to map vector chunks…
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