Web Scraping Engine
Paste any URL — docs site, landing page, or web app. Our Puppeteer + Browserless integration renders JavaScript, scrapes, cleans, and vectorizes content in seconds.
Everything included
Stop spending weeks configuring boilerplate. FastRAG handles the infrastructure so you can focus on what makes your product unique.
Paste any URL — docs site, landing page, or web app. Our Puppeteer + Browserless integration renders JavaScript, scrapes, cleans, and vectorizes content in seconds.
Drag & drop up to 10 PDFs simultaneously. Automatic chunking with configurable overlap ensures nothing gets lost — from dense legal contracts to research papers.
Forced 1024-dim embeddings reduce your Pinecone storage costs by 33% versus default settings. Every token counts when you're paying for vector DB at scale.
Vercel AI SDK integration means users see answers appearing token by token. No loading spinners, no waiting — just an experience that feels instant and alive.
Each user's documents live in a private Pinecone namespace. Context never bleeds between tenants — your SaaS is enterprise-safe from day one.
The chat interface is built mobile-first and feels native on iOS and Android. Tested on real devices — not just resized desktop layouts.
Full access to every API route under pages/api. Swap the vector DB, change the LLM, tweak prompts — everything is your code. No vendor lock-in.
Uses Browserless.io so Puppeteer runs safely in serverless environments. Renders React, Vue, and SPA sites perfectly without timing issues or bot blocking.
Buy once, get every future update. Private GitHub repo access means you pull improvements the day they ship. No new version to purchase.
Simple setup
No ML expertise required. No infrastructure headaches. Just clone, configure, and ship.
Drop in PDFs or paste any URL. Our ingestion engine automatically scrapes, parses, and chunks your content using LangChain's RecursiveCharacterTextSplitter — with sensible defaults you can override.
// pages/api/ingest-url.jsconst result = await ingestURL({url: 'https://docs.yourapp.com',namespace: req.user.id,chunkSize: 1000,chunkOverlap: 200,});// → "Processed 42 chunks in 3.1s"
Text chunks are converted to 1024-dimensional embeddings via OpenAI's API, then stored in your Pinecone serverless index. Each user gets their own namespace — clean multi-tenancy from day one.
// lib/vector-store.jsawait pinecone.index('fastrag').namespace(userId).upsert(chunks.map((chunk, i) => ({id: `${docId}-${i}`,values: embeddings[i],metadata: { text: chunk },})));
When a user asks a question, FastRAG retrieves the top-K most relevant chunks, constructs a context-rich prompt, and streams the GPT-4o response token by token — no full-page loads, no waiting.
// pages/api/chat.jsconst matches = await vectorStore.similaritySearch(query, {topK: 5,namespace: userId,});const { stream } = await streamChat({model: 'gpt-4o',context: matches,query,});
Push to Vercel with one command. Edge functions handle thousands of concurrent chats with zero cold starts. Add your custom domain, configure your env vars, and you're live.
# .env.localOPENAI_API_KEY=sk-...PINECONE_API_KEY=pcsk_...PINECONE_INDEX=fastragBROWSERLESS_URL=wss://chrome.browserless.io...# One-command deploy$ vercel --prod# ✅ https://yourapp.vercel.app
Real-world applications
FastRAG is a foundation, not a template. Here's what teams are shipping with it — and what you can build this weekend.
Let employees query your entire Notion, Confluence, or custom docs in plain English. No more hunting through folders — just ask and get an instant, accurate answer.
Train on your help docs, FAQs, and product manuals. Answer 80% of tier-1 tickets automatically — 24/7, in any language, without hiring more staff.
Upload lecture notes, textbooks, and syllabi. Students get instant answers from course material instead of waiting for office hours or digging through PDFs.
Upload NDAs, contracts, compliance policies, or regulation PDFs. Lawyers and operations teams query them conversationally instead of reading hundreds of pages manually.
Point FastRAG at your API reference, README files, and changelogs. Developers find what they need in seconds instead of searching GitHub or reading docs page by page.
Ingest your product catalog, specs, and customer reviews. Build a conversational shopping assistant that upsells intelligently and handles product questions at scale.
Loved by builders
Don't take our word for it. Here's what the community is building with FastRAG right now.
“Shipped my AI doc assistant in 2 days. The code quality is insane for the price — every file is clean, well-commented, and easy to extend. This isn't some hacky boilerplate.”
“We evaluated five RAG frameworks before finding FastRAG. The namespace isolation alone saved us two weeks of architecture work. Had it in production before the weekend was over.”
“I charge clients £5K–£12K for AI chat implementations. FastRAG is my secret weapon. The web scraping engine handles SPA sites that every other solution completely chokes on.”
“Even though I'm not technical, I got the demo running using just the docs. The creator replied to my support email within an hour. Incredible support for a £19 product.”
“We raised a pre-seed round partly on the back of a prototype built with FastRAG. The streaming responses are buttery smooth — investors thought we had a full engineering team.”
“The LangChain integration is done properly — not the 'copy-paste from a tutorial' version. Smart chunking, configurable overlap, clean abstractions. This is how it should be done.”
Simple pricing
No subscriptions. No seat fees. No surprise bills. You only pay for your own infrastructure (OpenAI, Pinecone, Vercel).
Everything you need to ship your first production RAG app this weekend.
one-time · deploy to unlimited projects
🔒 Secure checkout via Gumroad · Instant delivery
Advanced features for teams building multi-tenant SaaS products at scale.
one-time · deploy to unlimited projects
🔒 Secure checkout via Gumroad · Instant delivery
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Common questions
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