SiteAsk — Drop-in AI Chat for Any Website
SiteAsk is the productised version of the Ask Amit assistant on this site — generalised so any blog, docs, or marketing site can install it with a single script tag. The widget loads a static prebuilt index, runs query embedding in the visitor's browser via Transformers.js (all-MiniLM-L6-v2 q8), does cosine retrieval locally, and only then POSTs to a tiny user-supplied backend that proxies the LLM call. That architecture lets it undercut SaaS competitors (Inkeep, Kapa.ai) on cost: no embedding-API bill, no vector DB to host, you pay only for LLM calls and only when someone actually chats. Ships as @siteask/widget (the embed bundle) plus a Node CLI for building the index from markdown directories or arbitrary URLs.
Built in public to learn something or prove a pattern. Some are published packages, some are demos that live on this site.
- ▸Most chat-widget products are SaaS-only and charge per-query — too expensive for personal sites and small docs
- ▸RAG normally requires a backend index + a vector DB + an embedding API key — three pieces of infrastructure to host and pay for
- ▸Cross-site embeds usually need a brittle iframe; CSS isolation + clean theming is non-trivial
- ▸Move embedding and retrieval into the visitor's browser via Transformers.js (q8 quantised model, ~25 MB lazy-loaded once)
- ▸Ship the index as a static JSON file the site owner hosts on their own CDN — no vector DB needed
- ▸Backend is just a thin LLM proxy with a documented 2-key contract; reference implementation for Next.js, Cloudflare Workers and Vercel Edge on the roadmap
- ▸Single IIFE bundle with namespaced CSS classes — no iframe, no shadow DOM gotchas; theme via data attributes
~10 KB gzipped widget bundle; transformers.js + model lazy-load only when a visitor opens the chat
Site owners pay only for LLM tokens, never for embedding API calls or vector DB hosting
One-script-tag install with sensible defaults; programmatic SiteAsk.init() for advanced users
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