What is Private AI Chat with My Website (Local RAG)?
Sometimes you only need a fast way to question a website you already know, such as a product documentation hub, a pricing section, a policy center, or a help site. Copying page text manually into a chatbot is tedious, but sending the site content through a hosted crawl service is not always desirable either, especially when the goal is a lightweight private review in the browser.
Private AI Chat with My Website keeps that workflow local. The browser fetches accessible pages directly, extracts readable content, splits it into chunks with LangChain, builds a Voyager vector index, stores that local index in IndexedDB, and generates on-device answers from the retrieved site passages without app-server page storage.
Website Q&A is useful, but hosted crawling is often more than you want
A lot of website questions are narrow. You may want to ask what a docs site says about quotas, whether a pricing page mentions refunds, or how a policy center describes retention and deletion.
Manual copy-paste is repetitive, and repeating the same scan across several pages wastes time when you revisit the same site later.
At the same time, not every workflow needs a hosted crawler or a remote indexing service just to answer a few targeted questions from pages you can already open yourself.
A browser-side local website RAG flow is useful when you want lightweight page analysis, private retrieval, and clear source-page references on one device.
Browser fetch, local chunking, local Voyager search, local answers
This tool starts from a URL, fetches the accessible root page and optionally follows a few same-origin links, extracts readable text, and turns the page content into retrievable chunks.
It stores those chunks, embeddings, and a serialized Voyager index in IndexedDB so the same device can reopen the website knowledge base later instead of rebuilding it every time.
When you ask a question, the browser retrieves the strongest matching website chunks from the local vector store and feeds them into an on-device answer generation step.
How to Use Private AI Chat with My Website (Local RAG)
- 1Enter a root URL - Start with the main website page, docs landing page, pricing page, or policy section you want to question.
- 2Choose crawl size - Set a modest page limit and same-origin crawl depth so the browser only scans the pages you actually need.
- 3Build the local site index - Let the browser fetch accessible pages, extract text, create embeddings, and save the Voyager index in IndexedDB.
- 4Ask a targeted question - Use focused questions about pricing, documentation, help content, policy language, or product details.
- 5Review answer and source URLs - Check the retrieved website chunks and their original URLs so you can verify what the local answer relied on.
Key Features
- Browser-side website scanning with no proxy or hosted crawl backend
- LangChain.js chunking for extracted page content
- Voyager local vector search with IndexedDB persistence
- Transformers.js embeddings and local answer generation on-device
- Retrieved source chunks linked back to scanned website URLs
Benefits
- Analyze a site without pasting page text into a hosted chatbot
- Keep a local website index on one device for repeated questions
- Inspect the scanned page chunks used for each answer
- Summarize site structure, pricing terms, help pages, or docs with private browser-side retrieval
Use cases
Documentation lookup
Ask what a docs site says about limits, setup steps, supported features, or configuration requirements.
Pricing and policy review
Summarize refunds, plan differences, retention notes, or cancellation language from a few key pages.
Help center review
Question support articles and FAQ sections without manually copying each page into another tool.
Private site summarization
Keep a browser-local snapshot of accessible pages for repeated Q&A on one device.
Tips and common mistakes
Tips
- Start with a small crawl so the browser indexes the most relevant pages first.
- Ask specific questions tied to visible page content rather than broad open-ended prompts.
- Review the retrieved page chunks when the answer sounds too compressed or more confident than the evidence.
- Remember that crawl success depends on direct browser access, not on what a server-side crawler could reach.
Common mistakes
- Assuming the tool can scan any site on the web regardless of CORS or bot protection.
- Treating it like a full technical SEO crawler instead of a private local Q&A workflow.
- Scanning too many pages at once when only a small docs or policy subset is needed.
- Ignoring the cited source URLs and trusting only the top answer paragraph.
Educational notes
- Browser-side website RAG is limited by what the browser can fetch. If a site blocks direct access, a private local workflow cannot simply override that restriction.
- Vector search helps because websites often spread one answer across multiple help pages, pricing sections, and policy paragraphs rather than one obvious page.
- Voyager provides a compact local vector store, but retrieval quality still depends on page text extraction and the quality of the embeddings.
- This type of tool is strongest for targeted website Q&A and summarization rather than deep crawl coverage or formal auditing.
Frequently Asked Questions
Does it use a proxy to bypass site restrictions?
No. It is intentionally browser-only, so it works only where your browser is allowed to fetch the target pages directly.
What gets stored locally?
The route stores scanned page metadata, extracted text chunks, embeddings, the serialized Voyager index, and local chat history in IndexedDB.
Can it follow links across many domains?
No. It is designed around a specific site flow and only follows a limited number of same-origin links.
Can it replace a hosted crawler or enterprise site search?
No. It is a lightweight local RAG assistant for private browser-side page analysis on one device.
Why is this useful if I can already open the site?
Because retrieval can quickly surface relevant chunks across several pages, save the local index for later, and reduce repeated manual scanning.
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