RAG from the web
The pages your AI should know are already open in your browser.
Your sources are web pages. Your browser already has them.Use the browser as the ingestion layer of a RAG pipeline.
Version texte
- RAG from the web: The pages your AI should know are already open in your browser.
Most RAG tutorials start the same way: a crawler or a scraping API downloads pages, a script splits them into chunks, an embedding API turns the chunks into vectors and a vector database on a server stores them. That works, but it is a lot of moving parts when your sources are twenty help-center articles, a product wiki you are signed in to, and the tabs you keep reopening.
I build AWFlow, a browser extension with knowledge bases that live in your browser. This post shows how a web browser can be the first half of a RAG pipeline, which pages it can and can’t read, and where the approach stops. If you want the engineering story of how those knowledge bases were rebuilt, read we rebuilt RAG in a browser tab.
Can a web browser be part of a RAG pipeline?
Yes, and for small, personal or team collections it is often the simplest place to start. A RAG pipeline has two halves: fill a knowledge base (load, split, embed, store) and answer from it (search, then hand the best passages to a model). The browser is already good at the hardest part of the first half: it renders the page, runs its scripts and carries your sign-ins.
RAG needs a crawler, an embedding API and a vector database server.
For a few hundred pages, your browser can load, split, embed and store them on your device.
The browser can do all four for a small collection.Page text from the tab, token chunking, on-device embeddings, an embedded vector store in IndexedDB.
Version texte
- RAG needs a crawler, an embedding API and a vector database server.
- For a few hundred pages, your browser can load, split, embed and store them on your device.
In AWFlow, a knowledge base is a named collection of text chunks and their vectors, stored in an embedded database in your browser. Knowledge bases are never synced; one leaves your device only as a file you export.
How do you get web pages into a knowledge base?
There are five ways in, and they all go through the same indexing pipeline, so a page saved one way and later re-saved another way is still one source.
Five ways in
Getting web pages into a knowledge base
- Paste URLs in Add sources, one per line (fait)automatisé
- Add the tabs you have open (fait)automatisé
- Right-click: Add page to knowledge base (fait)automatisé
- Ask Aria or an agent to add this page (fait)automatisé
- Run an Indexer node in a workflow (fait)automatisé
Automate it with a workflow.Indexer node with a Text Splitter and a Local Knowledge dependency.
Version texte
- Getting web pages into a knowledge base
- Paste URLs in Add sources, one per line
- Add the tabs you have open
- Right-click: Add page to knowledge base
- Ask Aria or an agent to add this page
- Run an Indexer node in a workflow
From the knowledge bases page, New knowledge base opens a dialog where you name it, add your first sources (files, web pages, open tabs or pasted text) and choose its privacy. Each source then shows its own progress while it is indexed.


Four fields, then it fills itself.Re-adding a source later replaces the older copy.
Version texte
- 1. Name it: One knowledge base per topic works best.
- 2. Add the pages: Files, URLs one per line, open tabs or pasted text.
- 3. Keep it on this device: Choose who may read it.
- 4. Create: Indexing starts and shows progress per source.
What happens to a page when you save it?
Every source goes through the same steps. The page’s text is extracted (navigation, scripts, styles and images are skipped), split into chunks of about 350 tokens by default (200 or 800 if you choose small or large), turned into vectors by an embedding model, and written in batches of 16.
Stored on your device, with where it came from.Upsert into the embedded vector store with title, url, chunk index.
Version texte
- A web page: Help center: Refund policy
- Extract the text: Title + body, no menus or scripts
- Split into chunks: About 350 tokens each
- Turn into vectors: On-device model, 384 numbers each
- Store in the browser: Saved with title and link
The default embedding model runs on your device, so building the knowledge base sends nothing to an embedding API. Each chunk keeps its provenance (the page title and link), which is what the answers later cite.
Which web pages can it read: a URL or an open tab?
This is the part to get right, because it decides whether pages behind a login work.
- A URL is downloaded by the app without your cookies. Public pages work. Pages that need you to be signed in, and sites that block this kind of request, fail with a hint to add the open tab instead.
- An open tab (in the extension) is read from the page itself, as you see it. Pages you are signed in to work, because the text comes from your own session.
A URL and an open tab of the same address count as the same source. Saving it again replaces the older copy instead of adding a second one, and if the page got shorter, the extra old chunks are removed.
The rule of thumb
If you can read it in a tab, you can save it to a knowledge base.
Signed-in page? Save it from the tab.Tab sources read the rendered page's text through the extension.
Version texte
- The rule of thumb: If you can read it in a tab, you can save it to a knowledge base.
How do you keep the knowledge base up to date?
Pages change. You have two options:
- By hand: right-click the page and choose Add page to knowledge base again. It replaces the older copy.
- With a workflow: read the page with Get All Text or the Current Page Document Loader, then send the text to an Indexer node with a Text Splitter and a Local Knowledge node connected. Set the Indexer’s Source link to the page address so each run updates the same source, and start it from a Schedule trigger if the page changes often. The RAG docs walk through the nodes.
A workflow that reads a tab needs the browser open when it runs, and it saves one page per run step: it is not a crawler that walks a whole site on its own.
How do you ask the knowledge base questions?
Once it is filled, connect the same Local Knowledge node to a RAG Agent, a Q&A Agent or a Tools Agent in a workflow, or pin the knowledge base in a chat with Aria. Search is hybrid by default: keyword matches and meaning matches are combined, so an exact product code or error number is found even when a pure vector search would miss it. Why vector search misses exact terms explains why that matters.
The answer cites the pages it used.The node outputs sources[] with title and url.
Version texte
- Your question: Refund after 30 days? · Hybrid search: Best matching chunks · Sources: Page titles and links
- Refund after 30 days? + Best matching chunks + Page titles and links → RAG agent
- RAG agent → The reply: Answer with sources
Before you build the answering step, use Test search on the knowledge base page: type a question you expect, and check that the right page comes back.
What are the limits?
Before you rely on it
Where a browser RAG pipeline stops
- It lives on one device and is never synced (fait)automatisé
- URLs without your login can fail: use the tab (fait)automatisé
- Workflows that read tabs need the browser open (fait)automatisé
- It saves pages you choose; it does not crawl a site (fait)automatisé
You pick the pages. No site crawler.Ingestion is per source, one after the other.
Version texte
- Where a browser RAG pipeline stops
- It lives on one device and is never synced
- URLs without your login can fail: use the tab
- Workflows that read tabs need the browser open
- It saves pages you choose; it does not crawl a site
If you need to index a whole site every night, or serve answers to thousands of users, a server pipeline is the right tool, and cloud browser or your own browser? covers that choice. If you want an assistant that knows the pages you work with, a browser is the shorter road.
FAQ
Can I use a web browser for a RAG pipeline?
Yes. The browser loads the pages (including ones you are signed in to), and AWFlow splits, embeds and stores them in a knowledge base on your device. An agent connected to that knowledge base then answers from it.
Do I need an embedding API key?
No. The default embedding model runs on your device. You can choose another model for a knowledge base; a knowledge base built with a cloud model can’t be picked in the quick-save menu.
What happens if I save the same page twice?
It replaces the older copy. A URL and an open tab of the same address are one source.
Does it work on pages behind a login?
Yes, when you save them from an open tab with the extension. A pasted URL is fetched without your cookies, so signed-in pages fail that way.
Try it
Install the extension from awflow.io/download, open a page you keep coming back to, right-click and choose Add page to knowledge base. Then ask Aria a question about it, with the knowledge base pinned in the chat.



