NotebookLM keeps your sources in a Google account. SecureRAG keeps them in the tab.
NotebookLM is a hosted notebook: you add sources, it indexes them on Google's servers and answers with citations that open the source passage. SecureRAG keeps the retrieval half of that workflow and moves it into the browser — the same file is parsed, chunked and embedded locally with a 23–25 MB model, and the library never leaves the machine. This page compares the two on storage, citations, offline behaviour, limits and writing quality, and names the four things NotebookLM does better.
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Short answer: sources stay in Google's cloud and need a signed-in account; SecureRAG parses the same files in the tab and answers with the network off.
What NotebookLM genuinely does better
A comparison that only lists the local tool's advantages is advertising. These are the capabilities a single browser tab does not have, and no amount of pipeline tuning changes that:
- Audio Overviews — a spoken two-host summary generated from your sources, which you can listen to away from a screen.
- Sources that stay current. A file imported from Google Drive refreshes every few minutes, and editing the original updates the notebook. Google states that footnotes and comments from Google files are not imported.
- Wide intake, including website URLs and YouTube links alongside PDFs, Google Docs, Slides, Sheets and pasted text.
- Frontier-model prose. Summaries are written by a large cloud model, not by a 0.5B–1.5B quantised one running at 3–8 tokens/s on your CPU.
- Sharing. A notebook can be opened to another account or a team; a static page with no backend has nothing to share.
The decisive difference is where the source lives
Both tools answer a question about a 60-page policy manual in seconds, and the visible difference in the answer quality is small. The invisible difference is the file. In NotebookLM the manual is a row in your Google account: it appears in the notebook list on any device you sign into, it is covered by Google's retention and access policies, and your organisation's admin may control whether the app is available at all on a work account. In SecureRAG the manual is read through the browser File API into memory, cut into ~700-character chunks with 15% overlap, embedded with bge-small-zh-v1.5 (~25 MB) or all-MiniLM-L6-v2 (~23 MB) in a Web Worker, and written to IndexedDB in your browser profile. Files never leave the device.
| Question | NotebookLM | SecureRAG |
|---|---|---|
| Sign-in | A personal or work Google Account, signed in; age verification and regional availability apply | None — no account, no tracking pixels |
| Where sources are stored | In the notebook, in Google's cloud; Drive sources re-sync every few minutes | In IndexedDB, in your browser profile; clearing site data deletes them |
| Offline use | Not available | Works offline once the model is cached |
| Training on your content | Governed by Google's terms for the account type you use; read the current policy | Not used for training — there is no server to train on |
| Citations | Numbered inline citations that open the source passage | A per-sentence citation back to the chunk; PDF chunks keep their page numbers, DOCX chunks keep heading levels |
| Limits | Per-plan source and notebook limits; see Google's help pages | ≤25 MB per file, ≤40 files, ≤200 MB per library, ≤20,000 chunks |
| Answer engine | A large cloud model, quality tied to the plan | Extractive by default; optional Qwen2.5 0.5B–1.5B at 4-bit (400 MB–1.0 GB) writing from the retrieved passages |
Citation granularity, on a concrete question
Ask both tools "what notice period applies to a resignation during probation?" and click the citation. NotebookLM lands you in the right passage of the right source. SecureRAG lands you in a specific chunk of a specific file: the page number is preserved for PDFs because parsing runs through PDF.js, and the heading path is preserved for DOCX because the parser reads the archive with jszip and keeps heading levels. The difference is visible when an answer combines four passages — the local version shows which sentence came from which chunk, because the default answering step selects text rather than generating it. In the optional generation tier, the same citations are attached before the text reaches you.
Where SecureRAG loses to NotebookLM
- No audio overview, no mind map, no generated briefing document. If those are the deliverable, this is the wrong tool.
- No sync between devices. Import the same four PDFs on a second machine and they are indexed again from zero.
- No sharing. There is no link to send, because there is no server.
- Plain prose. Asked to summarise a 60-page manual, a 0.5B–1.5B quantised model is visibly weaker than a cloud model, and on a machine without WebGPU it runs at roughly 3–8 tokens/s.
- No OCR. A scanned PDF with no text layer is detected during ingestion and reported, rather than quietly returning an empty answer.
- Stricter limits. A single file over 25 MB is refused outright, and a library stops at 40 files, 200 MB or 20,000 chunks — whichever comes first. On mobile browsers the cap is lower, because memory is tighter.
Moving a notebook workflow onto the local tool
List the sources you actually re-read
Open the notebook and sort by what you cited this month. Most notebooks hold twenty to forty sources and three of them carry the answers you keep coming back to.
Download originals, not exports
A Google Doc exported to DOCX keeps its heading levels, and the parser preserves them. PDFs keep their page numbers. Sheets export to CSV and are read as plain text — column alignment is not preserved, which matters for wide financial tables.
Import and watch the counters
The library caps at 40 files and 200 MB total, with 20,000 chunks. The tool reports progress per file, so a refused file is named instead of failing the whole batch.
Set strictness to "documents only" for the first three questions
You want to see "not found in your documents" once, on a question you know the text cannot answer, before you trust an answer that it can.
Verify with the network off
Switch DevTools to Offline and repeat a question. Then clear site data and confirm the library is empty. Both checks take under a minute and are worth doing on your own machine rather than accepting our word for it.
Can NotebookLM be used offline?
No. Sources are indexed and answered in Google's cloud, which needs a signed-in Google Account and a network connection. SecureRAG caches its embedding model after the first load and keeps answering with the network switched off.
Does NotebookLM train on my sources?
That depends on the account type and on Google's current terms, published on Google's own pages. We do not restate those terms here because they change. SecureRAG has no server side, so the question does not arise — there is nowhere for a copy to be used.
Is SecureRAG a full replacement for NotebookLM?
No. It replaces retrieval and citation over documents you own. It does not replace audio overviews, Drive source syncing, notebook sharing, or the prose quality of a frontier model.
What happens to my library if I clear browser data?
It is deleted, completely and immediately — IndexedDB is the only store. That is the intended behaviour rather than a defect: nothing is kept on a server for you, so there is no recovery path, and no export button is needed to remove it.