# 006: Reverse RAG: cross-page context via encrypted localStorage

**Reporter:** fxhp
**Date:** 2026-03-03
**Priority:** medium
**Status:** open
**Affects:** Browser extension, embedded widget

## Description

When a user browses multiple pages on the same site, the machine learning model should be able to reference content from previously visited pages. Currently each page is isolated: the model only knows the current page's content.

## Goal

Build a reverse RAG system where page summaries are stored in encrypted localStorage (per-domain), and automatically included in the system prompt for new pages. This gives the model a growing knowledge base about the site as the user browses.

## Architecture

1. After generating the intro message for a page, extract a brief summary (title, key topics, 2-3 sentences)
2. Store summaries per-domain in vault-encrypted localStorage: `uncloseai-pageknowledge-{domain}`
3. On new pages, include the last N page summaries in the system prompt as context
4. Summaries should be compact (under 200 tokens each) to avoid context overflow
5. Use the existing `fitPageContent()` budget system to allocate space for cross-page context

## Privacy

All cross-page data must be encrypted via the vault, same as chat history. The user controls their data. No data leaves the device except through explicit LLM requests.

## Dependencies

- Site journey tracking (implemented in storage.js)
- Vault encryption (already working)
- `fitPageContent()` context budgeting (already working)

## Notes

This is the foundation for making uncloseai "unstoppable" on any site. The model gets smarter the more pages you visit, building a personal knowledge graph of the site, all stored locally and encrypted.