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Add architecture documentation page #5

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28 changes: 28 additions & 0 deletions docs/architecture.md
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# Architecture Overview

This project implements a modular delta load ETL pipeline for bookmark data.
It is composed of several pieces that work together to fetch, process, enrich,
and monitor bookmark information.

## Core Pipeline
- **`BookmarkDeltaETL`**: orchestrates extraction, transformation, and loading
while tracking changes between runs.
- **Fetchers**: pluggable modules such as `RaindropFetcher` and `GitHubFetcher`
pull bookmark data from different services.
- **`AIBookmarkEnricher`**: adds semantic analysis and metadata using AI
models.

## Scheduling and Monitoring
- **`DeltaScheduler`**: runs the pipeline on a schedule, handles source
fetching, optional AI enrichment, and report generation.
- **`delta-monitor.ts`**: collects statistics about processed bookmarks to aid
in monitoring and reporting.
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@gemini-code-assist gemini-code-assist bot Aug 8, 2025

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medium

For consistency with the other components listed, which use class names (e.g., BookmarkDeltaETL, DeltaScheduler), it would be clearer to refer to the DeltaMonitor class instead of the filename delta-monitor.ts.

Suggested change
- **`delta-monitor.ts`**: collects statistics about processed bookmarks to aid
in monitoring and reporting.
- **`DeltaMonitor`**: collects statistics about processed bookmarks to aid
in monitoring and reporting.


## Typical Workflow
1. Fetch bookmark sources.
2. Run the delta ETL to detect additions and updates.
3. Enrich changed bookmarks with AI-generated insights.
4. Generate daily reports and clean up old data.

For more usage examples, see the main [README](../README.md) and
[Delta Load Guide](../delta-load-guide.md).