Dinary — Your Dinar Diary
See what went into the shopping bag.
Scan a fiscal receipt from Serbia or Montenegro and turn its line items into categorized expenses. Correct a category once and reuse that choice on future purchases. Track spending across currencies, trips, and tags, with optional Google Sheets export and AI-assisted analysis.
Pick your category set |
Review receipt categories  |
Quick entry in any currency |
- Know what you bought. Split a supermarket receipt into item-level spending, review uncertain categories, and teach the app your preferences through corrections.
- Capture it while you remember. Scan QR codes or enter expenses on your phone, including offline. Queued entries sync when the app reconnects.
- Explore spending your way. Combine categories, trip or event context, and tags. Keep original currencies alongside converted totals, export to Sheets, or ask questions in the local analytics dashboard.
Setup and documentation · Install the PWA · Analytics
Under the hood
Corrections become reusable rules. Known items use stored classification rules; unfamiliar or uncertain items go to an LLM. A user correction takes precedence over the model and applies across branches of the same retail chain. Classification failures remain recoverable through retries and review. The classification design explains confidence, alternatives, and manual resolution.
Provider selection and failover are handled by llmbroker, a standalone library for applications that need a pool of LLM providers. Receipt logic stays in Dinary.
A saved expense outlives a failed network call. IndexedDB holds offline entries, and the QR scanner is bundled for offline use. On the server, the SQLite ledger is the source of truth. Google Sheets export runs through a retryable queue, so a spreadsheet outage does not prevent expense capture. See the offline design and Sheets integration.
Analysis has a separate home. The local analytics app uses DuckDB to query a read-only ledger replica, keeping heavy analytics dependencies off the capture server. Its AI can query spending and configure views without editing the ledger. The analytics design covers the dashboard and MCP interface.
Design decisions
Small, frequent lookups shaped the storage layer. In an early synthetic workload, 5,000 mapping lookups took 2.0 seconds with direct SQL and 19.9 seconds through Ibis. That comparison led to SQL files and migrations for the transactional backend. The architecture record documents the workload and trade-offs; the result describes that benchmark, not whole-application performance.
Contributing
Install uv and Node.js, then prepare the full development and test environment:
bash scripts/setup-test-env.sh
npm --prefix webapp ci
uv run inv dev --rebuild
The setup script installs all Python dependency groups and prepares the tools needed by analytics and backup tests. See the development guide for local configuration and platform prerequisites.
uv run inv pre
uv run pytest
npm --prefix webapp test
See AGENTS.md for repository conventions and the deployment guide for hosting.
1 maintainer
Metadata
1 maintainer
Release files for dinary 1.9.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dinary-1.9.0.tar.gz | 6.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dinary-1.9.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.7 MB
Release files / dinary-1.9.0.tar.gz
| Download URL | dinary-1.9.0.tar.gz |
|---|---|
| Size | 6.6 MB |
| Tags | Source |
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| Download URL | dinary-1.9.0-py3-none-any.whl |
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| Size | 147.4 kB |
| Tags | Python 3 |
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twine/7.0.0 CPython/3.13.14
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