43e1f3d6f5
Mirrors Phase 1 + 2 doc layout. CLAUDE.md captures architecture, the 7-method engine API, persisted models, controllers, OWL frontend, performance baselines (Tasks 23 + 41 numbers), test counts (140), and Phase 3.5 backlog. UPGRADE_NOTES.md anchors the Odoo 19 reference and records V19 deprecations applied. README.md is the user-facing intro. Made-with: Cursor
131 lines
6.1 KiB
Markdown
131 lines
6.1 KiB
Markdown
# fusion_accounting_assets — Cursor / Claude Context
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## Purpose
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AI-augmented fixed asset management with depreciation schedules — a
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Fusion-native replacement for (and coexisting with) Odoo Enterprise's
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`account_asset` module. Ships in Phase 3 of the fusion_accounting roadmap.
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## Architecture
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Hybrid: the engine (`fusion.asset.engine`, AbstractModel) is the SINGLE
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write surface for the asset lifecycle. Everything else (controllers, OWL
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widget, AI tools, wizards, cron) routes through the engine's 7-method
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public API:
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- `compute_depreciation_schedule(asset, recompute=False)`
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- `post_depreciation_entry(asset, period_date=None)`
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- `dispose_asset(asset, sale_amount=0, sale_date=None, sale_partner=None, disposal_type='sale')`
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- `partial_sale(asset, sold_amount, sold_qty=None, sale_date=None, sale_partner=None)`
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- `pause_asset(asset, pause_date=None)`
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- `resume_asset(asset, resume_date=None)`
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- `reverse_disposal(asset)`
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Pure-Python services live in `services/`:
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- `depreciation_methods` — straight_line, declining_balance, units_of_production
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- `prorate` — first/last-period prorating: full_month, days_365, days_period
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- `salvage_value` — % of cost, fixed amount, zero
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- `anomaly_detection` — variance vs expected schedule, low utilization
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- `useful_life_predictor` + `useful_life_prompt` — LLM-suggested useful life with templated fallback
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Persisted models in `models/`:
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- `fusion.asset` — main model, state machine: draft → running → paused → disposed
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- `fusion.asset.depreciation.line` — board lines
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- `fusion.asset.category` — templates
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- `fusion.asset.disposal` — disposal records
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- `fusion.asset.anomaly` — flagged variances
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- `fusion.asset.book.values.mv` — pre-aggregated materialized view
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- `fusion.asset.engine` — AbstractModel (the API)
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- `fusion.assets.cron` — cron handlers (post depreciations, MV refresh, anomaly scan)
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- `account.move.line` (inherits) — adds `fusion_asset_id` linkage
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- `fusion.migration.wizard` (inherits in `models/`) — adds asset backfill step
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Wizards (TransientModel) in `wizards/`:
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- `fusion.create.asset.wizard` — assisted creation with AI useful-life suggestion
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- `fusion.disposal.wizard` — full disposal flow
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- `fusion.partial.sale.wizard` — partial-quantity disposal
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- `fusion.depreciation.run.wizard` — period close runner
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Controller: `controllers/assets_controller.py` exposes 8 JSON-RPC
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endpoints under `/fusion/assets/*` (list, get_detail, compute_schedule,
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post_depreciation, dispose, get_anomalies, suggest_useful_life,
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get_partner_history). All calls route through the engine.
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OWL frontend: `static/src/`
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- `services/assets_service.js` — central reactive state + RPC wrappers
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- `views/asset_dashboard/*` — top-level dashboard view
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- `components/asset_card`, `asset_detail_panel`, `depreciation_board`,
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`disposal_dialog`, `ai_useful_life_panel`, `anomaly_strip` — 6 components
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- `scss/_variables.scss` + `assets.scss` + `dark_mode.scss`
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- `tours/assets_tours.js` — 5 OWL tour smoke tests
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## Coexistence
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When `account_asset` is installed the Asset Management menu hides via
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`fusion_accounting_core.group_fusion_show_when_enterprise_absent` (a
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computed group). The engine + AI tools remain available for the chat.
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The migration wizard backfills `fusion.asset` from existing
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`account.asset` records (verified live: 2 records, Task 35).
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## Conventions
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- **V19 deprecations to avoid:** `_sql_constraints` (use
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`models.Constraint`), `@api.depends('id')` (raises
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`NotImplementedError`), `@route(type='json')` (use `type='jsonrpc'`),
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`numbercall` field on `ir.cron` (removed), `groups_id` on `res.users`
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(use `all_group_ids` for searching), `users` field on `res.groups`
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(use `user_ids`), `groups_id` on `ir.ui.menu` (use `group_ids`).
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- **Materialized view refresh:** `fusion.asset.book.values.mv` is
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refreshed by cron (REFRESH CONCURRENTLY in an autocommit cursor since
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it can't run inside a regular Odoo transaction).
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- **Provider routing:** AI features look up
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`fusion_accounting.provider.asset_useful_life`, falling back to
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`fusion_accounting.provider.default`. When neither is set the
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templated keyword fallback in `useful_life_predictor` keeps the
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feature usable offline.
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## Performance baseline (Tasks 23 + 41)
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| Operation | P95 | Budget | Headroom |
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|------------------------------------|-------|----------|----------|
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| `engine.compute_schedule` (10yr SL)| 1ms | 500ms | 500x |
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| `engine.post_depreciation_entry` | <1ms | 300ms | huge |
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| `engine.dispose_asset` | 5ms | 300ms | 60x |
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| `controller.list` (35 assets) | 42ms | 300ms | 7x |
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| `controller.get_detail` | 40ms | 500ms | 12x |
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All Phase 3 perf metrics are within 1x of budget; no optimization was
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needed at ship (Task 42 skipped per the conditional rule).
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## Test counts (Phase 3 ship)
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- 140 logical tests total in fusion_accounting_assets
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- 0 failures, 0 errors
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- Coverage includes: 4 engine benchmarks + 1 controller benchmark
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(tagged `benchmark`), 1 local LLM smoke (tagged `local_llm`, skips
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when no LLM), 5 OWL tour tests (tagged `tour`, skip without
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websocket-client), Hypothesis property tests on the engine,
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integration tests on the public API, controller round-trip tests, MV
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shape tests.
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## Known concerns / Phase 3.5 backlog
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- Sub-annual depreciation frequency (currently annual only)
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- Units-of-production assumes even per-period units
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- Disposal journal entry not yet created — `dispose_asset` writes the
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`fusion.asset.disposal` record but not the cash / gain-loss move
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- Multi-currency, allocation rules, and analytic tags for depreciation
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moves are out of scope for Phase 3
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- Partial-sale child asset is created with no own depreciation schedule
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pre-disposal
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- Migration wizard inheritance lives in `models/` rather than
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`wizards/` (small inconsistency with the rest of the wizard layout —
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intentional to keep ORM ordering simple)
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- `useful_life_predictor` always returns a usable dict (templated
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fallback when LLM absent), so callers can't distinguish "AI said so"
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from "fallback fired"; the `confidence` key is the only signal
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