feat(fusion_accounting_assets): AI useful life predictor + prompt

Made-with: Cursor

fusion_accounting/PHASE_3_PLAN.md

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+# Phase 3 — Fusion Accounting Assets Implementation Plan
+
+**Module:** `fusion_accounting_assets`
+**Branch:** `fusion_accounting/phase-3-assets`
+**Pre-phase tag:** `fusion_accounting/pre-phase-3`
+**Estimated tasks:** ~50
+**Reference:** `/Users/gurpreet/Github/RePackaged-Odoo/accounting/account_asset/` (~2258 LOC Python)
+
+## Goal
+
+Replace Odoo Enterprise's `account_asset` module — asset management with depreciation schedules, disposal, partial sale, and reporting. CORE scope: 3 depreciation methods (straight-line, declining balance, units of production), full asset lifecycle, depreciation board, disposal/sale wizards. AI augmentation: utilization anomaly detection + AI-suggested useful life from invoice context. Coexists with Enterprise.
+
+## Architecture (HYBRID engine, Phase 1+2 pattern)
+
+```
+fusion.asset.engine (AbstractModel)         ← shared primitives
+├── compute_depreciation_schedule(asset, recompute=False)
+├── post_depreciation_entry(asset, period)
+├── dispose_asset(asset, *, sale_amount, sale_date, sale_partner=None)
+├── partial_sale(asset, *, sold_amount, sold_qty, sale_date)
+├── pause_asset(asset, pause_date)
+├── resume_asset(asset, resume_date)
+└── reverse_disposal(asset)
+
+services/                                    ← pure-Python
+├── depreciation_methods.py  → straight_line, declining_balance, units_of_production
+├── prorate.py               → first/last period prorating (calendar/365/etc.)
+├── salvage_value.py         → end-of-life value math
+├── anomaly_detection.py     → utilization variance vs expected
+├── useful_life_predictor.py → LLM-suggested useful life from invoice description
+└── useful_life_prompt.py    → provider-agnostic LLM prompt
+
+models/
+├── fusion_asset.py                  → main fusion.asset model
+├── fusion_asset_depreciation_line.py → depreciation board lines
+├── fusion_asset_category.py          → categories with default settings
+├── fusion_asset_disposal.py          → disposal records
+├── fusion_asset_anomaly.py           → flagged utilization issues
+├── fusion_asset_engine.py            → AbstractModel orchestrator
+└── account_move.py                   → inherit (link to asset, generate from invoice)
+
+controllers/assets_controller.py            ← 8 JSON-RPC endpoints
+├── /fusion/assets/list                      → paginated asset list with filters
+├── /fusion/assets/get_detail                → single asset with full schedule
+├── /fusion/assets/compute_schedule          → recompute depreciation board
+├── /fusion/assets/post_depreciation         → run periodic depreciation cron
+├── /fusion/assets/dispose                   → dispose an asset
+├── /fusion/assets/get_anomalies             → list flagged variances
+├── /fusion/assets/suggest_useful_life       → AI suggest useful life
+└── /fusion/assets/get_partner_history       → asset-related partner history
+
+static/src/
+├── scss/                          ← asset-specific design tokens
+├── services/assets_service.js     ← reactive state + RPC wrappers
+├── views/asset_dashboard/         ← top-level OWL controller
+└── components/                    ← asset_card, depreciation_board, disposal_dialog,
+                                     ai_useful_life_panel, anomaly_strip
+```
+
+## Coexistence
+
+`group_fusion_show_when_enterprise_absent` from `fusion_accounting_core`. Asset menu only visible when `account_asset` NOT installed. Engine + AI tools always available.
+
+## Tasks (50 total)
+
+### Group 1: Foundation (1-2)
+1. Safety net (DONE)
+2. Plan doc + module skeleton
+
+### Group 2: Pure-Python services TDD (3-7)
+3. `services/depreciation_methods.py` — straight_line + declining_balance + units_of_production (TDD)
+4. `services/prorate.py` — first/last period prorating
+5. `services/salvage_value.py` — end-of-life math
+6. `services/anomaly_detection.py` — utilization variance
+7. `services/useful_life_predictor.py` + `useful_life_prompt.py` — LLM integration
+
+### Group 3: Persisted models (8-13)
+8. `models/fusion_asset.py` — main asset model with state machine
+9. `models/fusion_asset_depreciation_line.py` — depreciation board lines
+10. `models/fusion_asset_category.py` — categories with defaults
+11. `models/fusion_asset_disposal.py` — disposal records
+12. `models/fusion_asset_anomaly.py` — flagged anomalies
+13. `models/account_move.py` (inherit) — link asset to invoice
+
+### Group 4: Engine (14-15)
+14. `models/fusion_asset_engine.py` — 7-method API
+15. Engine integration tests (compute_schedule + post_depreciation + dispose end-to-end)
+
+### Group 5: Backend wiring (16-19)
+16. JSON-RPC controller (8 endpoints)
+17. AssetsAdapter wiring `_via_fusion` paths
+18. 5 new AI tools
+19. Cron — daily depreciation post + monthly anomaly scan
+
+### Group 6: Tests + perf (20-23)
+20. Property-based tests (Hypothesis: schedule sums == cost - salvage)
+21. Integration tests — straight-line + declining-balance + units-of-production
+22. Materialized view for asset book values (perf)
+23. Performance benchmarks
+
+### Group 7: Frontend OWL (24-31)
+24. SCSS tokens + main asset stylesheet (light + dark)
+25. `assets_service.js` (reactive state + RPC wrappers)
+26. `asset_dashboard` (top-level kanban + summary)
+27. `asset_card` (one asset summary card)
+28. `asset_detail_panel` (right-side: schedule, history, AI suggestions)
+29. `depreciation_board` (table view of schedule with edit chevrons)
+30. `disposal_dialog` (sale/scrap wizard)
+31. Fusion-only: `ai_useful_life_panel` + `anomaly_strip`
+
+### Group 8: Wizards (32-35)
+32. Asset creation wizard (from invoice line)
+33. Disposal wizard (sale, scrap, donation)
+34. Partial sale wizard
+35. Period picker for depreciation runs
+
+### Group 9: Migration + coexistence (36-39)
+36. Migration wizard inheritance — backfill from account.asset rows
+37. Audit report PDF (per-company asset count, total NBV, etc.)
+38. Menu + window action with coexistence group filter
+39. Coexistence test
+
+### Group 10: Final tests + polish (40-50)
+40. 5 OWL tour tests
+41. Performance benchmarks (P95: schedule compute < 500ms, board render < 200ms)
+42. Optimize if benchmarks fail (conditional)
+43. Local LLM compat test for useful_life_predictor
+44. Update meta-module manifest
+45. CLAUDE.md, UPGRADE_NOTES.md, README.md
+46. End-to-end smoke + tag phase-3-complete + push
+47-50. Reserved for inherited features: account_move integration, draft journal entries, post-on-confirm flow, fiscal-year-aware proration
+
+## Performance Targets (P95)
+
+- `compute_schedule` (10-year asset): <500ms
+- `post_depreciation_entry`: <200ms
+- `dispose_asset`: <300ms
+- Controller `list`: <300ms
+- Controller `get_detail`: <500ms
+
+## V19 Conventions (carried from Phase 1+2)
+
+- `models.Constraint` not `_sql_constraints`
+- No `@api.depends('id')` on stored compute fields
+- `@route(type='jsonrpc')` not `type='json'`
+- `ir.cron` has no `numbercall` field
+- `res.groups.user_ids` not `users`
+- `ir.ui.menu.group_ids` not `groups_id`
+- `models.Constraint` for unique-keys
+- `env.flush_all()` before MV REFRESH
+- REFRESH MATERIALIZED VIEW CONCURRENTLY needs autocommit cursor
+
+## Test Targets
+
+Match Phase 1+2 test pyramid:
+- Unit (pure-Python services)
+- Integration (engine end-to-end)
+- Property-based (Hypothesis: schedule total invariants)
+- Controller (HttpCase JSON-RPC)
+- MV correctness
+- Performance benchmarks (tagged 'benchmark')
+- OWL tours (tagged 'tour')
+- Local LLM smoke (tagged 'local_llm')
+
+Phase 1+2 final: 287 tests. Phase 3 target: ~140-180 additional → ~430-470 total.

fusion_accounting_assets/__init__.py

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+from . import services

fusion_accounting_assets/__manifest__.py

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+{
+    'name': 'Fusion Accounting Assets',
+    'version': '19.0.1.0.5',
+    'category': 'Accounting/Accounting',
+    'summary': 'AI-augmented asset management with depreciation schedules.',
+    'description': """
+Fusion Accounting Assets
+========================
+
+A Fusion-native replacement for Odoo Enterprise's account_asset module.
+
+CORE scope (Phase 3):
+- 3 depreciation methods: straight-line, declining balance, units of production
+- Asset lifecycle: draft -> running -> paused -> disposed
+- Depreciation board with editable schedule
+- Disposal (sale, scrap, donation) + partial sale wizards
+- Daily cron for posting periodic depreciation
+
+AI augmentation:
+- Anomaly detection on utilization vs expected
+- AI-suggested useful life from invoice context (LLM)
+
+Coexists with Enterprise: when account_asset is installed, the Fusion
+menu hides; the engine + AI tools remain available for the chat.
+""",
+    'author': 'Fusion Accounting',
+    'license': 'LGPL-3',
+    'depends': [
+        'fusion_accounting_core',
+        'fusion_accounting_ai',
+        'account',
+    ],
+    'data': [
+        'security/ir.model.access.csv',
+    ],
+    'assets': {
+        'web.assets_backend': [
+        ],
+    },
+    'installable': True,
+    'auto_install': False,
+    'application': False,
+    'icon': '/fusion_accounting_assets/static/description/icon.png',
+}

fusion_accounting_assets/security/ir.model.access.csv

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+id,name,model_id:id,group_id:id,perm_read,perm_write,perm_create,perm_unlink

fusion_accounting_assets/services/__init__.py

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+from . import depreciation_methods
+from . import prorate
+from . import salvage_value
+from . import anomaly_detection
+from . import useful_life_prompt
+from . import useful_life_predictor

fusion_accounting_assets/services/anomaly_detection.py

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+"""Asset utilization anomaly detection.
+
+Flags assets where actual usage / posted depreciation deviates significantly
+from the expected schedule. Three signal types:
+- behind_schedule: actual depreciation < expected by > threshold pct
+- ahead_of_schedule: actual > expected (over-depreciated; scrap or recompute)
+- low_utilization: units_used < expected_units_per_period (waste alert)
+"""
+
+from dataclasses import dataclass
+
+
+@dataclass
+class AssetAnomaly:
+    asset_id: int
+    asset_name: str
+    anomaly_type: str
+    severity: str
+    expected: float
+    actual: float
+    variance_pct: float
+    detail: str
+
+    def to_dict(self):
+        return {
+            'asset_id': self.asset_id,
+            'asset_name': self.asset_name,
+            'anomaly_type': self.anomaly_type,
+            'severity': self.severity,
+            'expected': self.expected,
+            'actual': self.actual,
+            'variance_pct': self.variance_pct,
+            'detail': self.detail,
+        }
+
+
+DEFAULT_LOW_THRESHOLD_PCT = 10.0
+DEFAULT_MEDIUM_THRESHOLD_PCT = 25.0
+DEFAULT_HIGH_THRESHOLD_PCT = 50.0
+
+
+def detect_schedule_variance(*, asset_id: int, asset_name: str,
+                             expected_accumulated: float,
+                             actual_accumulated: float) -> AssetAnomaly | None:
+    """Compare expected accumulated depreciation vs actual posted."""
+    if expected_accumulated <= 0:
+        return None
+    variance_amt = actual_accumulated - expected_accumulated
+    variance_pct = abs(variance_amt) / expected_accumulated * 100
+    if variance_pct < DEFAULT_LOW_THRESHOLD_PCT:
+        return None
+    direction = 'ahead_of_schedule' if variance_amt > 0 else 'behind_schedule'
+    if variance_pct >= DEFAULT_HIGH_THRESHOLD_PCT:
+        severity = 'high'
+    elif variance_pct >= DEFAULT_MEDIUM_THRESHOLD_PCT:
+        severity = 'medium'
+    else:
+        severity = 'low'
+    detail = f"Posted ${actual_accumulated:,.2f} vs expected ${expected_accumulated:,.2f}"
+    return AssetAnomaly(
+        asset_id=asset_id,
+        asset_name=asset_name,
+        anomaly_type=direction,
+        severity=severity,
+        expected=expected_accumulated,
+        actual=actual_accumulated,
+        variance_pct=round(variance_pct, 1),
+        detail=detail,
+    )
+
+
+def detect_low_utilization(*, asset_id: int, asset_name: str,
+                           expected_units: float,
+                           actual_units: float) -> AssetAnomaly | None:
+    """For units-of-production assets: flag low actual usage."""
+    if expected_units <= 0:
+        return None
+    if actual_units >= expected_units * 0.9:
+        return None
+    deficit_pct = (expected_units - actual_units) / expected_units * 100
+    if deficit_pct >= 50:
+        severity = 'high'
+    elif deficit_pct >= 25:
+        severity = 'medium'
+    else:
+        severity = 'low'
+    return AssetAnomaly(
+        asset_id=asset_id,
+        asset_name=asset_name,
+        anomaly_type='low_utilization',
+        severity=severity,
+        expected=expected_units,
+        actual=actual_units,
+        variance_pct=round(deficit_pct, 1),
+        detail=f"Used {actual_units:.0f} of expected {expected_units:.0f} units",
+    )

fusion_accounting_assets/services/depreciation_methods.py

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+"""Depreciation method primitives.
+
+Three methods supported:
+- straight_line: equal periodic charge over useful_life
+- declining_balance: % per period of remaining book value
+- units_of_production: charge proportional to units used / total units expected
+
+All return a list of DepreciationStep dataclasses (period_index, period_amount,
+accumulated_depreciation, book_value_at_end). Total depreciation always
+sums to (cost - salvage_value), within 1-cent rounding tolerance.
+"""
+
+from dataclasses import dataclass
+from typing import Literal
+
+
+Method = Literal['straight_line', 'declining_balance', 'units_of_production']
+
+
+@dataclass
+class DepreciationStep:
+    period_index: int
+    period_amount: float
+    accumulated_depreciation: float
+    book_value_at_end: float
+
+
+def straight_line(*, cost: float, salvage_value: float = 0.0,
+                  n_periods: int) -> list[DepreciationStep]:
+    """Equal charge per period: (cost - salvage) / n_periods.
+
+    Last period absorbs rounding so total == cost - salvage exactly.
+    """
+    if n_periods < 1:
+        return []
+    depreciable = cost - salvage_value
+    per_period = round(depreciable / n_periods, 2)
+    steps = []
+    accumulated = 0.0
+    for i in range(n_periods):
+        if i == n_periods - 1:
+            amount = round(depreciable - accumulated, 2)
+        else:
+            amount = per_period
+        accumulated = round(accumulated + amount, 2)
+        book = round(cost - accumulated, 2)
+        steps.append(DepreciationStep(
+            period_index=i,
+            period_amount=amount,
+            accumulated_depreciation=accumulated,
+            book_value_at_end=book,
+        ))
+    return steps
+
+
+def declining_balance(*, cost: float, salvage_value: float = 0.0,
+                      n_periods: int, rate: float) -> list[DepreciationStep]:
+    """Apply `rate` (e.g. 0.20 = 20%) to remaining book each period.
+
+    Switches to straight-line when straight-line would deplete remaining book
+    faster (typical Odoo behavior). Last step caps at salvage_value.
+    """
+    if n_periods < 1 or rate <= 0:
+        return []
+    if rate >= 1:
+        # Pathological: 100%+ rate. Charge full depreciable amount in period 0.
+        depreciable = round(cost - salvage_value, 2)
+        return [DepreciationStep(0, depreciable, depreciable, round(salvage_value, 2))]
+    steps = []
+    book = cost
+    accumulated = 0.0
+    for i in range(n_periods):
+        remaining_periods = n_periods - i
+        db_amount = round(book * rate, 2)
+        sl_amount = round((book - salvage_value) / remaining_periods, 2) if remaining_periods else 0.0
+        amount = max(db_amount, sl_amount)
+        if book - amount < salvage_value:
+            amount = round(book - salvage_value, 2)
+        accumulated = round(accumulated + amount, 2)
+        book = round(book - amount, 2)
+        steps.append(DepreciationStep(
+            period_index=i,
+            period_amount=amount,
+            accumulated_depreciation=accumulated,
+            book_value_at_end=book,
+        ))
+        if book <= salvage_value:
+            break
+    return steps
+
+
+def units_of_production(*, cost: float, salvage_value: float = 0.0,
+                        total_units_expected: float,
+                        units_per_period: list[float]) -> list[DepreciationStep]:
+    """Charge per period = (units_used / total_expected) * (cost - salvage)."""
+    if total_units_expected <= 0:
+        return []
+    depreciable = cost - salvage_value
+    per_unit = depreciable / total_units_expected
+    steps = []
+    accumulated = 0.0
+    for i, units in enumerate(units_per_period):
+        amount = round(units * per_unit, 2)
+        if accumulated + amount > depreciable:
+            amount = round(depreciable - accumulated, 2)
+        accumulated = round(accumulated + amount, 2)
+        book = round(cost - accumulated, 2)
+        steps.append(DepreciationStep(
+            period_index=i,
+            period_amount=amount,
+            accumulated_depreciation=accumulated,
+            book_value_at_end=book,
+        ))
+        if accumulated >= depreciable:
+            break
+    return steps

fusion_accounting_assets/services/prorate.py

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+"""Prorating helpers for first-period and last-period depreciation.
+
+When an asset starts mid-month, the first period charges only a fraction
+of the full period_amount. Three conventions:
+- 'full_month': always charge full month (no proration)
+- 'days_365': pro-rate by actual days / 365
+- 'days_period': pro-rate by actual days in period / total days in period
+"""
+
+from datetime import date
+from typing import Literal
+
+
+ProrateConvention = Literal['full_month', 'days_365', 'days_period']
+
+
+def prorate_factor(*, period_start: date, period_end: date,
+                   asset_start: date,
+                   convention: ProrateConvention = 'days_period') -> float:
+    """Return a 0..1 factor for how much of `period`'s depreciation
+    applies to an asset that started on `asset_start`."""
+    if convention == 'full_month':
+        return 1.0
+    if asset_start <= period_start:
+        return 1.0
+    if asset_start > period_end:
+        return 0.0
+    actual_days = (period_end - asset_start).days + 1
+    if convention == 'days_365':
+        return actual_days / 365.0
+    if convention == 'days_period':
+        period_days = (period_end - period_start).days + 1
+        return actual_days / period_days
+    raise ValueError(f"Unknown convention: {convention}")

fusion_accounting_assets/services/salvage_value.py

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+"""Salvage value (scrap value) calculation helpers.
+
+Most clients use straight % of cost; some use fixed dollar amounts.
+"""
+
+from dataclasses import dataclass
+from typing import Literal
+
+
+SalvageMethod = Literal['percentage', 'fixed', 'zero']
+
+
+@dataclass
+class SalvageConfig:
+    method: SalvageMethod
+    value: float = 0.0
+
+
+def compute_salvage_value(*, cost: float, config: SalvageConfig) -> float:
+    """Compute end-of-life salvage value."""
+    if config.method == 'zero':
+        return 0.0
+    if config.method == 'percentage':
+        return round(cost * config.value / 100, 2)
+    if config.method == 'fixed':
+        return round(config.value, 2)
+    raise ValueError(f"Unknown salvage method: {config.method}")
+
+
+def remaining_useful_life_value(*, current_book: float, salvage: float,
+                                periods_used: int, total_periods: int) -> float:
+    """Estimate remaining value if asset is sold/scrapped now."""
+    if total_periods <= 0:
+        return current_book
+    if periods_used >= total_periods:
+        return salvage
+    remaining_pct = (total_periods - periods_used) / total_periods
+    return round(salvage + (current_book - salvage) * remaining_pct, 2)

fusion_accounting_assets/services/useful_life_predictor.py

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+"""AI-suggested useful life from invoice context.
+
+Wraps useful_life_prompt + an LLMProvider. Returns a dict per the prompt's
+output contract. Templated fallback when no provider configured.
+"""
+
+import json
+import logging
+import re
+
+_logger = logging.getLogger(__name__)
+
+
+# Templated fallback rules: (regex, years, method, rationale)
+FALLBACK_RULES = [
+    (r'\b(computer|laptop|monitor|server|workstation)\b', 4, 'straight_line', 'Computer hardware'),
+    (r'\b(furniture|desk|chair|cabinet)\b', 7, 'straight_line', 'Furniture'),
+    (r'\b(vehicle|truck|car|van)\b', 5, 'declining_balance', 'Vehicle (CRA Class 10)'),
+    (r'\b(building|warehouse)\b', 30, 'straight_line', 'Building'),
+    (r'\b(software|license)\b', 4, 'straight_line', 'Software license'),
+    (r'\b(equipment|machinery|machine)\b', 10, 'straight_line', 'Manufacturing equipment'),
+    (r'\b(leasehold improvement)\b', 5, 'straight_line', 'Leasehold improvements'),
+]
+FALLBACK_DEFAULT = (5, 'straight_line', 'Generic fixed asset (default)')
+
+
+def predict_useful_life(env, *, description: str, amount: float = None,
+                        partner_name: str = None, provider=None) -> dict:
+    """Suggest useful life + method via LLM, with templated fallback."""
+    if provider is None:
+        provider = _get_provider(env)
+    if provider is None:
+        return _templated_fallback(description)
+
+    try:
+        from .useful_life_prompt import build_prompt
+        system, user = build_prompt(
+            description=description, amount=amount, partner_name=partner_name,
+        )
+        response = provider.complete(
+            system=system,
+            messages=[{'role': 'user', 'content': user}],
+            max_tokens=400, temperature=0.1,
+        )
+        content = response.get('content') if isinstance(response, dict) else response
+        parsed = json.loads(content)
+        for key in ('useful_life_years', 'depreciation_method', 'rationale'):
+            if key not in parsed:
+                raise ValueError(f"Missing key: {key}")
+        parsed.setdefault('confidence', 0.7)
+        return parsed
+    except Exception as e:
+        _logger.warning("Useful life LLM prediction failed (%s); falling back", e)
+        return _templated_fallback(description)
+
+
+def _templated_fallback(description: str) -> dict:
+    """Pattern-match keyword rules. Always returns a usable dict."""
+    desc_lower = description.lower() if description else ''
+    for pattern, years, method, rationale in FALLBACK_RULES:
+        if re.search(pattern, desc_lower):
+            return {
+                'useful_life_years': years,
+                'depreciation_method': method,
+                'rationale': rationale,
+                'confidence': 0.5,
+            }
+    years, method, rationale = FALLBACK_DEFAULT
+    return {
+        'useful_life_years': years,
+        'depreciation_method': method,
+        'rationale': rationale,
+        'confidence': 0.3,
+    }
+
+
+def _get_provider(env):
+    """Look up provider for 'asset_useful_life' feature."""
+    param = env['ir.config_parameter'].sudo()
+    name = param.get_param('fusion_accounting.provider.asset_useful_life')
+    if not name:
+        name = param.get_param('fusion_accounting.provider.default')
+    if not name:
+        return None
+    try:
+        from odoo.addons.fusion_accounting_ai.services.adapters.openai_adapter import OpenAIAdapter
+        from odoo.addons.fusion_accounting_ai.services.adapters.claude import ClaudeAdapter
+    except ImportError:
+        return None
+    if name.startswith('openai'):
+        return OpenAIAdapter(env)
+    elif name.startswith('claude'):
+        return ClaudeAdapter(env)
+    return None

fusion_accounting_assets/services/useful_life_prompt.py

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+"""LLM prompt builder for AI-suggested useful life from invoice description.
+
+Output contract:
+{
+  "useful_life_years": <int>,
+  "depreciation_method": "straight_line" | "declining_balance" | "units_of_production",
+  "rationale": "<short explanation>",
+  "confidence": <float 0-1>
+}
+"""
+
+
+SYSTEM_PROMPT = """You are an experienced accountant. Given an invoice line
+description for a fixed asset, suggest the appropriate useful life in years
+and depreciation method based on common accounting standards (IFRS / GAAP / CRA).
+
+Respond ONLY with valid JSON of this exact shape:
+{
+  "useful_life_years": <integer>,
+  "depreciation_method": "straight_line" | "declining_balance" | "units_of_production",
+  "rationale": "<one or two sentence explanation>",
+  "confidence": <float between 0 and 1>
+}
+
+Common useful-life conventions:
+- Furniture: 7 years, straight-line
+- Office equipment: 5 years, straight-line
+- Computers: 3-4 years, straight-line or declining
+- Vehicles: 5 years, declining-balance (CRA Class 10 30%)
+- Buildings: 25-40 years, straight-line
+- Manufacturing equipment: 10-15 years, units of production if measurable
+- Software (licenses): 3-5 years, straight-line
+- Leasehold improvements: lesser of lease term or useful life
+
+Do NOT include markdown code fences. Do NOT include any prose outside the JSON."""
+
+
+def build_prompt(*, description: str, amount: float = None,
+                 partner_name: str = None) -> tuple[str, str]:
+    """Return (system, user) prompt tuple."""
+    parts = [f"INVOICE LINE: {description}"]
+    if amount is not None:
+        parts.append(f"AMOUNT: ${amount:,.2f}")
+    if partner_name:
+        parts.append(f"VENDOR: {partner_name}")
+    parts.append("")
+    parts.append("Suggest the useful life and depreciation method per the system prompt.")
+    return (SYSTEM_PROMPT, "\n".join(parts))

fusion_accounting_assets/tests/__init__.py

@@ -0,0 +1,5 @@
+from . import test_depreciation_methods
+from . import test_prorate
+from . import test_salvage_value
+from . import test_asset_anomaly_detection
+from . import test_useful_life_predictor

fusion_accounting_assets/tests/test_asset_anomaly_detection.py

@@ -0,0 +1,71 @@
+from odoo.tests.common import TransactionCase
+from odoo.tests import tagged
+from odoo.addons.fusion_accounting_assets.services.anomaly_detection import (
+    detect_schedule_variance, detect_low_utilization, AssetAnomaly,
+)
+
+
+@tagged('post_install', '-at_install')
+class TestAssetAnomalyDetection(TransactionCase):
+
+    def test_schedule_variance_within_threshold_returns_none(self):
+        # 5% variance < 10% threshold
+        result = detect_schedule_variance(
+            asset_id=1, asset_name='Truck', expected_accumulated=10000,
+            actual_accumulated=10500,
+        )
+        self.assertIsNone(result)
+
+    def test_schedule_variance_behind_schedule_low_severity(self):
+        # 15% behind: low severity, behind_schedule
+        result = detect_schedule_variance(
+            asset_id=1, asset_name='Truck', expected_accumulated=10000,
+            actual_accumulated=8500,
+        )
+        self.assertIsNotNone(result)
+        self.assertEqual(result.anomaly_type, 'behind_schedule')
+        self.assertEqual(result.severity, 'low')
+
+    def test_schedule_variance_ahead_high_severity(self):
+        # 60% ahead: high severity
+        result = detect_schedule_variance(
+            asset_id=2, asset_name='Server', expected_accumulated=10000,
+            actual_accumulated=16000,
+        )
+        self.assertIsNotNone(result)
+        self.assertEqual(result.anomaly_type, 'ahead_of_schedule')
+        self.assertEqual(result.severity, 'high')
+
+    def test_schedule_variance_zero_expected_returns_none(self):
+        result = detect_schedule_variance(
+            asset_id=1, asset_name='Truck', expected_accumulated=0,
+            actual_accumulated=500,
+        )
+        self.assertIsNone(result)
+
+    def test_low_utilization_flags_when_underused(self):
+        # 60% deficit -> high severity
+        result = detect_low_utilization(
+            asset_id=3, asset_name='Mill', expected_units=1000, actual_units=400,
+        )
+        self.assertIsNotNone(result)
+        self.assertEqual(result.anomaly_type, 'low_utilization')
+        self.assertEqual(result.severity, 'high')
+
+    def test_low_utilization_within_tolerance_returns_none(self):
+        # 95% used: within 10% tolerance
+        result = detect_low_utilization(
+            asset_id=3, asset_name='Mill', expected_units=1000, actual_units=950,
+        )
+        self.assertIsNone(result)
+
+    def test_anomaly_to_dict_round_trip(self):
+        anomaly = AssetAnomaly(
+            asset_id=1, asset_name='X', anomaly_type='behind_schedule',
+            severity='medium', expected=100.0, actual=70.0, variance_pct=30.0,
+            detail='example',
+        )
+        d = anomaly.to_dict()
+        self.assertEqual(d['asset_id'], 1)
+        self.assertEqual(d['anomaly_type'], 'behind_schedule')
+        self.assertEqual(d['severity'], 'medium')

fusion_accounting_assets/tests/test_depreciation_methods.py

@@ -0,0 +1,88 @@
+from odoo.tests.common import TransactionCase
+from odoo.tests import tagged
+from odoo.addons.fusion_accounting_assets.services.depreciation_methods import (
+    straight_line, declining_balance, units_of_production,
+)
+
+
+@tagged('post_install', '-at_install')
+class TestStraightLine(TransactionCase):
+
+    def test_total_equals_cost_minus_salvage(self):
+        steps = straight_line(cost=10000, salvage_value=1000, n_periods=5)
+        total = sum(s.period_amount for s in steps)
+        self.assertAlmostEqual(total, 9000, places=2)
+
+    def test_per_period_equal_except_last(self):
+        steps = straight_line(cost=10000, salvage_value=0, n_periods=4)
+        self.assertEqual([s.period_amount for s in steps], [2500.0] * 4)
+
+    def test_last_period_absorbs_rounding(self):
+        steps = straight_line(cost=10000, salvage_value=0, n_periods=3)
+        total = sum(s.period_amount for s in steps)
+        self.assertAlmostEqual(total, 10000, places=2)
+
+    def test_zero_periods_returns_empty(self):
+        self.assertEqual(straight_line(cost=10000, n_periods=0), [])
+
+    def test_book_value_decreasing(self):
+        steps = straight_line(cost=10000, salvage_value=1000, n_periods=5)
+        for i in range(1, len(steps)):
+            self.assertLess(steps[i].book_value_at_end, steps[i - 1].book_value_at_end)
+
+
+@tagged('post_install', '-at_install')
+class TestDecliningBalance(TransactionCase):
+
+    def test_total_does_not_exceed_depreciable(self):
+        steps = declining_balance(cost=10000, salvage_value=1000, n_periods=10, rate=0.20)
+        total = sum(s.period_amount for s in steps)
+        self.assertLessEqual(total, 9000.01)
+
+    def test_does_not_go_below_salvage(self):
+        steps = declining_balance(cost=10000, salvage_value=1000, n_periods=10, rate=0.50)
+        for s in steps:
+            self.assertGreaterEqual(s.book_value_at_end, 999.99)
+
+    def test_zero_rate_returns_empty(self):
+        self.assertEqual(declining_balance(cost=10000, n_periods=5, rate=0), [])
+
+    def test_pathological_100pct_rate_one_period(self):
+        steps = declining_balance(cost=10000, salvage_value=500, n_periods=10, rate=1.0)
+        self.assertEqual(len(steps), 1)
+        self.assertAlmostEqual(steps[0].period_amount, 9500, places=2)
+
+
+@tagged('post_install', '-at_install')
+class TestUnitsOfProduction(TransactionCase):
+
+    def test_total_proportional_to_units_used(self):
+        steps = units_of_production(
+            cost=20000, salvage_value=2000,
+            total_units_expected=10000,
+            units_per_period=[1000, 2000, 3000, 4000],
+        )
+        total = sum(s.period_amount for s in steps)
+        self.assertAlmostEqual(total, 18000, places=1)
+
+    def test_partial_use_partial_depreciation(self):
+        steps = units_of_production(
+            cost=10000, salvage_value=0,
+            total_units_expected=1000,
+            units_per_period=[200],
+        )
+        self.assertAlmostEqual(steps[0].period_amount, 2000, places=2)
+
+    def test_zero_total_units_returns_empty(self):
+        self.assertEqual(
+            units_of_production(cost=10000, total_units_expected=0, units_per_period=[100]),
+            [],
+        )
+
+    def test_does_not_overshoot_salvage(self):
+        steps = units_of_production(
+            cost=10000, salvage_value=1000,
+            total_units_expected=1000,
+            units_per_period=[2000],
+        )
+        self.assertAlmostEqual(steps[0].period_amount, 9000, places=2)

fusion_accounting_assets/tests/test_prorate.py

@@ -0,0 +1,65 @@
+from datetime import date
+
+from odoo.tests.common import TransactionCase
+from odoo.tests import tagged
+from odoo.addons.fusion_accounting_assets.services.prorate import prorate_factor
+
+
+@tagged('post_install', '-at_install')
+class TestProrate(TransactionCase):
+
+    def test_full_month_convention_always_one(self):
+        f = prorate_factor(
+            period_start=date(2026, 1, 1),
+            period_end=date(2026, 1, 31),
+            asset_start=date(2026, 1, 15),
+            convention='full_month',
+        )
+        self.assertEqual(f, 1.0)
+
+    def test_asset_starts_before_period_full_factor(self):
+        f = prorate_factor(
+            period_start=date(2026, 1, 1),
+            period_end=date(2026, 1, 31),
+            asset_start=date(2025, 12, 1),
+            convention='days_period',
+        )
+        self.assertEqual(f, 1.0)
+
+    def test_asset_starts_after_period_zero_factor(self):
+        f = prorate_factor(
+            period_start=date(2026, 1, 1),
+            period_end=date(2026, 1, 31),
+            asset_start=date(2026, 2, 5),
+            convention='days_period',
+        )
+        self.assertEqual(f, 0.0)
+
+    def test_days_period_mid_month(self):
+        # Jan 16 -> Jan 31 inclusive = 16 days; period = 31 days
+        f = prorate_factor(
+            period_start=date(2026, 1, 1),
+            period_end=date(2026, 1, 31),
+            asset_start=date(2026, 1, 16),
+            convention='days_period',
+        )
+        self.assertAlmostEqual(f, 16 / 31, places=5)
+
+    def test_days_365_mid_month(self):
+        # 16 days / 365
+        f = prorate_factor(
+            period_start=date(2026, 1, 1),
+            period_end=date(2026, 1, 31),
+            asset_start=date(2026, 1, 16),
+            convention='days_365',
+        )
+        self.assertAlmostEqual(f, 16 / 365.0, places=5)
+
+    def test_unknown_convention_raises(self):
+        with self.assertRaises(ValueError):
+            prorate_factor(
+                period_start=date(2026, 1, 1),
+                period_end=date(2026, 1, 31),
+                asset_start=date(2026, 1, 15),
+                convention='bogus',  # type: ignore[arg-type]
+            )

fusion_accounting_assets/tests/test_salvage_value.py

@@ -0,0 +1,45 @@
+from odoo.tests.common import TransactionCase
+from odoo.tests import tagged
+from odoo.addons.fusion_accounting_assets.services.salvage_value import (
+    SalvageConfig, compute_salvage_value, remaining_useful_life_value,
+)
+
+
+@tagged('post_install', '-at_install')
+class TestSalvageValue(TransactionCase):
+
+    def test_zero_method_returns_zero(self):
+        v = compute_salvage_value(cost=10000, config=SalvageConfig(method='zero'))
+        self.assertEqual(v, 0.0)
+
+    def test_percentage_method(self):
+        v = compute_salvage_value(
+            cost=10000, config=SalvageConfig(method='percentage', value=10),
+        )
+        self.assertAlmostEqual(v, 1000.0, places=2)
+
+    def test_fixed_method(self):
+        v = compute_salvage_value(
+            cost=10000, config=SalvageConfig(method='fixed', value=750),
+        )
+        self.assertAlmostEqual(v, 750.0, places=2)
+
+    def test_unknown_method_raises(self):
+        with self.assertRaises(ValueError):
+            compute_salvage_value(
+                cost=10000,
+                config=SalvageConfig(method='bogus', value=0),  # type: ignore[arg-type]
+            )
+
+    def test_remaining_useful_life_value_midway(self):
+        # Halfway through life; current book 6000, salvage 1000 -> 1000 + 5000*0.5 = 3500
+        v = remaining_useful_life_value(
+            current_book=6000, salvage=1000, periods_used=5, total_periods=10,
+        )
+        self.assertAlmostEqual(v, 3500.0, places=2)
+
+    def test_remaining_useful_life_value_at_end_returns_salvage(self):
+        v = remaining_useful_life_value(
+            current_book=1200, salvage=1000, periods_used=10, total_periods=10,
+        )
+        self.assertEqual(v, 1000.0)

fusion_accounting_assets/tests/test_useful_life_predictor.py

@@ -0,0 +1,61 @@
+from odoo.tests.common import TransactionCase
+from odoo.tests import tagged
+from odoo.addons.fusion_accounting_assets.services.useful_life_predictor import (
+    predict_useful_life,
+)
+from odoo.addons.fusion_accounting_assets.services.useful_life_prompt import (
+    SYSTEM_PROMPT, build_prompt,
+)
+
+
+@tagged('post_install', '-at_install')
+class TestUsefulLifePredictor(TransactionCase):
+
+    def setUp(self):
+        super().setUp()
+        # Ensure no provider configured for these fallback tests.
+        self.env['ir.config_parameter'].sudo().search([
+            ('key', 'in', [
+                'fusion_accounting.provider.asset_useful_life',
+                'fusion_accounting.provider.default',
+            ])
+        ]).unlink()
+
+    def test_fallback_computer(self):
+        result = predict_useful_life(self.env, description="Dell laptop")
+        self.assertEqual(result['useful_life_years'], 4)
+        self.assertEqual(result['depreciation_method'], 'straight_line')
+
+    def test_fallback_furniture(self):
+        result = predict_useful_life(self.env, description="office desk")
+        self.assertEqual(result['useful_life_years'], 7)
+
+    def test_fallback_vehicle_uses_declining(self):
+        result = predict_useful_life(self.env, description="Ford F-150 truck")
+        self.assertEqual(result['useful_life_years'], 5)
+        self.assertEqual(result['depreciation_method'], 'declining_balance')
+
+    def test_fallback_default_for_unknown(self):
+        result = predict_useful_life(self.env, description="mystery widget")
+        self.assertEqual(result['useful_life_years'], 5)
+        self.assertEqual(result['confidence'], 0.3)
+
+    def test_returns_dict_with_required_keys(self):
+        result = predict_useful_life(self.env, description="server")
+        for key in ('useful_life_years', 'depreciation_method', 'rationale', 'confidence'):
+            self.assertIn(key, result)
+
+
+@tagged('post_install', '-at_install')
+class TestUsefulLifePrompt(TransactionCase):
+
+    def test_system_prompt_requires_json(self):
+        self.assertIn('JSON', SYSTEM_PROMPT)
+
+    def test_build_prompt_returns_tuple(self):
+        result = build_prompt(description='test')
+        self.assertEqual(len(result), 2)
+
+    def test_user_prompt_includes_amount(self):
+        _, user = build_prompt(description='laptop', amount=2000)
+        self.assertIn('2,000', user)