feat(fusion_accounting_bank_rec): persisted AI suggestion model with state lifecycle

Made-with: Cursor

fusion_accounting_bank_rec/models/__init__.py

@@ -1,2 +1,3 @@
 from . import fusion_reconcile_pattern
 from . import fusion_reconcile_precedent
+from . import fusion_reconcile_suggestion

fusion_accounting_bank_rec/models/fusion_reconcile_suggestion.py

@@ -0,0 +1,98 @@
+"""Persisted AI suggestions for bank line reconciliations.
+
+One row per (statement_line, candidate_match). The OWL widget reads these
+to render confidence badges; users accept/reject which feeds back into
+the pattern learning system.
+
+The AI never writes account.partial.reconcile directly — it writes
+suggestions here, and the user (or batch-accept action) approves them
+through the engine's accept_suggestion() method.
+"""
+
+from odoo import api, fields, models
+
+
+class FusionReconcileSuggestion(models.Model):
+    _name = "fusion.reconcile.suggestion"
+    _description = "AI-generated bank reconciliation suggestion"
+    _order = "statement_line_id, confidence desc"
+
+    company_id = fields.Many2one('res.company', required=True, index=True,
+                                   default=lambda self: self.env.company)
+    statement_line_id = fields.Many2one('account.bank.statement.line',
+                                          required=True, index=True, ondelete='cascade')
+
+    # The proposal
+    proposed_move_line_ids = fields.Many2many('account.move.line',
+                                                string="Proposed matches")
+    proposed_write_off_amount = fields.Monetary(currency_field='currency_id')
+    proposed_write_off_account_id = fields.Many2one('account.account')
+    currency_id = fields.Many2one('res.currency',
+                                    related='statement_line_id.currency_id',
+                                    store=True)
+
+    # Scoring
+    confidence = fields.Float(required=True)
+    confidence_band = fields.Selection([
+        ('high', 'High (>=95%)'),
+        ('medium', 'Medium (70-94%)'),
+        ('low', 'Low (50-69%)'),
+        ('none', 'No confidence (<50%)'),
+    ], compute='_compute_band', store=True)
+    rank = fields.Integer(help="1 = top suggestion, 2-N = alternatives")
+    reasoning = fields.Text(help="Human-readable explanation")
+
+    # Feature breakdown (for transparency + future learning)
+    score_amount_match = fields.Float()
+    score_partner_pattern = fields.Float()
+    score_precedent_similarity = fields.Float()
+    score_ai_rerank = fields.Float()
+
+    # Provenance
+    generated_at = fields.Datetime(default=fields.Datetime.now)
+    generated_by = fields.Selection([
+        ('cron_batch', 'Batch cron'),
+        ('on_demand', 'User refreshed alternatives'),
+        ('on_open', 'Widget opened (lazy)'),
+    ])
+    provider_used = fields.Char(
+        help="e.g. 'claude_sonnet_4_5', 'lmstudio_qwen_7b', 'statistical_only'")
+    tokens_used = fields.Integer(help="if AI re-rank invoked")
+    generation_ms = fields.Integer(help="latency for monitoring")
+
+    # Lifecycle
+    state = fields.Selection([
+        ('pending', 'Pending review'),
+        ('accepted', 'Accepted'),
+        ('rejected', 'Rejected'),
+        ('superseded', 'Superseded by newer suggestion'),
+        ('stale', 'Stale (line changed since)'),
+    ], default='pending', required=True, index=True)
+    accepted_at = fields.Datetime()
+    accepted_by = fields.Many2one('res.users')
+    rejected_at = fields.Datetime()
+    rejected_reason = fields.Selection([
+        ('wrong_invoice', 'Wrong invoice'),
+        ('wrong_partner', 'Wrong partner'),
+        ('wrong_amount', 'Amount off'),
+        ('not_a_match', 'No good match exists'),
+        ('other', 'Other'),
+    ])
+
+    _confidence_in_range = models.Constraint(
+        'CHECK (confidence >= 0.0 AND confidence <= 1.0)',
+        'Confidence must be between 0.0 and 1.0',
+    )
+
+    @api.depends('confidence')
+    def _compute_band(self):
+        for sug in self:
+            c = sug.confidence
+            if c >= 0.95:
+                sug.confidence_band = 'high'
+            elif c >= 0.70:
+                sug.confidence_band = 'medium'
+            elif c >= 0.50:
+                sug.confidence_band = 'low'
+            else:
+                sug.confidence_band = 'none'