feat(fusion_accounting_ai): bank_rec_prompt for AI re-rank step

Provider-agnostic system + user prompt builder for the confidence
scoring pipeline's Pass 3 (AI re-rank). Output contract is JSON with
"ranked" array; works with OpenAI, Claude, and local OpenAI-compatible
servers (LM Studio, Ollama).

Made-with: Cursor

fusion_accounting_ai/services/prompts/__init__.py

@@ -1,2 +1,3 @@
 from . import system_prompt
 from . import domain_prompts
+from . import bank_rec_prompt

fusion_accounting_ai/services/prompts/bank_rec_prompt.py

@@ -0,0 +1,107 @@
+"""Bank reconciliation AI re-rank prompt.
+
+Used by fusion_accounting_bank_rec/services/confidence_scoring.py to ask
+an LLM to refine the statistical ranking of candidate matches.
+
+Output contract: the LLM MUST respond with valid JSON of shape:
+    {"ranked": [{"candidate_id": int, "confidence": float, "reason": str}, ...]}
+
+System prompt is provider-agnostic - works with OpenAI Chat Completions,
+Claude Messages, and local OpenAI-compatible servers (LM Studio, Ollama).
+"""
+
+from datetime import date
+
+
+SYSTEM_PROMPT = """You are an expert accountant assisting with bank reconciliation.
+
+Your job: given a bank statement line and a list of candidate journal items
+that statistically scored well as potential matches, re-rank them based on
+domain expertise. Consider:
+
+1. **Amount-exact matches** are almost always correct unless the partner is wrong.
+2. **Memo / reference clues** - bank memos often contain invoice numbers, partner
+   names, or transaction references that disambiguate matches.
+3. **Date proximity** - invoices are typically reconciled within 30 days of issue.
+4. **Pattern conformance** - if the partner has a learned pattern (e.g. "always
+   pays exact amount, weekly cadence"), favor candidates that fit that pattern.
+5. **Precedent similarity** - if a near-identical reconcile happened before,
+   it's likely the right one.
+
+Return ONLY valid JSON of this exact shape:
+{
+  "ranked": [
+    {"candidate_id": <int>, "confidence": <float 0-1>, "reason": "<short string>"},
+    ...
+  ]
+}
+
+Do NOT include any prose before or after the JSON. Do NOT use markdown code fences.
+The "ranked" array MUST contain every candidate_id from the input, in your
+preferred order (highest confidence first).
+"""
+
+
+def build_prompt(statement_line, scored_candidates, pattern=None, precedents=None):
+    """Build (system_prompt, user_prompt) for AI re-rank.
+
+    Args:
+        statement_line: account.bank.statement.line recordset (singleton)
+        scored_candidates: list of ScoredCandidate dataclasses (from confidence_scoring)
+        pattern: fusion.reconcile.pattern recordset for the partner, or None
+        precedents: list of PrecedentMatch dataclasses, or None
+
+    Returns:
+        (system_prompt: str, user_prompt: str) tuple
+    """
+    user_parts = []
+
+    user_parts.append("BANK LINE:")
+    user_parts.append(f"  Date: {statement_line.date}")
+    user_parts.append(
+        f"  Amount: {statement_line.amount} {statement_line.currency_id.name or ''}"
+    )
+    user_parts.append(
+        f"  Memo / payment ref: {statement_line.payment_ref or '(none)'}"
+    )
+    if statement_line.partner_id:
+        user_parts.append(f"  Partner: {statement_line.partner_id.name}")
+
+    if pattern:
+        user_parts.append("")
+        user_parts.append("PARTNER PATTERN (learned from past reconciles):")
+        user_parts.append(f"  Reconcile count: {pattern.reconcile_count}")
+        user_parts.append(f"  Preferred strategy: {pattern.pref_strategy}")
+        user_parts.append(
+            f"  Typical cadence: ~{pattern.typical_cadence_days} days between reconciles"
+        )
+        if pattern.typical_amount_range:
+            user_parts.append(f"  Typical amount range: {pattern.typical_amount_range}")
+        if pattern.common_memo_tokens:
+            user_parts.append(f"  Common memo tokens: {pattern.common_memo_tokens}")
+
+    if precedents:
+        user_parts.append("")
+        user_parts.append("RECENT PRECEDENTS (most-similar past reconciles for this partner):")
+        # Cap at 3 precedents to keep prompt small and reduce token cost.
+        for p in precedents[:3]:
+            user_parts.append(
+                f"  - amount={p.amount}, similarity={p.similarity_score:.2f}, "
+                f"matched {p.matched_move_line_count} line(s), tokens={p.memo_tokens}"
+            )
+
+    user_parts.append("")
+    user_parts.append("CANDIDATES (scored by statistical pipeline):")
+    for s in scored_candidates:
+        user_parts.append(
+            f"  - candidate_id={s.candidate_id}, statistical_confidence={s.confidence}, "
+            f"amount_match={s.score_amount_match}, pattern_fit={s.score_partner_pattern}, "
+            f"precedent_sim={s.score_precedent_similarity}, "
+            f"reason=\"{s.reasoning}\""
+        )
+
+    user_parts.append("")
+    user_parts.append("Re-rank these candidates and return JSON per the system prompt.")
+
+    user_prompt = "\n".join(user_parts)
+    return (SYSTEM_PROMPT, user_prompt)

fusion_accounting_bank_rec/tests/__init__.py

@@ -9,3 +9,4 @@ from . import test_reconcile_engine_unit
 from . import test_reconcile_engine_property
 from . import test_factories
 from . import test_reconcile_engine_integration
+from . import test_bank_rec_prompt

fusion_accounting_bank_rec/tests/test_bank_rec_prompt.py

@@ -0,0 +1,92 @@
+"""Smoke tests for bank_rec_prompt module."""
+
+from odoo.tests.common import TransactionCase, tagged
+from odoo.addons.fusion_accounting_ai.services.prompts.bank_rec_prompt import (
+    SYSTEM_PROMPT,
+    build_prompt,
+)
+from odoo.addons.fusion_accounting_bank_rec.services.confidence_scoring import (
+    ScoredCandidate,
+)
+from . import _factories as f
+
+
+@tagged('post_install', '-at_install')
+class TestBankRecPrompt(TransactionCase):
+
+    def setUp(self):
+        super().setUp()
+        self.partner = self.env['res.partner'].create({'name': 'Prompt Test Partner'})
+        self.bank_line = f.make_bank_line(
+            self.env,
+            amount=1847.50,
+            partner=self.partner,
+            memo='RBC ETF DEP REF 4831',
+        )
+        self.scored = [
+            ScoredCandidate(
+                candidate_id=101,
+                confidence=0.92,
+                reasoning='Exact amount match',
+                score_amount_match=1.0,
+                score_partner_pattern=0.5,
+                score_precedent_similarity=0.85,
+            ),
+            ScoredCandidate(
+                candidate_id=102,
+                confidence=0.71,
+                reasoning='Close amount',
+                score_amount_match=0.95,
+                score_partner_pattern=0.5,
+                score_precedent_similarity=0.6,
+            ),
+        ]
+
+    def test_system_prompt_requires_json_output(self):
+        self.assertIn('JSON', SYSTEM_PROMPT)
+        self.assertIn('"ranked"', SYSTEM_PROMPT)
+
+    def test_build_prompt_returns_tuple(self):
+        result = build_prompt(self.bank_line, self.scored)
+        self.assertEqual(len(result), 2)
+        system, user = result
+        self.assertIsInstance(system, str)
+        self.assertIsInstance(user, str)
+
+    def test_user_prompt_includes_bank_line_details(self):
+        _, user = build_prompt(self.bank_line, self.scored)
+        self.assertIn('1847.5', user)
+        self.assertIn('RBC ETF DEP REF 4831', user)
+        self.assertIn('Prompt Test Partner', user)
+
+    def test_user_prompt_includes_all_candidates(self):
+        _, user = build_prompt(self.bank_line, self.scored)
+        self.assertIn('candidate_id=101', user)
+        self.assertIn('candidate_id=102', user)
+
+    def test_user_prompt_omits_pattern_section_when_none(self):
+        _, user = build_prompt(self.bank_line, self.scored, pattern=None)
+        self.assertNotIn('PARTNER PATTERN', user)
+
+    def test_user_prompt_includes_pattern_section_when_provided(self):
+        pattern = f.make_pattern(self.env, partner=self.partner, reconcile_count=15)
+        _, user = build_prompt(self.bank_line, self.scored, pattern=pattern)
+        self.assertIn('PARTNER PATTERN', user)
+        self.assertIn('15', user)
+
+    def test_user_prompt_includes_precedents_when_provided(self):
+        from odoo.addons.fusion_accounting_bank_rec.services.precedent_lookup import (
+            PrecedentMatch,
+        )
+        precedents = [
+            PrecedentMatch(
+                precedent_id=1,
+                amount=1847.50,
+                memo_tokens='RBC,ETF',
+                matched_move_line_count=1,
+                similarity_score=0.95,
+            ),
+        ]
+        _, user = build_prompt(self.bank_line, self.scored, precedents=precedents)
+        self.assertIn('RECENT PRECEDENTS', user)
+        self.assertIn('0.95', user)