| from __future__ import annotations |
| import json |
| import re |
| from typing import Any |
| COMMUNICATION_ROLE_NAMES = [] |
| GOLD = {'conclusion': 'Select', 'numeric_result': 300.0} |
| POSITIVE_CONCLUSION = 'Select' |
| NEGATIVE_CONCLUSION = 'Do not select' |
| NUMERIC_PRECISION = 1 |
| PUBLIC_ROLES = [{'role_name': 'Quality assurance director (QAD)', 'responsibility': 'Maintains Good Distribution Practice certification records and regulatory audit history', 'role_abbreviation': 'QAD'}, {'role_name': 'Cold-chain monitoring supervisor (CCM)', 'responsibility': 'Maintains active IoT telemetry logging and verified shipment excursion metrics', 'role_abbreviation': 'CCM'}, {'role_name': 'Fleet operations manager (FOM)', 'responsibility': 'Maintains fleet vehicle allocation records and active transport capacity counts', 'role_abbreviation': 'FOM'}, {'role_name': 'Commercial procurement lead (CPL)', 'responsibility': 'Maintains annual volume commitments and commercial master service agreements', 'role_abbreviation': 'CPL'}, {'role_name': 'Financial risk analyst (FRA)', 'responsibility': 'Maintains vendor credit ratings, balance sheet ratios, and solvency approvals', 'role_abbreviation': 'FRA'}, {'role_name': 'Corporate sustainability liaison (CSL)', 'responsibility': 'Monitors fleet carbon intensity and public ESG reporting scores', 'role_abbreviation': 'CSL'}] |
| ROLE_KEY_PREFIXES = {'Quality assurance director (QAD)': 'QAD-', 'Cold-chain monitoring supervisor (CCM)': 'CCM-', 'Fleet operations manager (FOM)': 'FOM-', 'Commercial procurement lead (CPL)': 'CPL-', 'Financial risk analyst (FRA)': 'FRA-', 'Corporate sustainability liaison (CSL)': 'CSL-'} |
| ROLE_VALID_KEYS = {'Quality assurance director (QAD)': ['QAD-AUDIT-9MT', 'QAD-AUDIT-TXE', 'QAD-AUDIT-OFI', 'QAD-GDPREV-A29', 'QAD-GDPREV-FFR', 'QAD-GDPREV-FWE', 'QAD-GDPREV-303'], 'Cold-chain monitoring supervisor (CCM)': ['CCM-BATLIFE-TB8', 'CCM-BATLIFE-155', 'CCM-BATLIFE-MR4', 'CCM-TEMPTRK-010', 'CCM-TEMPTRK-F7E', 'CCM-TEMPTRK-API'], 'Fleet operations manager (FOM)': ['FOM-FLEETOPS-7LL', 'FOM-FLEETOPS-0ZM', 'FOM-FLEETOPS-TK4', 'FOM-DOWNTIME-T22', 'FOM-DOWNTIME-C2Y', 'FOM-DOWNTIME-YVL'], 'Commercial procurement lead (CPL)': ['CPL-PAYTRM-07N', 'CPL-PAYTRM-S9I', 'CPL-PAYTRM-UXP', 'CPL-COMMVOL-HA1', 'CPL-COMMVOL-7EN', 'CPL-COMMVOL-TRI'], 'Financial risk analyst (FRA)': ['FRA-SOLVEVAL-8PR', 'FRA-SOLVEVAL-K8L', 'FRA-SOLVEVAL-B79', 'FRA-BONDING-TCA', 'FRA-BONDING-OAL', 'FRA-BONDING-IUX'], 'Corporate sustainability liaison (CSL)': ['CSL-ESGTRACK-ZS8', 'CSL-ESGTRACK-LVY', 'CSL-ESGTRACK-CAB', 'CSL-RECYC-R86', 'CSL-RECYC-HAI', 'CSL-RECYC-XT1']} |
| RECORD_REPLY_KEYS = {'QAD-GDPREV-A29': ['QAD-GDPREV-A29'], 'QAD-GDPREV-FWE': ['QAD-GDPREV-FWE'], 'QAD-GDPREV-303': ['QAD-GDPREV-303'], 'CCM-TEMPTRK-010': ['CCM-TEMPTRK-010'], 'CCM-TEMPTRK-API': ['CCM-TEMPTRK-API'], 'CCM-TEMPTRK-F7E': ['CCM-TEMPTRK-F7E'], 'FOM-FLEETOPS-7LL': ['FOM-FLEETOPS-7LL'], 'FOM-FLEETOPS-TK4': ['FOM-FLEETOPS-TK4'], 'FOM-FLEETOPS-0ZM': ['FOM-FLEETOPS-0ZM'], 'CPL-COMMVOL-TRI': ['CPL-COMMVOL-TRI'], 'CPL-COMMVOL-HA1': ['CPL-COMMVOL-HA1'], 'CPL-COMMVOL-7EN': ['CPL-COMMVOL-7EN'], 'FRA-SOLVEVAL-B79': ['FRA-SOLVEVAL-B79'], 'FRA-SOLVEVAL-K8L': ['FRA-SOLVEVAL-K8L'], 'FRA-SOLVEVAL-8PR': ['FRA-SOLVEVAL-8PR'], 'CSL-ESGTRACK-LVY': ['CSL-ESGTRACK-LVY'], 'CSL-ESGTRACK-CAB': ['CSL-ESGTRACK-CAB'], 'CSL-ESGTRACK-ZS8': ['CSL-ESGTRACK-ZS8'], 'QAD-AUDIT-9MT': ['QAD-AUDIT-9MT'], 'QAD-AUDIT-TXE': ['QAD-AUDIT-TXE'], 'QAD-AUDIT-OFI': ['QAD-AUDIT-OFI'], 'CCM-BATLIFE-MR4': ['CCM-BATLIFE-MR4'], 'CCM-BATLIFE-155': ['CCM-BATLIFE-155'], 'CCM-BATLIFE-TB8': ['CCM-BATLIFE-TB8'], 'FOM-DOWNTIME-T22': ['FOM-DOWNTIME-T22'], 'FOM-DOWNTIME-C2Y': ['FOM-DOWNTIME-C2Y'], 'FOM-DOWNTIME-YVL': ['FOM-DOWNTIME-YVL'], 'CPL-PAYTRM-UXP': ['CPL-PAYTRM-UXP'], 'CPL-PAYTRM-S9I': ['CPL-PAYTRM-S9I'], 'CPL-PAYTRM-07N': ['CPL-PAYTRM-07N'], 'FRA-BONDING-OAL': ['FRA-BONDING-OAL'], 'FRA-BONDING-TCA': ['FRA-BONDING-TCA'], 'FRA-BONDING-IUX': ['FRA-BONDING-IUX'], 'CSL-RECYC-R86': ['CSL-RECYC-R86'], 'CSL-RECYC-HAI': ['CSL-RECYC-HAI'], 'CSL-RECYC-XT1': ['CSL-RECYC-XT1'], 'QAD-GDPREV-FFR': ['QAD-GDPREV-FFR']} |
| REQUIRED_KEYS = ['CCM-TEMPTRK-010', 'CCM-TEMPTRK-API', 'CPL-COMMVOL-TRI', 'FOM-FLEETOPS-7LL', 'FRA-SOLVEVAL-B79', 'QAD-GDPREV-303', 'QAD-GDPREV-A29'] |
| DISTRACTOR_KEYS = ['CSL-ESGTRACK-CAB', 'CSL-ESGTRACK-LVY', 'CSL-ESGTRACK-ZS8', 'CSL-RECYC-HAI', 'CSL-RECYC-R86', 'CSL-RECYC-XT1'] |
| SETTING = 'incomplete' |
|
|
| def runtime_defaults() -> dict[str, Any]: |
| return { |
| "setting": SETTING, |
| "communication_role_names": list(COMMUNICATION_ROLE_NAMES), |
| "finalize_ideal_round": 3, |
| "max_turns": 6, |
| } |
|
|
| FINALIZE_IDEAL_ROUND = runtime_defaults()["finalize_ideal_round"] |
| DEFAULT_MAX_TURNS = runtime_defaults()["max_turns"] |
| ERROR_INJECTION = {'enabled': False, 'role_name': 'Quality assurance director (QAD)', 'target_key': 'QAD-GDPREV-A29', 'alternate_key': 'QAD-GDPREV-FFR', 'target_context': 'Production environment', 'alternate_context': 'Staging environment'} |
| INCOMPLETE_INJECTION = {'record_id': 'rec_cold_chain_monitoring_lead_A', 'role_id': 'cold_chain_monitoring_lead', 'candidate_id': 'A', 'key': 'CCM-TEMPTRK-010', 'boolean_predicate_id': 'A_monitoring_telemetry_enabled', 'boolean_label': 'Continuous temperature telemetry enabled', 'hidden_predicate_id': 'A_temp_excursion_rate', 'threshold_label': 'Historical temperature excursion rate', 'threshold_aliases': ['Thermal excursion frequency', 'Out-of-range shipment percentage'], 'threshold_value': 0.15, 'threshold_unit': '%', 'partial_record_text': 'CCM-TEMPTRK-010: Apex TempLogistics Group.Apex TempLogistics Group: Continuous temperature telemetry enabled = true .Source: Central cold-chain telemetry dashboard; Scope: Active fleet transport units; Measurement: Operational switch verifying cellular transmission of thermal sensor data.', 'alternative_outputs': [{'conclusion': 'Do not select', 'numeric_result': None, 'qualified_candidate_id': None}], 'disclosure_mode': 'interval', 'disclosure_version': 'truthful_bounds_v3_domain_bounds', 'disclosure_bounds': {'lower': 0.1, 'upper': 2}, 'coarse_statement': 'Historical temperature excursion rate is between 0.1 % and 2 %, inclusive.', 'role_name': 'Cold-chain monitoring supervisor (CCM)', 'candidate_name': 'Apex TempLogistics Group', 'other_candidate_names': ['Nordic BioPharma Express', 'Sovereign Clinical Couriers'], 'context_value': None, 'other_context_values': [], 'language': 'en'} |
| INCOMPLETE_FOLLOWUP_RECORDS = [{'key': 'QAD-GDPREV-A29', 'role': 'Quality assurance director (QAD)', 'candidate': 'Apex TempLogistics Group', 'context': 'Production environment', 'fields': [{'id': 'gdp_certified', 'label': 'Good Distribution Practice certification status', 'aliases': ['GDP certification', 'EU GDP compliance']}]}, {'key': 'QAD-GDPREV-FWE', 'role': 'Quality assurance director (QAD)', 'candidate': 'Nordic BioPharma Express', 'context': 'Production environment', 'fields': [{'id': 'gdp_certified', 'label': 'Good Distribution Practice certification status', 'aliases': ['GDP certification', 'EU GDP compliance']}]}, {'key': 'QAD-GDPREV-303', 'role': 'Quality assurance director (QAD)', 'candidate': 'Sovereign Clinical Couriers', 'context': 'Production environment', 'fields': [{'id': 'gdp_certified', 'label': 'Good Distribution Practice certification status', 'aliases': ['GDP certification', 'EU GDP compliance']}]}, {'key': 'CCM-TEMPTRK-010', 'role': 'Cold-chain monitoring supervisor (CCM)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'monitoring_telemetry_enabled', 'label': 'Continuous temperature telemetry enabled', 'aliases': ['Real-time telemetry enablement', 'Active sensor logging status']}, {'id': 'temp_excursion_rate', 'label': 'Historical temperature excursion rate', 'aliases': ['Thermal excursion frequency', 'Out-of-range shipment percentage']}]}, {'key': 'CCM-TEMPTRK-API', 'role': 'Cold-chain monitoring supervisor (CCM)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'monitoring_telemetry_enabled', 'label': 'Continuous temperature telemetry enabled', 'aliases': ['Real-time telemetry enablement', 'Active sensor logging status']}, {'id': 'temp_excursion_rate', 'label': 'Historical temperature excursion rate', 'aliases': ['Thermal excursion frequency', 'Out-of-range shipment percentage']}]}, {'key': 'CCM-TEMPTRK-F7E', 'role': 'Cold-chain monitoring supervisor (CCM)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'monitoring_telemetry_enabled', 'label': 'Continuous temperature telemetry enabled', 'aliases': ['Real-time telemetry enablement', 'Active sensor logging status']}, {'id': 'temp_excursion_rate', 'label': 'Historical temperature excursion rate', 'aliases': ['Thermal excursion frequency', 'Out-of-range shipment percentage']}]}, {'key': 'FOM-FLEETOPS-7LL', 'role': 'Fleet operations manager (FOM)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'dedicated_fleet_available', 'label': 'Dedicated refrigerated fleet availability', 'aliases': ['Refrigerated vehicle allocation', 'Dedicated fleet confirmation']}, {'id': 'operational_fleet_size', 'label': 'Operational refrigerated fleet size', 'aliases': ['Active fleet vehicle count', 'Dedicated vehicle count']}]}, {'key': 'FOM-FLEETOPS-TK4', 'role': 'Fleet operations manager (FOM)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'dedicated_fleet_available', 'label': 'Dedicated refrigerated fleet availability', 'aliases': ['Refrigerated vehicle allocation', 'Dedicated fleet confirmation']}, {'id': 'operational_fleet_size', 'label': 'Operational refrigerated fleet size', 'aliases': ['Active fleet vehicle count', 'Dedicated vehicle count']}]}, {'key': 'FOM-FLEETOPS-0ZM', 'role': 'Fleet operations manager (FOM)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'dedicated_fleet_available', 'label': 'Dedicated refrigerated fleet availability', 'aliases': ['Refrigerated vehicle allocation', 'Dedicated fleet confirmation']}, {'id': 'operational_fleet_size', 'label': 'Operational refrigerated fleet size', 'aliases': ['Active fleet vehicle count', 'Dedicated vehicle count']}]}, {'key': 'CPL-COMMVOL-TRI', 'role': 'Commercial procurement lead (CPL)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'contracted_annual_volume', 'label': 'Contracted annual shipment volume', 'aliases': ['Annual freight volume', 'Projected pallet volume']}]}, {'key': 'CPL-COMMVOL-HA1', 'role': 'Commercial procurement lead (CPL)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'contracted_annual_volume', 'label': 'Contracted annual shipment volume', 'aliases': ['Annual freight volume', 'Projected pallet volume']}]}, {'key': 'CPL-COMMVOL-7EN', 'role': 'Commercial procurement lead (CPL)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'contracted_annual_volume', 'label': 'Contracted annual shipment volume', 'aliases': ['Annual freight volume', 'Projected pallet volume']}]}, {'key': 'FRA-SOLVEVAL-B79', 'role': 'Financial risk analyst (FRA)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'solvency_verified', 'label': 'Financial solvency verified', 'aliases': ['Solvency confirmation', 'Credit risk clearance']}]}, {'key': 'FRA-SOLVEVAL-K8L', 'role': 'Financial risk analyst (FRA)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'solvency_verified', 'label': 'Financial solvency verified', 'aliases': ['Solvency confirmation', 'Credit risk clearance']}]}, {'key': 'FRA-SOLVEVAL-8PR', 'role': 'Financial risk analyst (FRA)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'solvency_verified', 'label': 'Financial solvency verified', 'aliases': ['Solvency confirmation', 'Credit risk clearance']}]}, {'key': 'CSL-ESGTRACK-LVY', 'role': 'Corporate sustainability liaison (CSL)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'fleet_carbon_intensity', 'label': 'Fleet carbon intensity', 'aliases': ['Carbon footprint per ton-km', 'Emissions intensity']}]}, {'key': 'CSL-ESGTRACK-CAB', 'role': 'Corporate sustainability liaison (CSL)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'fleet_carbon_intensity', 'label': 'Fleet carbon intensity', 'aliases': ['Carbon footprint per ton-km', 'Emissions intensity']}]}, {'key': 'CSL-ESGTRACK-ZS8', 'role': 'Corporate sustainability liaison (CSL)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'fleet_carbon_intensity', 'label': 'Fleet carbon intensity', 'aliases': ['Carbon footprint per ton-km', 'Emissions intensity']}]}, {'key': 'QAD-AUDIT-9MT', 'role': 'Quality assurance director (QAD)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'supplemental_quality_assurance_lead', 'label': 'Annual external audit score', 'aliases': []}]}, {'key': 'QAD-AUDIT-TXE', 'role': 'Quality assurance director (QAD)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'supplemental_quality_assurance_lead', 'label': 'Annual external audit score', 'aliases': []}]}, {'key': 'QAD-AUDIT-OFI', 'role': 'Quality assurance director (QAD)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'supplemental_quality_assurance_lead', 'label': 'Annual external audit score', 'aliases': []}]}, {'key': 'CCM-BATLIFE-MR4', 'role': 'Cold-chain monitoring supervisor (CCM)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'supplemental_cold_chain_monitoring_lead', 'label': 'Sensor battery replacement cycle', 'aliases': []}]}, {'key': 'CCM-BATLIFE-155', 'role': 'Cold-chain monitoring supervisor (CCM)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'supplemental_cold_chain_monitoring_lead', 'label': 'Sensor battery replacement cycle', 'aliases': []}]}, {'key': 'CCM-BATLIFE-TB8', 'role': 'Cold-chain monitoring supervisor (CCM)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'supplemental_cold_chain_monitoring_lead', 'label': 'Sensor battery replacement cycle', 'aliases': []}]}, {'key': 'FOM-DOWNTIME-T22', 'role': 'Fleet operations manager (FOM)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'supplemental_fleet_operations_manager', 'label': 'Unplanned maintenance downtime', 'aliases': []}]}, {'key': 'FOM-DOWNTIME-C2Y', 'role': 'Fleet operations manager (FOM)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'supplemental_fleet_operations_manager', 'label': 'Unplanned maintenance downtime', 'aliases': []}]}, {'key': 'FOM-DOWNTIME-YVL', 'role': 'Fleet operations manager (FOM)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'supplemental_fleet_operations_manager', 'label': 'Unplanned maintenance downtime', 'aliases': []}]}, {'key': 'CPL-PAYTRM-UXP', 'role': 'Commercial procurement lead (CPL)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'supplemental_commercial_contracts_lead', 'label': 'Standard settlement period', 'aliases': []}]}, {'key': 'CPL-PAYTRM-S9I', 'role': 'Commercial procurement lead (CPL)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'supplemental_commercial_contracts_lead', 'label': 'Standard settlement period', 'aliases': []}]}, {'key': 'CPL-PAYTRM-07N', 'role': 'Commercial procurement lead (CPL)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'supplemental_commercial_contracts_lead', 'label': 'Standard settlement period', 'aliases': []}]}, {'key': 'FRA-BONDING-OAL', 'role': 'Financial risk analyst (FRA)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'supplemental_financial_risk_analyst', 'label': 'Surety bond limit', 'aliases': []}]}, {'key': 'FRA-BONDING-TCA', 'role': 'Financial risk analyst (FRA)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'supplemental_financial_risk_analyst', 'label': 'Surety bond limit', 'aliases': []}]}, {'key': 'FRA-BONDING-IUX', 'role': 'Financial risk analyst (FRA)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'supplemental_financial_risk_analyst', 'label': 'Surety bond limit', 'aliases': []}]}, {'key': 'CSL-RECYC-R86', 'role': 'Corporate sustainability liaison (CSL)', 'candidate': 'Apex TempLogistics Group', 'context': '', 'fields': [{'id': 'supplemental_sustainability_liaison', 'label': 'Insulated packaging recycling rate', 'aliases': []}]}, {'key': 'CSL-RECYC-HAI', 'role': 'Corporate sustainability liaison (CSL)', 'candidate': 'Nordic BioPharma Express', 'context': '', 'fields': [{'id': 'supplemental_sustainability_liaison', 'label': 'Insulated packaging recycling rate', 'aliases': []}]}, {'key': 'CSL-RECYC-XT1', 'role': 'Corporate sustainability liaison (CSL)', 'candidate': 'Sovereign Clinical Couriers', 'context': '', 'fields': [{'id': 'supplemental_sustainability_liaison', 'label': 'Insulated packaging recycling rate', 'aliases': []}]}, {'key': 'QAD-GDPREV-FFR', 'role': 'Quality assurance director (QAD)', 'candidate': 'Apex TempLogistics Group', 'context': 'Staging environment', 'fields': [{'id': 'gdp_certified', 'label': 'Good Distribution Practice certification status', 'aliases': ['GDP certification', 'EU GDP compliance']}]}] |
| KEY_PATTERN = re.compile(r"^[A-Z]+-[A-Z]+-[A-Z0-9]{3}$") |
| REPLY_KEY_PATTERN = re.compile(r"(?<![A-Z0-9])[A-Z]+-[A-Z]+-[A-Z0-9]{3}(?![A-Z0-9])", re.IGNORECASE) |
| ROLE_REPLY_MAX_ATTEMPTS = 3 |
| TASK_QUERY = 'Assess the candidate distributors against cold-chain quality, operational, and financial standards. To qualify, a distributor must satisfy all of the following requirements: Good Distribution Practice certification status must be verified as true based on compliance records from the Production environment; continuous temperature telemetry must be enabled and the historical temperature excursion rate must be at most 0.40%; dedicated refrigerated fleet availability must be confirmed as true; and financial solvency must be verified as true. Determine whether exactly one distributor meets all criteria. If exactly one candidate distributor qualifies, output the conclusion label "Select", identify the selected distributor, and compute its transport efficiency ratio (contracted annual shipment volume divided by operational refrigerated fleet size in pallets/vehicles). If no candidate distributor qualifies or if more than one qualifies, output the conclusion label "Do not select" and set the numeric result to null.\n\nNumeric calculation rule: Compute only for the unique eligible candidate: [Contracted annual shipment volume] / [Operational refrigerated fleet size] (direct ratio; do not multiply by 100; denominator must not be zero). Result unit: pallets/vehicles. Round only the final result to 1 decimal places (ties to even). If no candidate qualifies, numeric_result is null.\n\nCandidates and records to verify:\n- Apex TempLogistics Group：QAD-GDPREV-A29、CCM-TEMPTRK-010、FOM-FLEETOPS-7LL、CPL-COMMVOL-TRI、FRA-SOLVEVAL-B79、CSL-ESGTRACK-LVY\n- Nordic BioPharma Express：QAD-GDPREV-FWE、CCM-TEMPTRK-API、FOM-FLEETOPS-TK4、CPL-COMMVOL-HA1、FRA-SOLVEVAL-K8L、CSL-ESGTRACK-CAB\n- Sovereign Clinical Couriers：QAD-GDPREV-303、CCM-TEMPTRK-F7E、FOM-FLEETOPS-0ZM、CPL-COMMVOL-7EN、FRA-SOLVEVAL-8PR、CSL-ESGTRACK-ZS8' |
| TASK_CONTEXT = 'The procurement department is reviewing candidate distributors for cold-chain pharmaceutical logistics to secure compliant, temperature-controlled distribution capacity for regional distribution networks.' |
|
|
| """Parse one model message without treating trailing generation noise as an action.""" |
| import json |
| import re |
| from typing import Any |
|
|
|
|
| def parse_json_message(content: Any, *, trace: dict[str, Any] | None = None) -> dict[str, Any]: |
| if trace is not None: |
| trace.clear() |
| trace.update(policy="first_complete_object", raw_response=content, recovered=False) |
| if not isinstance(content, str): |
| raise ValueError("model response must be text") |
| text = content.strip().lstrip("\ufeff").strip() |
| |
| try: |
| value, end = json.JSONDecoder().raw_decode(text) |
| start = 0 |
| except json.JSONDecodeError: |
| if text.startswith(("{", "[", '"')): |
| raise |
| opening = re.search(r"[\{\[]", text) |
| if opening is None: |
| raise |
| start = opening.start() |
| value, consumed = json.JSONDecoder().raw_decode(text[start:]) |
| end = start + consumed |
| if not isinstance(value, dict): |
| raise ValueError("model response must be a JSON object") |
| prefix, suffix = text[:start], text[end:] |
| if trace is not None: |
| trace.update(recovered=bool(prefix.strip() or suffix.strip()), |
| ignored_prefix=prefix, ignored_suffix=suffix) |
| return value |
|
|
|
|
| def expand_question_keys(questions): |
| expanded = [] |
| for question in questions: |
| if not isinstance(question, dict): |
| expanded.append(question) |
| continue |
| text = question.get("question") |
| keys = list(dict.fromkeys(REPLY_KEY_PATTERN.findall(text))) if isinstance(text, str) else [] |
| for key in keys or [None]: |
| expanded.append(dict(question, key=key)) |
| return expanded |
|
|
|
|
| def validate_question(question, *, role_name, semantic_enabled=False): |
| if not isinstance(question, dict) or not isinstance(question.get("question"), str) or not question["question"].strip(): |
| raise ValueError("question must contain natural-language text") |
| if role_name not in ROLE_KEY_PREFIXES: |
| raise ValueError("unknown Role") |
| key = question.get("key") |
| authorized = semantic_enabled if key is None else ( |
| isinstance(key, str) and key in ROLE_VALID_KEYS[role_name] and key in question["question"]) |
| return {"authorized": bool(authorized)} |
|
|
| def recovery_key_match(answer: str) -> str | None: |
| target = ERROR_INJECTION.get("target_key") |
| if not target: |
| return None |
| def contains(key: str) -> bool: |
| return bool(re.search(r"(?<![A-Z0-9-])" + re.escape(key) + r"(?![A-Z0-9-])", answer, re.IGNORECASE)) |
| if contains(target): |
| return "full_key" |
| parts = target.split("-") |
| alias = parts[0] + "-" + parts[-1] |
| matches = [key for key in ROLE_VALID_KEYS.get(ERROR_INJECTION.get("role_name"), []) |
| if key.split("-")[0] + "-" + key.split("-")[-1] == alias] |
| if matches == [target] and contains(alias): |
| return "abbreviated_key" |
| return None |
|
|
| def communication_metrics(transcript: list[dict[str, Any]]) -> dict[str, Any]: |
| key_query_rounds: dict[str, int] = {} |
| key_query_counts: dict[str, int] = {} |
| queried_roles: set[str] = set() |
| mismatch_round: int | None = None |
| recovery_query_sent = False |
| recovery_query_turn: int | None = None |
| target_key_returned = False |
| recovery_turn: int | None = None |
| recovery_method: str | None = None |
| recovery_reference: str | None = None |
| target_key_query_turn: int | None = None |
| finalized_before_recovery = False |
| for event in transcript: |
| turn = int(event.get("turn", len(transcript))) |
| action = event.get("main_agent_action", {}) |
| if ( |
| ERROR_INJECTION.get("enabled") |
| and action.get("action") == "finalize" |
| and not target_key_returned |
| ): |
| finalized_before_recovery = True |
| if action.get("action") != "ask_role_agents": continue |
| for answer in event.get("role_answers", []): |
| role_name = answer.get("role_agent") |
| if role_name: queried_roles.add(role_name) |
| if ( |
| ERROR_INJECTION.get("enabled") and mismatch_round is not None and turn > mismatch_round |
| and role_name == ERROR_INJECTION.get("role_name") |
| and answer.get("question", {}).get("key") == ERROR_INJECTION.get("target_key") |
| and "role_trace" in answer |
| and recovery_key_match(str(answer.get("answer", ""))) is not None |
| ): |
| target_key_query_turn = target_key_query_turn or turn |
| if ( |
| ERROR_INJECTION.get("enabled") |
| and role_name == ERROR_INJECTION.get("role_name") |
| and answer.get("question", {}).get("key") == ERROR_INJECTION.get("alternate_key") |
| and "role_trace" in answer |
| ): |
| mismatch_round = mismatch_round or turn |
| for message in action.get("messages", []): |
| for question in message.get("questions", []): |
| key = question.get("key") |
| if ( |
| ERROR_INJECTION.get("enabled") |
| and resolve_role_name(message.get("role_agent")) == ERROR_INJECTION.get("role_name") |
| and (key is None or key == ERROR_INJECTION.get("target_key")) |
| and mismatch_round is not None |
| and turn > mismatch_round |
| ): |
| recovery_query_sent = True |
| recovery_query_turn = recovery_query_turn or turn |
| if not isinstance(key, str) or not KEY_PATTERN.fullmatch(key): continue |
| key_query_rounds.setdefault(key, turn) |
| key_query_counts[key] = key_query_counts.get(key, 0) + 1 |
| if recovery_query_sent and not target_key_returned and mismatch_round is not None and turn > mismatch_round: |
| for answer in event.get("role_answers", []): |
| question = answer.get("question", {}) |
| if ( |
| answer.get("role_agent") == ERROR_INJECTION.get("role_name") |
| and (question.get("key") is None or question.get("key") == ERROR_INJECTION.get("target_key")) |
| and "role_trace" in answer |
| and recovery_key_match(str(answer.get("answer", ""))) is not None |
| ): |
| target_key_returned = True |
| recovery_reference = recovery_key_match(str(answer.get("answer", ""))) |
| recovery_turn = turn |
| recovery_method = "keyless_query" if question.get("key") is None else "target_key_query" |
| break |
| queried = set(key_query_counts) |
| required = set(REQUIRED_KEYS) |
| relevant = queried & required |
| return { |
| "required_key_recall":len(relevant)/len(required) if required else 1.0, |
| "required_key_precision":len(relevant)/len(queried) if queried else 0.0, |
| "key_query_rounds":key_query_rounds, |
| "key_query_counts":key_query_counts, |
| "duplicate_key_queries":sum(max(0, count-1) for count in key_query_counts.values()), |
| "distractor_key_queries":sum(key_query_counts.get(key, 0) for key in DISTRACTOR_KEYS), |
| "distractor_key_query_rate":sum(key_query_counts.get(key, 0) for key in DISTRACTOR_KEYS)/sum(key_query_counts.values()) if key_query_counts else 0.0, |
| "turns_to_finalize":len(transcript), |
| "mismatch_key_queried":mismatch_round is not None, |
| "mismatch_key_turn":mismatch_round, |
| "recovery_query_sent":recovery_query_sent, |
| "recovery_query_turn":recovery_query_turn, |
| "target_key_returned":target_key_returned, |
| "recovery_turn":recovery_turn, |
| "recovery_method":recovery_method, |
| "target_key_query_turn":target_key_query_turn, |
| "recovery_key_match":recovery_reference, |
| "recovery_metrics_version":"keyed_and_keyless_v3", |
| "recovery_latency":recovery_turn - mismatch_round if recovery_turn is not None and mismatch_round is not None else None, |
| "finalized_before_recovery":finalized_before_recovery, |
| } |
|
|
| def main_system_prompt() -> str: |
| role_text = "\n".join(f"- {item['role_name']}: {item['responsibility']}" for item in PUBLIC_ROLES) |
| ask_example = json.dumps({"action":"ask_role_agents","messages":[{"role_agent":"Role name","questions":[{"id":"Q1","question":"Please provide the record for ABC-DATA-A1B using the supplied key."}]}]}, ensure_ascii=False) |
| final_example = json.dumps({"action":"finalize","conclusion":"Exactly: " + POSITIVE_CONCLUSION + " or " + NEGATIVE_CONCLUSION,"numeric_result":None}, ensure_ascii=False).replace('"numeric_result": null', '"numeric_result": null/number') |
| communication_protocol = ('You may output {"action":"wait"} to advance one logical turn. An empty observation {"observations":[]} means no new visible messages; it does not imply receipt, processing or any future reply.' if SETTING in {'missed', 'mixed'} else '') |
| followup = ('For keyless follow-ups, name the candidate in full and the metric explicitly, or request that candidate\'s complete records. A unique previously queried record can be checked directly; otherwise supply a full key and metric when asked to clarify. Explicit directory requests return the complete directory.' if SETTING == 'incomplete' else |
| 'Authorized keyless follow-ups may name the candidate and metric or request complete records. A role may retrieve facts, request clarification or return a directory. If given a directory, select a valid key to obtain facts.' if SETTING == 'mixed' else |
| 'Authorized keyless questions return that role\'s complete directory. Choose a key from the directory to obtain concrete facts.') |
| return "\n\n".join([ |
| 'You are the Main Agent. Integrate information from independent role agents and make the final decision. Communicate in English.', |
| 'You cannot see private role information directly. Obtain it only by explicitly messaging the role agents.', |
| 'Key protocol:\n1. Candidate names and record keys are supplied in the task. Initial access requires a valid key; authorized later follow-ups are the exception.\n' |
| '2. Keys use ROLE-BUSINESS-XXX: role abbreviation, business abbreviation and three random alphanumeric characters. Use complete supplied keys; do not invent them.\n' |
| '3. Send each key to its owning business role. Use valid keys supplied by roles for subsequent checks.\n' |
| '4. Do not output a separate key field. Put the complete key in question text; the system extracts it and checks ownership. Separate different keys into individual questions, preferably one record per question.\n' |
| '5. After a successful keyed query to a role, keyless natural-language follow-ups to that role are authorized starting from the next turn.\n6. ' + followup, |
| 'Available independent role agents:\n\n' + role_text, |
| 'To ask questions, output only JSON in this form:\n\n' + ask_example, |
| *([communication_protocol] if communication_protocol else []), |
| 'Final answer format (output only valid JSON):\n\n' + final_example, |
| 'Use exactly one of these conclusion labels: ' + POSITIVE_CONCLUSION + ' or ' + NEGATIVE_CONCLUSION + '. ' |
| 'Do not append a candidate name, explanation or other text. null/number means choose one, not literal syntax. ' |
| 'For a positive conclusion provide the computed number; for a negative conclusion use null. Do not include units or explanations in numeric_result.', |
| ]) |
|
|
|
|
| """Conservative English evidence rules shared by standalone generated runtimes. |
| |
| This is attribution of explicit statements, not arbitrary semantic entailment. |
| Unsupported or conflicting statements remain unknown; no model calls are made. |
| """ |
| import re |
| from decimal import Decimal |
|
|
| _RECOVERY_NUMBER = r'[+-]?(?:\d+(?:\.\d+)?|\.\d+)(?:[eE][+-]?\d+)?' |
| _RECOVERY_UNCERTAIN = r"\b(?:unknown|uncertain|unclear|may|might|could|should|would|must|expected|possibly|whether|cannot|unable|unconfirmed|approximately|about|around|roughly|estimated|not confirmed|not known|not established)\b" |
| _RECOVERY_NEGATIVE = r'\b(?:not|no|never|false|invalid|blocked|failed|denied|unavailable|unapproved|revoked|inactive|disabled|noncompliant)\b' |
|
|
|
|
| def evidence_text(text): |
| text = text.replace('’', "'").replace('−', '-').replace('‑', '-') |
| text = re.sub(r"\bcan't\b", 'cannot', text, flags=re.I) |
| text = re.sub(r"n't\b", ' not', text, flags=re.I) |
| text = re.sub(r'(?<![\w.,])[-+]?\d{1,3}(?:,\d{3})+(?:\.\d+)?(?![\d,]|\.\d)', |
| lambda m: m[0].replace(',', ''), text) |
| return text |
|
|
|
|
| def evidence_phrase(text, phrase): |
| if not phrase: |
| return False |
| return bool(re.search(r'(?<!\w)' + re.escape(phrase).replace(r'\ ', r'\s+') + r'(?!\w)', text, re.I)) |
|
|
|
|
| def evidence_nonasserted(text): |
| """Questions, hypotheses and unconfirmed claims are not returned facts.""" |
| return bool('?' in text or re.search(_RECOVERY_UNCERTAIN, text, re.I) |
| or re.search(r'\b(?:if|unless|assuming|suppose|supposing|hypothetically)\b', text, re.I) |
| or re.search(r'\b(?:do|does|did|has|have|had)\s+not\s+(?:confirm|verify|establish)(?:ed)?\b', text, re.I)) |
|
|
|
|
| def evidence_clauses(text): |
| |
| sentences = re.split(r'[。\n]|\.(?!\d)|(?<=[!?])\s+', text) |
| clauses = [] |
| for sentence in sentences: |
| if evidence_nonasserted(sentence): |
| clauses.append(sentence) |
| else: |
| clauses.extend(re.split(r'[;；]|,?\s+\b(?:but|and)\b\s+(?![+-]?\d)', sentence, flags=re.I)) |
| return clauses |
|
|
|
|
| def evidence_scope(answer, question, profile): |
| for target in [profile.get('candidate', ''), profile.get('target_context', '')]: |
| if target and re.search(r'\b(?:not|outside|excluding|except)(?:\s+(?:for|the))?\s+' + re.escape(target) + r'(?!\w)', answer, re.I): |
| return 'not_returned' |
| candidates = profile.get('other_candidates', []) |
| contexts = profile.get('other_contexts', [profile.get('alternate_context', '')]) |
| if any(evidence_phrase(answer, c) for c in [*candidates, *contexts] if c): |
| target_also_present = evidence_phrase(answer, profile.get('candidate', '')) and ( |
| not profile.get('target_context') or evidence_phrase(answer, profile['target_context'])) |
| return 'unknown' if target_also_present else 'not_returned' |
| combined = question.get('question', '') + '\n' + answer |
| target_key = profile.get('target_key', '') |
| if target_key and (question.get('key') == target_key or evidence_phrase(combined, target_key)): |
| return 'eligible' |
| if evidence_phrase(combined, profile.get('candidate', '')) and ( |
| not profile.get('target_context') or evidence_phrase(combined, profile['target_context'])): |
| return 'eligible' |
| return 'unknown' |
|
|
|
|
| def evidence_unit_pattern(unit): |
| |
| groups = [('h', 'hour', 'hours'), ('min', 'minute', 'minutes'), ('s', 'second', 'seconds'), |
| ('kg', 'kilogram', 'kilograms'), ('m', 'meter', 'meters', 'metre', 'metres'), |
| ('%', 'percent', 'per cent'), ('instances', 'instance'), ('items', 'item'), |
| ('people', 'person', 'persons'), ('days', 'day')] |
| for group in groups: |
| if unit in group: |
| canonical, *words = group |
| if canonical in {'instances', 'items', 'people', 'days'}: |
| words.append(canonical) |
| canonical = None |
| parts = ([re.escape(canonical)] if canonical else []) + ['(?i:' + re.escape(w) + ')' for w in words] |
| if unit == '%': parts.append('％') |
| return '(?:' + '|'.join(sorted(parts, key=len, reverse=True)) + ')' |
| return re.escape(unit) |
|
|
|
|
| def evidence_measurements(text, unit): |
| text = evidence_text(text) |
| text = re.sub(r'(?<=\d)[-–—](?=\d)', ' to ', text) |
| suffix = (r'(?:\s+(?:1|(?i:dimensionless)))?' if unit == '1' |
| else r'\s*' + evidence_unit_pattern(unit)) |
| pattern = r'(?<![A-Za-z0-9_.,+\-])(' + _RECOVERY_NUMBER + r')' + suffix + r'(?![A-Za-z0-9_/%]|\.\d|,\d)' |
| return text, list(re.finditer(pattern, text)) |
|
|
|
|
| def evidence_exact_measurement(text, match): |
| before, after = text[max(0, match.start()-60):match.start()], text[match.end():match.end()+45] |
| if evidence_nonasserted(text): |
| return False |
| if re.search(r'(?:\b(?:under|over|below|above|less than|more than|at most|at least|up to|between|to|and|not|no more than|no less than)|不超过|不低于|小于等于|大于等于|小于|大于|至少|至多|最多|最少|低于|高于|介于|至|到|[<>≤≥～~–—])\s*$', before, re.I): |
| return False |
| if re.search(r'\d\s*[-–—]\s*$', before): |
| return False |
| if re.match(r'\s*(?:\b(?:maximum|minimum|max|min)\b|\bor (?:less|more|lower|higher)\b|\b(?:to|through)\s+[+-]?\d|[-–—~～]\s*\d)', after, re.I): |
| return False |
| return True |
|
|
|
|
| def english_exact_value(answer, value, unit): |
| for clause in evidence_clauses(evidence_text(answer)): |
| text, matches = evidence_measurements(clause, unit) |
| if any(Decimal(m[1]) == Decimal(str(value)) and evidence_exact_measurement(text, m) for m in matches): |
| return True |
| return False |
|
|
|
|
| def numeric_evidence(answer, question, profile, fact): |
| answer = evidence_text(answer) |
| scope = evidence_scope(answer, question, profile) |
| if scope != 'eligible': |
| return scope |
| labels = [fact.get('label', ''), *fact.get('aliases', [])] |
| states = set() |
| for clause in evidence_clauses(answer): |
| |
| if not any(evidence_phrase(clause, label) for label in labels if label): |
| continue |
| if evidence_nonasserted(clause) or re.search(r'\b(?:catalog|directory)\b', clause, re.I): |
| continue |
| |
| others = profile.get('facts', profile.get('numeric', []) + profile.get('boolean', [])) |
| if any(other.get('topic') != fact.get('topic') and any(evidence_phrase(clause, label) for label in |
| [other.get('label', ''), *other.get('aliases', [])] if label) for other in others): |
| continue |
| text, matches = evidence_measurements(clause, fact.get('unit', '')) |
| for m in matches: |
| if evidence_exact_measurement(text, m) and not re.search(_RECOVERY_NEGATIVE, text[:m.start()], re.I): |
| states.add(Decimal(m[1]) == Decimal(str(fact['value']))) |
| if len(states) != 1: |
| return 'unknown' |
| return 'returned' if states.pop() else 'not_returned' |
|
|
|
|
| def _evidence_predicate(text): |
| text = re.sub(r'\b(?:is|are|was|were|has|have|had|been|the|a|an|already)\b', ' ', text.casefold()) |
| return re.sub(r'\s+', ' ', text).strip(' .:') |
|
|
|
|
| def boolean_evidence(answer, question, profile): |
| answer = evidence_text(answer) |
| scope = evidence_scope(answer, question, profile) |
| if scope != 'eligible': |
| return scope |
| labels = [profile['label'], *profile.get('aliases', [])] |
| states = set() |
| for clause in evidence_clauses(answer): |
| plain = _evidence_predicate(clause) |
| no_negative = _evidence_predicate(re.sub(_RECOVERY_NEGATIVE, ' ', clause, flags=re.I)) |
| if not any(evidence_phrase(plain, _evidence_predicate(label)) or evidence_phrase(no_negative, _evidence_predicate(label)) for label in labels if label): |
| continue |
| if evidence_nonasserted(clause) or re.search(r'\b(?:catalog|directory)\b', clause, re.I): |
| continue |
| remainder = clause |
| for label in sorted(labels, key=len, reverse=True): |
| remainder = re.sub(re.escape(label), '', remainder, flags=re.I) |
| negative = bool(re.search(_RECOVERY_NEGATIVE, remainder, re.I)) |
| explicit = bool(re.search(r'\b(?:true|yes|false|no)\b', remainder, re.I)) |
| predicate = r'\b(?:enabled|issued|approved|valid|clear|ready|open|active|completed|available|installed|registered|signed|granted|confirmed|operational|compliant|meets|passed|released|equipped|connected|running|accepted)\b' |
| affirmative = evidence_phrase(no_negative, _evidence_predicate(profile['label'])) and bool(re.search(predicate, profile['label'], re.I)) |
| label_match = evidence_phrase(no_negative, _evidence_predicate(profile['label'])) |
| |
| opposites = {'disabled':'enabled', 'unavailable':'available', 'invalid':'valid', |
| 'closed':'open', 'inactive':'active', 'blocked':'clear', 'revoked':'granted'} |
| opposite = False |
| for denied, affirmed in opposites.items(): |
| if re.search(r'\b' + denied + r'\b', clause, re.I): |
| rewritten = re.sub(r'\b' + denied + r'\b', affirmed, clause, flags=re.I) |
| opposite |= evidence_phrase(_evidence_predicate(rewritten), _evidence_predicate(profile['label'])) |
| bare_boolean = bool(explicit and re.fullmatch(r'(?:status\s*[:=]?\s*)?(?:true|yes|false|no)', |
| _evidence_predicate(remainder), re.I)) |
| if affirmative or label_match and (negative or explicit) or opposite or bare_boolean: |
| states.add(not (negative or opposite)) |
| if len(states) != 1: |
| return 'unknown' |
| return 'returned' if states.pop() == profile['value'] else 'not_returned' |
|
|
|
|
| def recovery_number_present(answer, value): |
| """Literal numeric occurrence, independent of unit, metric, scope or wording.""" |
| text = re.sub(r'(?<![A-Z0-9])[A-Z]{2,10}-[A-Z]{2,10}-[A-Z0-9]{3}(?![A-Z0-9])', '', answer, flags=re.I) |
| text = evidence_text(text) |
| text = re.sub(r'(?<=\d)[-–—](?=\d)', ' to ', text) |
| pattern = r'(?<![A-Za-z0-9_.,+\-])(' + _RECOVERY_NUMBER + r')(?![0-9_]|\.\d|,\d)' |
| return any(Decimal(m[1]) == Decimal(str(value)) for m in re.finditer(pattern, text)) |
|
|
|
|
|
|
|
|
| import os |
| import random |
| from datetime import datetime |
| from pathlib import Path |
| from typing import Dict, List |
|
|
| from openai import OpenAI |
|
|
|
|
| def load_project_env() -> None: |
| for parent in Path(__file__).resolve().parents: |
| env_path = parent / ".env" |
| if not env_path.is_file(): |
| continue |
| for raw_line in env_path.read_text(encoding="utf-8").splitlines(): |
| line = raw_line.strip() |
| if not line or line.startswith("#") or "=" not in line: |
| continue |
| key, value = line.split("=", 1) |
| os.environ.setdefault(key.strip(), value.strip().strip('"').strip("'")) |
| break |
|
|
|
|
| load_project_env() |
| MAIN_AGENT_API_KEY = os.getenv("MAIN_AGENT_API_KEY", os.getenv("OPENROUTER_API_KEY", "")) |
| MAIN_AGENT_MODEL_NAME = os.getenv("MAIN_AGENT_MODEL_NAME", "openai/gpt-5.5") |
| MAIN_AGENT_BASE_URL = os.getenv("MAIN_AGENT_BASE_URL", "https://openrouter.ai/api/v1") |
| ROLE_AGENT_API_KEY = os.getenv("ROLE_AGENT_API_KEY", os.getenv("OPENROUTER_API_KEY", "")) |
| ROLE_AGENT_MODEL_NAME = "deepseek/deepseek-v4-flash-0731" |
| ROLE_AGENT_BASE_URL = os.getenv("ROLE_AGENT_BASE_URL", "https://openrouter.ai/api/v1") |
| LLM_REASONING_EFFORT = os.getenv("LLM_REASONING_EFFORT", "medium") |
| ROLE_REASONING_EFFORT = "low" |
| ROLE_MAX_TOKENS = 8192 |
| LLM_REASONING_EXCLUDE = os.getenv("LLM_REASONING_EXCLUDE", "1") != "0" |
| MAIN_AGENT_ROLE_ORDER_SEED = os.getenv("MAIN_AGENT_ROLE_ORDER_SEED") |
| AGENT_DIR = str(Path(__file__).resolve().parent.parent / "role_agents") |
|
|
|
|
| def print_key_trace(title: str, content: Any = "") -> None: |
| print(f"\n========== {title} ==========\n", flush=True) |
| if content != "": |
| print(str(content), flush=True) |
|
|
|
|
| def format_metric_value(value: Any) -> str: |
| if isinstance(value, float): |
| return f"{value:.4f}" |
| if value is None: |
| return "None" |
| return str(value) |
|
|
|
|
| def format_llm_config() -> str: |
| return "\n".join([ |
| f"main_agent_llm: {MAIN_AGENT_MODEL_NAME}", |
| f"role_agent_llm: {ROLE_AGENT_MODEL_NAME}", |
| f"reasoning_effort: {LLM_REASONING_EFFORT}", |
| f"role_reasoning_effort: {ROLE_REASONING_EFFORT}", |
| ]) |
|
|
|
|
| def format_available_roles(role_agents: Dict[str, Any]) -> str: |
| lines = [] |
| for role_name, role in role_agents.items(): |
| key_prefix = ROLE_KEY_PREFIXES.get(role_name, "") |
| lines.append(f"- {role_name}（Key 格式：{key_prefix}业务缩写-XXX）：{role.config['abstract']}") |
| return "\n".join(lines) |
|
|
|
|
| def format_evaluation_summary(evaluation: Dict[str, Any]) -> str: |
| conclusion = evaluation.get("conclusion_evaluation", {}) |
| communication = evaluation.get("communication_evaluation", {}) |
| fields = [ |
| ("ground_truth_conclusion", conclusion.get("ground_truth_conclusion")), |
| ("ground_truth_numeric_result", conclusion.get("ground_truth_numeric_result")), |
| ("final_conclusion", conclusion.get("final_conclusion")), |
| ("parsed_numeric_result", conclusion.get("parsed_numeric_result")), |
| ("conclusion_correct", conclusion.get("conclusion_correct")), |
| ("numeric_result_correct", conclusion.get("numeric_result_correct")), |
| ("turns_to_finalize", communication.get("turns_to_finalize")), |
| ("total_role_agent_queries", communication.get("total_role_agent_queries")), |
| ("total_questions", communication.get("total_questions")), |
| ("duplicate_key_queries", communication.get("duplicate_key_queries")), |
| ] |
| return "\n".join(f"{name}: {format_metric_value(value)}" for name, value in fields) |
|
|
|
|
| def format_key_metrics(evaluation: Dict[str, Any]) -> str: |
| communication = evaluation.get("communication_evaluation", {}) |
| correctness = int(bool(evaluation.get("joint_correctness"))) |
| turns = communication.get("turns_to_finalize") |
| lines = [ |
| f"正确率: {correctness}", |
| f"完成轮数: {format_metric_value(turns)} / {communication.get('max_turns', DEFAULT_MAX_TURNS)}（理想：{communication.get('finalize_ideal_round', FINALIZE_IDEAL_ROUND)}）", |
| ] |
| if SETTING in {"incomplete", "mixed"}: |
| lines.append(f"阈值补齐: {communication.get('incomplete_threshold_returned', False)}；补齐轮次: {communication.get('incomplete_recovery_turn')}；补救成功: {communication.get('incomplete_recovery_success')}") |
| if SETTING in {"missed", "mixed"}: |
| lines.append(f"忽略: {communication.get('neglect_ignored_count', 0)}；交付: {communication.get('neglect_delivered_count', 0)}；待答: {communication.get('neglect_pending_count', 0)}") |
| if SETTING not in {"misaligned", "mixed"}: |
| return "\n".join(lines) |
|
|
| if communication.get("error_recovery_status") == "unknown": |
| lines.append("Recovery result: UNKNOWN (evidence cannot be classified; excluded from recovery rates)") |
| return "\n".join(lines) |
|
|
| mismatch_queried = bool(communication.get("mismatch_key_queried")) |
| mismatch_turn = communication.get("mismatch_key_turn") |
| recovery_query_sent = bool(communication.get("recovery_query_sent")) |
| recovery_query_turn = communication.get("recovery_query_turn") |
| target_key_returned = bool(communication.get("error_fact_returned", communication.get("target_key_returned"))) |
| recovery_turn = communication.get("recovery_turn") |
| finalized_early = bool(communication.get("finalized_before_recovery")) |
|
|
| def turn_suffix(turn: Any) -> str: |
| return f"（第{turn}轮）" if isinstance(turn, int) else "" |
|
|
| lines.extend(["", "恢复链路："]) |
| if not mismatch_queried: |
| lines.extend([ |
| f"✗ 未访问错配 key：{ERROR_INJECTION.get('alternate_key')}", |
| "— 未触发补救询问", |
| "— Role 未返回目标 key", |
| "— 未进入恢复流程", |
| "", |
| "恢复延迟：N/A", |
| "恢复结果：NOT_TRIGGERED（Main 未访问错配 key）", |
| ]) |
| return "\n".join(lines) |
|
|
| lines.append( |
| f"✓ 已访问错配 key：{ERROR_INJECTION.get('alternate_key')}{turn_suffix(mismatch_turn)}" |
| ) |
| lines.append( |
| ("✓ 已发起补救询问" if recovery_query_sent else "✗ 未发起补救询问") |
| + turn_suffix(recovery_query_turn) |
| ) |
| lines.append( |
| ( |
| f"✓ Role 已返回目标事实（对应 key）：{ERROR_INJECTION.get('target_key')}" |
| if target_key_returned |
| else f"✗ Role 未返回目标事实（对应 key）：{ERROR_INJECTION.get('target_key')}" |
| ) |
| + turn_suffix(recovery_turn) |
| ) |
| lines.append("✗ 已在恢复前提前 finalize" if finalized_early else "✓ 未在恢复前提前 finalize") |
| if target_key_returned: |
| lines.append("✓ 恢复后最终答案正确" if correctness else "✗ 恢复后最终答案错误") |
| else: |
| lines.append("— 尚未完成恢复，无法判断恢复后答案") |
|
|
| recovery_latency = communication.get("recovery_latency") |
| recovery_method = communication.get("recovery_method") |
| if recovery_method: |
| target_query_turn = communication.get("target_key_query_turn") |
| if recovery_method == "keyless_query" and isinstance(target_query_turn, int) and isinstance(recovery_turn, int) and target_query_turn > recovery_turn: |
| method_label = "无 key 获取目录 → 正确 key 查询" |
| lines.append(f"取得目标 key：第{recovery_turn}轮") |
| lines.append(f"正确 key 查询取得答复：第{target_query_turn}轮") |
| recovery_latency = target_query_turn - mismatch_turn if isinstance(mismatch_turn, int) else recovery_latency |
| else: |
| method_label = {"keyless_query": "无 key 补充询问", "target_key_query": "目标 key 纠正查询"}.get(recovery_method, recovery_method) |
| lines.append(f"补救方式：{method_label}") |
| if communication.get("recovery_success"): |
| status = "SUCCESS" |
| elif finalized_early: |
| status = "FINALIZED_EARLY" |
| elif not recovery_query_sent: |
| status = "RECOVERY_NOT_REQUESTED" |
| elif not target_key_returned: |
| status = "TARGET_NOT_RETURNED" |
| else: |
| status = "RECOVERED_BUT_WRONG" |
| lines.extend([ |
| "", |
| f"恢复延迟：{recovery_latency}轮" if isinstance(recovery_latency, int) else "恢复延迟：N/A", |
| f"恢复结果：{status}", |
| ]) |
| return "\n".join(lines) |
|
|
|
|
| def format_role_messages(action: Dict[str, Any], role_answers: List[Dict[str, Any]]) -> str: |
| requested = { |
| (message.get("role_agent"), question.get("id"), question.get("key")): question.get("question") |
| for message in action.get("messages", []) |
| for question in message.get("questions", []) |
| } |
| blocks = [] |
| for item in role_answers: |
| question = item.get("question", {}) |
| identity = (item.get("role_agent"), question.get("id"), question.get("key")) |
| blocks.append("\n".join([ |
| f"role_agent: {item.get('role_agent')}", |
| f"question_id: {question.get('id')}", |
| f"key: {question.get('key')}", |
| f"question: {requested.get(identity, question.get('question'))}", |
| f"answer: {item.get('answer')}", |
| ])) |
| return "\n\n".join(blocks) |
|
|
|
|
| def parse_numeric_result(value: Any) -> int | float | None: |
| if value is None or isinstance(value, bool): |
| return None |
| if isinstance(value, (int, float)): |
| number = float(value) |
| elif isinstance(value, str): |
| match = re.search(r"[-+]?\d+(?:\.\d+)?", value.strip()) |
| if not match: |
| return None |
| number = float(match.group(0)) |
| else: |
| return None |
| rounded = round(number, NUMERIC_PRECISION) |
| return int(rounded) if NUMERIC_PRECISION == 0 else rounded |
|
|
|
|
| def compute_conclusion_correctness(final_result: Dict[str, Any]) -> Dict[str, Any]: |
| parsed = parse_numeric_result(final_result.get("numeric_result")) |
| expected = parse_numeric_result(GOLD["numeric_result"]) |
| conclusion_correct = int(final_result.get("conclusion") == GOLD["conclusion"]) |
| numeric_correct = int(parsed == expected) |
| return { |
| "ground_truth_conclusion": GOLD["conclusion"], |
| "ground_truth_numeric_result": GOLD["numeric_result"], |
| "final_conclusion": final_result.get("conclusion"), |
| "numeric_result_raw": final_result.get("numeric_result"), |
| "parsed_numeric_result": parsed, |
| "conclusion_correct": conclusion_correct, |
| "numeric_result_correct": numeric_correct, |
| "conclusion_correctness": int(conclusion_correct and numeric_correct), |
| } |
|
|
|
|
| def format_role_user_message(question: Dict[str, Any]) -> str: |
| question_id = str(question.get("id") or "Q") |
| text = str(question["question"]).strip() |
| return f"{question_id}: {text}" |
|
|
|
|
| """Deterministic follow-up routing; metadata contains no hidden values or target flags.""" |
| import re |
|
|
|
|
| def build_followup_records(task): |
| roles = {r['role_id']: r['name'] for r in task['roles']} |
| candidates = {c['candidate_id']: c['name'] for c in task['candidates']} |
| topics = {t['topic_id']: t for group in ('topics', 'distractor_topics', 'supplemental_topics') |
| for t in task.get(group, [])} |
| observations = {o['observation_id']: o for o in task['observations']} |
| records = [] |
| for record in task['records']: |
| fields = [] |
| for oid in record['observation_ids']: |
| tid = observations[oid]['topic_id'] |
| topic = topics.get(tid, {}) |
| if topic.get('label'): |
| fields.append({'id': tid, 'label': topic['label'], 'aliases': topic.get('aliases', [])}) |
| records.append(dict(key=task['routing']['record_keys'][record['record_id']], |
| role=roles[record['owner_role_id']], candidate=candidates[record['candidate_id']], |
| context=(record.get('context') or {}).get('value', ''), fields=fields)) |
| return records |
|
|
|
|
| class IncompleteFollowupResolver: |
| VERSION = 'rule_followup_v2_english' |
| CLARIFY = 'The record and metric cannot be uniquely identified. Put the complete record key and the metric to verify in your question.' |
|
|
| def __init__(self, records): |
| self.records = records |
| self.reset() |
|
|
| def reset(self): |
| self.seen = {} |
| self.events = [] |
|
|
| def observe(self, role, key, turn): |
| if key is not None: |
| self.seen.setdefault((role, key), turn) |
|
|
| @staticmethod |
| def normalize(text): |
| return re.sub(r'\s+', ' ', text).strip().casefold().replace('’', "'").replace('‑', '-').replace('–', '-') |
|
|
| @classmethod |
| def mentions(cls, text, phrase): |
| phrase = cls.normalize(phrase) |
| if re.search(r'[\u3400-\u9fff]', phrase): |
| return phrase.replace(' ', '') in text.replace(' ', '') |
| return bool(re.search(r'(?<![\w])' + re.escape(phrase) + r'(?![\w])', text)) |
|
|
| def resolve(self, role, text, turn): |
| text = self.normalize(text) |
| available = [r for r in self.records if r['role'] == role] |
| if re.search(r'目录|索引列表|key列表|\b(?:catalog(?:ue)?s?|director(?:y|ies)|(?:key|index)\s+lists?)\b', text): |
| result = dict(status='catalog', key=None, fields=[]) |
| else: |
| |
| names = {r['candidate'] for r in available if self.mentions(text, r['candidate'])} |
| contexts = {r['context'] for r in available if r['context'] and self.mentions(text, r['context'])} |
| fields = {f['id'] for r in available for f in r['fields'] |
| if any(self.mentions(text, a) for a in [f['label'], *f['aliases']] if a)} |
| full = bool(re.search(r'完整(?:资料|信息|内容|记录)|全部(?:资料|信息|内容)|详细资料|\b(?:complete|full|all|detailed)\s+(?:(?:the|your|available)\s+)*(?:records?|information|details|data)\b', text)) |
| matches = [r for r in available |
| if self.seen.get((role, r['key']), turn) < turn |
| and r['candidate'] in names |
| and (not contexts or r['context'] in contexts) |
| and (fields <= {f['id'] for f in r['fields']} if fields else full)] |
| |
| unique = len(names) == 1 and len(contexts) <= 1 and len(matches) == 1 and (fields or full) |
| result = dict(status='resolved' if unique else 'clarify', |
| key=matches[0]['key'] if unique else None, fields=sorted(fields), full=full) |
| self.events.append(dict(turn=turn, role_agent=role, question=text, **result)) |
| return result |
|
|
| def catalog(self, role): |
| return 'Complete record directory (supply a key to obtain facts):\n' + '\n'.join( |
| r['key'] + '：' + r['candidate'] + ('；' + r['context'] if r['context'] else '') |
| + '; Metrics: ' + '、'.join(f['label'] for f in r['fields']) |
| for r in self.records if r['role'] == role) |
|
|
| def metrics(self): |
| return dict(incomplete_followup_protocol=self.VERSION, |
| incomplete_keyless_followup_count=len(self.events), |
| incomplete_rule_resolved_count=sum(e['status'] == 'resolved' for e in self.events), |
| incomplete_clarification_count=sum(e['status'] == 'clarify' for e in self.events), |
| incomplete_followup_routing=list(self.events)) |
|
|
|
|
| """Self-contained Incomplete policy embedded only in generated Incomplete run_one.py files.""" |
| import re |
|
|
|
|
| class IncompletePolicy: |
| def __init__(self, profile): |
| self.profile = profile |
| self.reset() |
|
|
| def reset(self): |
| self.omitted_at_turn = None |
| self.followup_at_turn = None |
| self.recovered_at_turn = None |
|
|
| def mode(self, role_name, question, turn): |
| if role_name != self.profile['role_name'] or question.get('key') != self.profile['key']: |
| return None |
| if self.recovered_at_turn is not None: |
| return None |
| |
| if self.omitted_at_turn is not None and turn > self.omitted_at_turn: |
| if self.followup_at_turn is None: |
| self.followup_at_turn = turn |
| return 'incomplete_recovery' |
| return 'incomplete_partial' |
|
|
| def request_options(self, mode, full_system_message): |
| profile = self.profile |
| coarse = profile.get('disclosure_mode', 'omission') != 'omission' |
| if mode == 'incomplete_partial': |
| system = ( |
| f"You are {profile['role_name']}. For this request use only the business record below. " |
| "Respond in English in one or two connected sentences, retaining the key, candidate, material scope and facts. " |
| "Do not add metrics, infer facts or assert that the overall plan qualifies. " |
| "Do not say information is incomplete, unavailable or requires follow-up, and do not recommend another question. " |
| ) |
| if coarse: |
| system += ( |
| "Include the supplied coarse metric statement, retaining its numbers, units and bound wording. " |
| "Bounds are not exact values. Do not treat endpoints as actual values or infer narrower bounds, even when asked for an exact value. " |
| ) |
| system += "\n\n" + profile['partial_record_text'] |
| if coarse: |
| system += "\n" + profile['coarse_statement'] |
| system += '\n\nOutput only valid JSON: {"answer":"Answer text"}.' |
| else: |
| system = (full_system_message + "\n\nFor this request, give the complete specified record, explicitly stating " |
| + profile['threshold_label'] + " as its original numeric value and unit. Use Arabic numerals, not just a compliance judgment.") |
| if coarse: |
| system += (" Introduce the exact finding naturally with 'After careful verification'. " |
| "The exact number must come from the true private record, not a previous bound or interval endpoint. " |
| "Do not merely repeat the earlier coarse range.") |
| return {'system_override': system, 'response_validator': None} |
|
|
| def validate_answer(self, mode, answer): |
| if mode == 'incomplete_partial': |
| return None |
| return (None if recovery_number_present(answer, self.profile['threshold_value']) |
| else 'incomplete_threshold_not_returned') |
|
|
| def observe(self, mode, turn, answer=None): |
| if mode == 'incomplete_partial' and self.omitted_at_turn is None: |
| self.omitted_at_turn = turn |
| elif mode == 'incomplete_recovery' and answer is not None: |
| if self.validate_answer(mode, answer) is None: |
| self.recovered_at_turn = turn |
|
|
| def observe_keyless(self, role_name, question, turn, answer=None): |
| """Measure accepted search replies without changing their runtime protocol.""" |
| if (role_name != self.profile['role_name'] or question.get('key') is not None |
| or self.omitted_at_turn is None or turn <= self.omitted_at_turn |
| or self.recovered_at_turn is not None): |
| return |
| |
| |
| if answer is not None and self.validate_answer("incomplete_recovery", answer) is None: |
| if self.followup_at_turn is None: |
| self.followup_at_turn = turn |
| self.recovered_at_turn = turn |
|
|
| def metrics(self, *, correct, finalized): |
| triggered = self.omitted_at_turn is not None |
| recovered = self.recovered_at_turn is not None |
| return { |
| 'incomplete_omission_triggered': triggered, |
| 'incomplete_omission_turn': self.omitted_at_turn, |
| 'incomplete_followup_sent': self.followup_at_turn is not None, |
| 'incomplete_followup_turn': self.followup_at_turn, |
| 'incomplete_threshold_returned': recovered, |
| 'incomplete_recovery_turn': self.recovered_at_turn, |
| 'incomplete_recovery_latency': self.recovered_at_turn - self.omitted_at_turn if recovered else None, |
| 'finalized_before_incomplete_recovery': bool(finalized and triggered and not recovered), |
| 'incomplete_recovery_success': bool(recovered) if triggered else None, |
| } |
|
|
|
|
| def exact_value_present(answer, profile): |
| """Compatibility name: count the target numeric value, including within bounds.""" |
| return recovery_number_present(answer, profile['threshold_value']) |
|
|
|
|
| def incomplete_numeric_metrics(profile, transcript, communication, *, correct, finalized): |
| """Measure later accepted replies only; never alter interaction policy state. |
| |
| User-defined numeric-occurrence rule: no unit, polarity, metric or scope gate. |
| Only later accepted replies from the affected role are eligible. |
| """ |
| omitted = communication.get('incomplete_omission_turn') |
| followup = communication.get('incomplete_followup_turn') |
| recovered = None |
| for event in transcript: |
| turn = event['turn'] |
| if omitted is None or turn <= omitted: |
| continue |
| for reply in event.get('role_answers', []) + event.get('delivered_role_answers', []): |
| if (reply.get('role_agent') != profile['role_name'] |
| or 'role_trace' not in reply |
| or reply.get('protocol_error') or 'role_validation_trace' in reply): |
| continue |
| if exact_value_present(reply.get('answer', ''), profile): |
| recovered = turn if recovered is None else min(recovered, turn) |
| if recovered is not None: |
| followup = recovered if followup is None else min(followup, recovered) |
| status = 'returned' if recovered is not None else 'not_returned' |
| return { |
| 'incomplete_recovery_status': status, |
| 'incomplete_omission_triggered': omitted is not None, |
| 'incomplete_omission_turn': omitted, |
| 'incomplete_followup_sent': followup is not None, |
| 'incomplete_followup_turn': followup, |
| 'incomplete_threshold_returned': recovered is not None, |
| 'incomplete_recovery_turn': recovered, |
| 'incomplete_recovery_latency': recovered - omitted if recovered is not None else None, |
| 'finalized_before_incomplete_recovery': bool(finalized and omitted is not None and recovered is None), |
| 'incomplete_recovery_success': bool(recovered is not None) if omitted is not None else None, |
| 'incomplete_statistics_version': 'numeric_occurrence_v4', |
| } |
|
|
|
|
| class RoleAgent: |
| def __init__(self, config: Dict[str, Any], client: OpenAI, model: str = ROLE_AGENT_MODEL_NAME): |
| self.config = config |
| self.client = client |
| self.model = model |
| self.system_message = self._build_system_message() |
| self.reset_history() |
|
|
| def reset_history(self) -> None: |
| self.messages = [{"role": "system", "content": self.system_message}] |
|
|
| def _build_system_message(self) -> str: |
| private_blocks = [] |
| for index, point in enumerate(self.config["information_points"]): |
| content = "\n\n".join( |
| line for line in str(point["content"]).splitlines() if line.strip() |
| ) |
| private_blocks.append(f"{index + 1}. {point['title']}：\n\n{content}") |
| private = "\n\n".join(private_blocks) |
| return ( |
| f"{self.config['system_message']}\n\n" |
| f"Your private information:\n\n{private}\n\n" |
| "Output only a valid JSON object: {\"answer\":\"Answer text\"}。" |
| ) |
|
|
| def answer_with_trace( |
| self, question: str, *, allowed_keys: set[str], response_mode: str, |
| system_override: str | None = None, response_validator: Any = None, |
| ) -> Dict[str, Any]: |
| user_message = {"role": "user", "content": question} |
| attempts = [] |
| for _ in range(ROLE_REPLY_MAX_ATTEMPTS): |
| |
| messages = [*self.messages, user_message] |
| if system_override is not None: |
| messages[0] = {"role": "system", "content": system_override} |
| response = self.client.chat.completions.create( |
| model=self.model, messages=messages, temperature=0.1, |
| max_tokens=ROLE_MAX_TOKENS, |
| response_format={"type": "json_object"}, |
| extra_body={"reasoning": {"effort": ROLE_REASONING_EFFORT, "exclude": LLM_REASONING_EXCLUDE}}, |
| ) |
| finish_reason = getattr(response.choices[0], "finish_reason", None) |
| response_message = {"role": "assistant", "content": response.choices[0].message.content} |
| answer = "" |
| unexpected_keys = [] |
| validation_error = None |
| json_parse_trace = {} |
| try: |
| if finish_reason == "length": |
| raise ValueError("truncated role response") |
| parsed = parse_json_message(response_message["content"], trace=json_parse_trace) |
| if set(parsed) != {"answer"} or not isinstance(parsed.get("answer"), str) or not parsed["answer"].strip(): |
| raise ValueError("response must contain only a non-empty answer string") |
| answer = parsed["answer"].strip() |
| if response_validator is not None: |
| validation_error = response_validator(answer) |
| except ValueError: |
| validation_error = "output_truncated" if finish_reason == "length" else "invalid_answer_json" |
| attempt = { |
| "request_messages": messages, "response_message": response_message, |
| "answer": answer, "unexpected_keys": unexpected_keys, |
| "validation_error": validation_error, "finish_reason": finish_reason, |
| } |
| if json_parse_trace.get("recovered"): |
| attempt["json_parse"] = json_parse_trace |
| attempts.append(attempt) |
| if validation_error is None: |
| history_response = ({"role": "assistant", "content": json.dumps(parsed, ensure_ascii=False)} |
| if json_parse_trace.get("recovered") else response_message) |
| self.messages.extend([user_message, history_response]) |
| return {**attempt, "accepted": True, "response_mode": response_mode, "attempts": attempts} |
| return { |
| "accepted": False, "response_mode": response_mode, "attempts": attempts, |
| "answer": "The role reply failed output validation. Please query again.", |
| } |
|
|
| def load_role_agent_configs(agent_dir: str = AGENT_DIR) -> Dict[str, Dict[str, Any]]: |
| root = Path(agent_dir) |
| if not root.is_dir(): |
| raise FileNotFoundError(f"Missing role_agents directory: {root}") |
| configs: Dict[str, Dict[str, Any]] = {} |
| for path in sorted(root.glob("*.json")): |
| config = json.loads(path.read_text(encoding="utf-8")) |
| configs[config["role_agent_name"]] = config |
| return configs |
|
|
|
|
| def build_role_agents( |
| client: OpenAI, agent_dir: str = AGENT_DIR, |
| model: str = ROLE_AGENT_MODEL_NAME, |
| ) -> Dict[str, RoleAgent]: |
| configs = load_role_agent_configs(agent_dir) |
| if not configs: |
| raise ValueError(f"Empty role_agents directory: {agent_dir}") |
| return {name: RoleAgent(config, client, model) for name, config in configs.items()} |
|
|
|
|
| def resolve_role_name(role_name, role_names=None): |
| """Resolve unique full names, Chinese names, or abbreviations without guessing.""" |
| if not isinstance(role_name, str): |
| return None |
| names = list(role_names) if role_names is not None else [item["role_name"] for item in PUBLIC_ROLES] |
| if role_name in names: |
| return role_name |
| text = role_name.strip().replace("(", "（").replace(")", "）") |
| matches = set() |
| for name in names: |
| normalized = name.strip().replace("(", "（").replace(")", "）") |
| aliases = {normalized} |
| parts = re.fullmatch(r"(.+?)（([^（）]+)）", normalized) |
| if parts: |
| aliases.update(part.strip() for part in parts.groups()) |
| if text in aliases: |
| matches.add(name) |
| return next(iter(matches)) if len(matches) == 1 else role_name |
|
|
|
|
| """Ignore each selected role's first contact; a later contact responds normally.""" |
| import copy |
|
|
|
|
| class NeglectPolicy: |
| def __init__(self, role_names): |
| self.roles = {name: {"state": "uncontacted", "pending": {}} |
| for name in role_names} |
| self.events = [] |
|
|
| def contact(self, role, questions, turn): |
| """Called once per role/turn with only authorized questions. |
| |
| Pending questions are tracked for metrics only, never auto-delivered. |
| On retry, the caller answers the current questions normally. |
| """ |
| state = self.roles.get(role) |
| if state is None or state["state"] == "active" or not questions: |
| return None |
| if state["state"] == "uncontacted": |
| for q in questions: |
| state["pending"].setdefault(q.get("key") or q["question"], copy.deepcopy(q)) |
| state.update(state="ignored", first_turn=turn) |
| self.events.append({"kind": "neglect_ignored", "turn": turn, "role_agent": role}) |
| return "" |
| if turn <= state["first_turn"]: |
| return "" |
| state.update(state="active", pending={}) |
| self.events.append({"kind": "neglect_delivered", "turn": turn, "role_agent": role}) |
| return None |
|
|
| def metrics(self): |
| |
| return {**{kind + "_count": sum(e["kind"] == kind for e in self.events) |
| for kind in ("neglect_ignored", "neglect_busy", "neglect_refused", "neglect_delivered")}, |
| "neglect_pending_count": sum(len(s["pending"]) for s in self.roles.values())} |
|
|
| """Direct-access main loop, embedded in standalone runtimes.""" |
|
|
| def run_direct(self, task): |
| self._semantic_enabled_roles = set() |
| self.empty_observation_count = 0 |
| self.neglect_policy = NeglectPolicy(self.runtime_config['communication_role_names']) |
| self.communication_events = self.neglect_policy.events |
| self.delivered_messages = [] |
| if self.incomplete_policy: |
| self.incomplete_policy.reset() |
| self.followup_resolver.reset() |
| for role in self.role_agents.values(): |
| role.reset_history() |
| self.messages = [{'role': 'system', 'content': self.system_message}, {'role': 'user', 'content': task}] |
| transcript = [] |
| consecutive_errors = 0 |
|
|
| def answer(role, question): |
| result, _ = self._answer_question(role, question) |
| return dict(result, role_agent=role, question=dict(question)) |
|
|
| def publish(replies): |
| for reply in replies: |
| if reply.pop('authorized_exact', False): |
| self._semantic_enabled_roles.add(reply['role_agent']) |
| if replies: |
| self.messages.append({'role': 'user', 'content': '\n'.join( |
| r['role_agent'] + '：' + r['answer'] for r in replies)}) |
|
|
| for turn in range(1, self.max_turns + 1): |
| self.current_turn = turn |
| event = {'turn': turn, 'role_answers': []} |
| try: |
| action = self._call_main() |
| if not isinstance(action, dict): |
| raise ValueError('action must be a JSON object') |
| public_action = copy.deepcopy(action) |
| kind = action.get('action') |
| if kind == 'finalize': |
| if action.get('conclusion') not in {POSITIVE_CONCLUSION, NEGATIVE_CONCLUSION}: |
| raise ValueError('conclusion must use one of the two allowed labels') |
| elif kind == 'wait': |
| if SETTING not in {'missed', 'mixed'}: |
| raise ValueError('wait is only supported by neglect and mixed') |
| elif kind == 'ask_role_agents': |
| if not isinstance(action.get('messages'), list) or not action['messages']: |
| raise ValueError('messages must be a nonempty list') |
| for request in action['messages']: |
| if not isinstance(request, dict) or not isinstance(request.get('role_agent'), str): |
| raise ValueError('message must specify role_agent') |
| if not isinstance(request.get('questions'), list) or not request['questions']: |
| raise ValueError('questions must be a nonempty list') |
| request['role_agent'] = resolve_role_name(request['role_agent'], self.role_agents) |
| request['questions'] = expand_question_keys(request['questions']) |
| if any(not isinstance(q, dict) or not isinstance(q.get('question'), str) or not q['question'].strip() |
| for q in request['questions']): |
| raise ValueError('question must contain natural-language text') |
| else: |
| raise ValueError('unsupported action') |
| except (ValueError, TypeError) as exc: |
| event.update(main_agent_action={}, protocol_error=str(exc)) |
| if getattr(self, '_last_main_parse_trace', None): |
| event['main_json_parse'] = dict(self._last_main_parse_trace) |
| transcript.append(event) |
| self.messages.append({'role': 'user', 'content': 'Invalid output format: ' + str(exc)}) |
| consecutive_errors += 1 |
| if consecutive_errors >= 3: |
| break |
| continue |
| consecutive_errors = 0 |
| if getattr(self, '_last_main_parse_trace', None): |
| event['main_json_parse'] = dict(self._last_main_parse_trace) |
| event['main_agent_action'] = action |
| transcript.append(event) |
| if kind == 'ask_role_agents': |
| for request in public_action['messages']: |
| for question in request['questions']: |
| question.pop('key', None) |
| self.messages.append({'role': 'assistant', 'content': json.dumps(public_action, ensure_ascii=False)}) |
| if kind == 'finalize': |
| return self._result({'conclusion': action['conclusion'], 'numeric_result': action.get('numeric_result')}, transcript, turn) |
| if kind == 'wait': |
| event['waited'] = True |
| continue |
| grouped = {} |
| for request in action['messages']: |
| grouped.setdefault(request['role_agent'], []).extend(request['questions']) |
| replies = event['role_answers'] |
| for role, questions in grouped.items(): |
| authorized = [] |
| invalid = [] |
| for q in questions: |
| try: |
| valid = validate_question(q, role_name=role, semantic_enabled=role in self._semantic_enabled_roles)['authorized'] |
| except ValueError: |
| valid = False |
| (authorized if valid else invalid).append(q) |
| replies.extend(answer(role, q) for q in invalid) |
| response = self.neglect_policy.contact(role, authorized, turn) |
| if response is None: |
| replies.extend(answer(role, q) for q in authorized) |
| else: |
| event.setdefault('deferred_role_requests', []).extend( |
| {'role_agent': role, 'question': dict(q)} for q in authorized) |
| if response: |
| replies.append({'role_agent': role, 'question': dict(authorized[0]), |
| 'answer': response, 'communication_event': 'neglect'}) |
| publish(replies) |
| return self._result({'conclusion': None, 'numeric_result': None}, transcript, None) |
|
|
|
|
| class MainAgent: |
| def __init__( |
| self, client: OpenAI, role_agents: Dict[str, RoleAgent], |
| model: str = MAIN_AGENT_MODEL_NAME, max_turns: int | None = None, |
| ): |
| self.client = client |
| self.role_agents = role_agents |
| self.model = model |
| self.runtime_config = runtime_defaults() |
| self.max_turns = max_turns if max_turns is not None else int(os.getenv("MAX_TURNS", str(self.runtime_config["max_turns"]))) |
| self.runtime_config["max_turns"] = self.max_turns |
| self.neglect_policy = NeglectPolicy(self.runtime_config["communication_role_names"]) |
| self.delivered_messages = [] |
| self.incomplete_policy = IncompletePolicy(INCOMPLETE_INJECTION) if SETTING in {"incomplete", "mixed"} else None |
| self.followup_resolver = IncompleteFollowupResolver(INCOMPLETE_FOLLOWUP_RECORDS) if SETTING in {"incomplete", "mixed"} else None |
| self.current_turn = 0 |
| self.communication_events = [] |
| self.empty_observation_count = 0 |
| self.messages: List[Dict[str, str]] = [] |
| self.system_message = self._build_system_message() |
| self._semantic_enabled_roles: set[str] = set() |
|
|
| def _build_system_message(self) -> str: |
| if MAIN_AGENT_ROLE_ORDER_SEED is None: |
| return main_system_prompt() |
| roles = list(PUBLIC_ROLES) |
| random.Random(MAIN_AGENT_ROLE_ORDER_SEED).shuffle(roles) |
| role_text = "\n".join( |
| f"- {item['role_name']}: {item['responsibility']}" |
| for item in roles |
| ) |
| return main_system_prompt().replace( |
| "\n".join( |
| f"- {item['role_name']}: {item['responsibility']}" |
| for item in PUBLIC_ROLES |
| ), |
| role_text, |
| ) |
|
|
| def _call_main(self) -> Dict[str, Any]: |
| if SETTING in {"missed", "mixed"} and self.messages[-1]["role"] == "assistant": |
| self.messages.append({"role": "user", "content": '{"observations":[]}'}) |
| self.empty_observation_count += 1 |
| response = self.client.chat.completions.create( |
| model=self.model, |
| messages=self.messages, |
| temperature=0.1, |
| response_format={"type": "json_object"}, |
| extra_body={"reasoning": {"effort": LLM_REASONING_EFFORT, "exclude": LLM_REASONING_EXCLUDE}}, |
| ) |
| self._last_main_parse_trace = {} |
| return parse_json_message(response.choices[0].message.content, trace=self._last_main_parse_trace) |
|
|
| def _answer_question( |
| self, role_name: str, question: Dict[str, Any], |
| ) -> tuple[Dict[str, Any], bool]: |
| role_name = resolve_role_name(role_name, self.role_agents) |
| role = self.role_agents.get(role_name) |
| if role is None: |
| return {"answer": f"Error: unknown role {role_name}。"}, False |
| try: |
| normalized = validate_question( |
| question, role_name=role_name, |
| semantic_enabled=role_name in self._semantic_enabled_roles, |
| ) |
| except ValueError as error: |
| return {"answer": f"Invalid query: {error}"}, False |
| if not normalized["authorized"]: |
| return {"answer": "The key is invalid, belongs to another role, or keyless follow-up is not yet authorized. Include a complete key for this role in question text, without a separate key field."}, False |
| key = question.get("key") |
| role_question = dict(question) |
| binding = None |
| if (self.followup_resolver and key is None |
| and (SETTING != "mixed" or role_name == INCOMPLETE_INJECTION["role_name"])): |
| binding = self.followup_resolver.resolve(role_name, question['question'], self.current_turn) |
| if binding['status'] != 'resolved': |
| answer = (self.followup_resolver.catalog(role_name) if binding['status'] == 'catalog' |
| else self.followup_resolver.CLARIFY) |
| return {"answer": answer, "followup_binding": binding}, False |
| key = binding['key'] |
| |
| role_question = dict(question, key=key, question=question['question'] + ' (System-linked record: ' + key + ')') |
| allowed_keys = set(RECORD_REPLY_KEYS[key] if key is not None else ROLE_VALID_KEYS[role_name]) |
| unrelated_field = False |
| if binding and key == INCOMPLETE_INJECTION['key']: |
| hidden_topic = INCOMPLETE_INJECTION['hidden_predicate_id'].removeprefix(INCOMPLETE_INJECTION['candidate_id'] + '_') |
| unrelated_field = not binding['full'] and hidden_topic not in binding['fields'] and self.incomplete_policy.recovered_at_turn is None |
| disclosure_mode = ('incomplete_partial' if unrelated_field else self.incomplete_policy.mode(role_name, role_question, self.current_turn)) if self.incomplete_policy else None |
| if binding: |
| binding['recovery_requested'] = disclosure_mode == 'incomplete_recovery' |
| options = self.incomplete_policy.request_options(disclosure_mode, role.system_message) if disclosure_mode else {} |
| trace = role.answer_with_trace( |
| format_role_user_message(role_question), allowed_keys=allowed_keys, |
| response_mode=disclosure_mode or ("exact" if key is not None else "search"), |
| **options, |
| ) |
| if not trace["accepted"]: |
| return { |
| "answer": trace["answer"], "role_validation_trace": trace, |
| "protocol_error": "role_reply_scope_violation", |
| **({"followup_binding": binding} if binding else {}), |
| }, False |
| if disclosure_mode: |
| self.incomplete_policy.observe(disclosure_mode, self.current_turn, trace["answer"]) |
| if self.followup_resolver: |
| self.followup_resolver.observe(role_name, key, self.current_turn) |
| return { |
| "answer": trace["answer"], "role_trace": trace, |
| "authorized_exact": question.get("key") is not None, |
| **({"followup_binding": binding} if binding else {}), |
| }, False |
|
|
| def _communication_evaluation( |
| self, transcript: List[Dict[str, Any]], final_turn: int | None, |
| ) -> Dict[str, Any]: |
| metrics = communication_metrics(transcript) |
| total_questions = sum( |
| len(message.get("questions", [])) |
| for event in transcript |
| for message in event.get("main_agent_action", {}).get("messages", []) |
| ) |
| ideal = max(1, self.runtime_config["finalize_ideal_round"]) |
| turns = final_turn if final_turn is not None else len(transcript) |
| return { |
| **metrics, |
| "total_role_agent_queries": total_questions, |
| "total_questions": total_questions, |
| "finalize_ideal_round": self.runtime_config["finalize_ideal_round"], |
| "max_turns": self.max_turns, |
| "wait_turns": sum(bool(event.get("waited")) for event in transcript), |
| "empty_observation_count": self.empty_observation_count, |
| **self.neglect_policy.metrics(), |
| "delivered_reply_count": len(self.delivered_messages), |
| "pending_reply_count": self.neglect_policy.metrics()["neglect_pending_count"], |
| "efficiency": min(1.0, ideal / turns) if final_turn else 0.0, |
| } |
|
|
| def _result( |
| self, final: Dict[str, Any], transcript: List[Dict[str, Any]], final_turn: int | None, |
| ) -> Dict[str, Any]: |
| conclusion = compute_conclusion_correctness(final) |
| communication = self._communication_evaluation(transcript, final_turn) |
| communication["recovery_success"] = ( |
| recovery_success(communication, conclusion["conclusion_correctness"] == 1) |
| if ERROR_INJECTION.get("enabled") else None |
| ) |
| if self.incomplete_policy: |
| communication.update(self.followup_resolver.metrics()) |
| communication.update(self.incomplete_policy.metrics( |
| correct=conclusion["conclusion_correctness"] == 1, finalized=final_turn is not None, |
| )) |
| communication.update(incomplete_numeric_metrics( |
| INCOMPLETE_INJECTION, transcript, communication, |
| correct=conclusion["conclusion_correctness"] == 1, finalized=final_turn is not None, |
| )) |
| return { |
| "final_result": final, |
| "evaluation": { |
| "conclusion_correct": conclusion["conclusion_correct"], |
| "numeric_result_correct": conclusion["numeric_result_correct"], |
| "joint_correctness": conclusion["conclusion_correctness"], |
| "conclusion_evaluation": conclusion, |
| "communication_evaluation": communication, |
| }, |
| "transcript": transcript, |
| "runtime_config": dict(self.runtime_config), |
| "collaboration_state": { |
| "neglect_roles": self.neglect_policy.roles, |
| "delivered_messages": list(self.delivered_messages), |
| "communication_events": list(self.communication_events), |
| }, |
| } |
|
|
| run = run_direct |
|
|
|
|
| def build_task() -> str: |
| return TASK_CONTEXT.strip() + "\n\n" + TASK_QUERY.strip() |
|
|
|
|
| def create_timestamped_run_dir( |
| root: str | Path, prefix: str, started: datetime | None = None, |
| ) -> Path: |
| root_path = Path(root) |
| root_path.mkdir(parents=True, exist_ok=True) |
| timestamp = (started or datetime.now()).strftime("%m%d_%H%M") |
| base_name = f"{prefix}_{timestamp}" |
| suffix = 1 |
| while True: |
| name = base_name if suffix == 1 else f"{base_name}_{suffix:02d}" |
| candidate = root_path / name |
| try: |
| candidate.mkdir(exist_ok=False) |
| return candidate |
| except FileExistsError: |
| suffix += 1 |
|
|
|
|
| def save_single_run_result( |
| result: Dict[str, Any], output_root: str | Path | None = None, |
| ) -> Path: |
| logs_root = ( |
| Path(output_root) |
| if output_root is not None |
| else Path(__file__).resolve().parent.parent / "logs" |
| ) |
| run_dir = create_timestamped_run_dir(logs_root, "single_run") |
| result_path = run_dir / "result.json" |
| result_path.write_text( |
| json.dumps(result, ensure_ascii=False, indent=2) + "\n", |
| encoding="utf-8", |
| ) |
| return result_path |
|
|
|
|
| def main() -> None: |
| main_client = OpenAI(base_url=MAIN_AGENT_BASE_URL, api_key=MAIN_AGENT_API_KEY) |
| role_client = OpenAI(base_url=ROLE_AGENT_BASE_URL, api_key=ROLE_AGENT_API_KEY) |
| role_agents = build_role_agents(role_client) |
| agent = MainAgent(main_client, role_agents) |
| task = build_task() |
| print_key_trace("LLM CONFIG", format_llm_config()) |
| print_key_trace("AVAILABLE ROLES", format_available_roles(role_agents)) |
| print_key_trace("TASK QUERY", task) |
| result = agent.run(task) |
| result_path = save_single_run_result(result) |
| summary_title = "MISALIGNED RECOVERY SUMMARY" if SETTING in {"misaligned", "mixed"} else "TASK SUMMARY" |
| print_key_trace(summary_title, format_key_metrics(result["evaluation"])) |
| print_key_trace("RESULT SAVED", result_path) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
