feat: add agent guided birth time rectification

scripts/active_rectification_questions.py

@@ -8,23 +8,7 @@ import json
 from datetime import datetime, timedelta
 from typing import Any
 
-OPTIONS = [
-    {"key": "A", "label": "明确有，且时间大致吻合", "score": 2},
-    {"key": "B", "label": "有类似，但时间略偏或不够重大", "score": 1},
-    {"key": "C", "label": "没有明显发生", "score": -2},
-    {"key": "D", "label": "不确定 / 不记得", "score": 0},
-]
-
-QUESTION_TEMPLATES = [
-    ("education_environment_shift", 1, "education", ["D24", "D4", "Dasha"], "age_16_to_18", "16-18岁附近，是否有明显学业、学校、专业方向或学习环境变化？", "middle_candidate_cluster", "against_D24_sensitive_cluster"),
-    ("residence_relocation_shift", 1, "residence", ["D4", "12H", "Rahu/Ketu", "Transit"], "age_20_to_24", "20-24岁附近，是否有搬家、离乡、长期异地、住宿或居住结构变化？", "D4_relocation_cluster", "against_D4_relocation_cluster"),
-    ("relationship_or_partner_entry", 1, "relationship", ["D9", "UL", "A7", "7H"], "age_21_to_26", "21-26岁附近，是否有关系对象进入、关系断裂、暧昧升级或关系观明显转变？", "D9_UL_A7_cluster", "against_relationship_cluster"),
-    ("career_responsibility_pressure", 1, "career", ["D10", "A10", "Saturn", "10H"], "age_26_to_30", "26-30岁附近，是否有责任增加、合作压力、工作结构变化或长期压力阶段？", "D10_A10_saturn_cluster", "against_career_pressure_cluster"),
-    ("research_tool_expression_shift", 1, "career_learning", ["D10", "D24", "Mercury", "A10"], "recent_three_years", "近三年是否明显进入写作、技术、系统化学习、工具搭建、内容表达、AI/研究类方向？", "Mercury_D24_A10_cluster", "against_learning_expression_cluster"),
-    ("health_crisis_or_low_period", 2, "health_pressure", ["D30", "6H", "8H", "Saturn/Mars"], "largest_pressure_window", "某个压力窗口附近，是否有健康、事故、低谷、睡眠/精神压力或身体负担明显阶段？", "D30_crisis_cluster", "against_D30_crisis_cluster"),
-    ("public_role_or_project_visibility", 2, "public_work", ["A10", "D10", "AmK", "Karakamsha"], "career_visibility_window", "某个事业窗口附近，是否有项目公开、作品产出、职位/身份变化或被他人看见的机会？", "A10_public_visibility_cluster", "against_A10_cluster"),
-    ("sequence_inner_vs_outer", 3, "fine_timing", ["KP_cusp", "Pratyantar", "Dasha_boundary"], "top_candidate_window", "关键变化更像先有内在转向、后有外部结果，还是几乎同时发生？", "fine_boundary_cluster", "neutral"),
-]
+from scripts.active_rectification_scoring import build_questions, score_answers
 
 
 def _parse_time(value: str) -> datetime:
@@ -113,7 +97,6 @@ def _candidate_recast(
         return None
     import domain_calculation_service
     import jaimini
-    import kp_system
     import varga
 
     chart = domain_calculation_service.compute_chart({
@@ -134,7 +117,7 @@ def _candidate_recast(
         if name in {"Sun", "Moon", "Mars", "Mercury", "Jupiter", "Venus", "Saturn", "Rahu", "Ketu"}
     }
     asc_lon = chart["ascendant"]["lon"]
-    vargas = varga.calc_all_vargas(planet_lons, asc_lon, divisions=[9, 10, 24, 30, 60])
+    vargas = varga.calc_all_vargas(planet_lons, asc_lon, divisions=[4, 9, 10, 24, 30, 60])
     arudha = jaimini.calc_arudha_padas(int(asc_lon // 30), planet_lons)
     padas = arudha.get("padas", {})
     upapada = arudha.get("upapada", {})
@@ -145,8 +128,16 @@ def _candidate_recast(
             "degree_in_sign": chart["ascendant"].get("degree_in_sign"),
         },
         "varga_lagna": {
-            key: value.get("Ascendant", {})
-            for key, value in vargas.items()
+            **{
+                key: value.get("Ascendant", {})
+                for key, value in vargas.items()
+            },
+            **{
+                f"D{division}": value.get("Ascendant", {})
+                for division in (4, 9, 10, 24, 30)
+                for key, value in vargas.items()
+                if key.startswith(f"D{division}_")
+            },
         },
         "arudha": {
             "A7": padas.get("A7", {}),
@@ -189,23 +180,7 @@ def build_questionnaire(
     tz: float | None = None,
     ayanamsa: str = "lahiri",
 ) -> dict[str, Any]:
-    questions = []
-    for qid, round_id, domain, sensitivity, window, prompt, yes_bias, no_bias in QUESTION_TEMPLATES:
-        questions.append({
-            "id": qid,
-            "round": round_id,
-            "domain": domain,
-            "sensitivity": sensitivity,
-            "window": window,
-            "prompt": prompt,
-            "options": OPTIONS,
-            "scoring_map": {
-                "A": {"effect": "support", "cluster": yes_bias, "points": 2},
-                "B": {"effect": "weak_support", "cluster": yes_bias, "points": 1},
-                "C": {"effect": "exclude_or_penalize", "cluster": no_bias, "points": -2},
-                "D": {"effect": "neutral", "cluster": "neutral", "points": 0},
-            },
-        })
+    questions = build_questions()
     return {
         "scope": "active_birth_time_rectification_questionnaire",
         "schema_version": 1,
@@ -237,51 +212,6 @@ def build_questionnaire(
     }
 
 
-def score_answers(questionnaire: dict[str, Any], answers: dict[str, str]) -> dict[str, Any]:
-    questions = questionnaire.get("questions") if isinstance(questionnaire.get("questions"), list) else []
-    by_id = {question["id"]: question for question in questions if isinstance(question, dict) and question.get("id")}
-    cluster_scores: dict[str, int] = {}
-    applied = []
-    unknown_ids = []
-    invalid_answers = []
-
-    for question_id, raw_choice in (answers or {}).items():
-        question = by_id.get(question_id)
-        if not question:
-            unknown_ids.append(question_id)
-            continue
-        choice = str(raw_choice or "").strip().upper()
-        scoring = (question.get("scoring_map") or {}).get(choice)
-        if not isinstance(scoring, dict):
-            invalid_answers.append({"id": question_id, "answer": raw_choice})
-            continue
-        cluster = str(scoring.get("cluster") or "neutral")
-        points = int(scoring.get("points") or 0)
-        if cluster != "neutral":
-            cluster_scores[cluster] = cluster_scores.get(cluster, 0) + points
-        applied.append({"id": question_id, "answer": choice, "cluster": cluster, "points": points})
-
-    answered_ids = {item["id"] for item in applied}
-    unanswered = [question for question in questions if question.get("id") not in answered_ids]
-    next_round = min((int(question.get("round") or 0) for question in unanswered), default=None)
-    rankings = [
-        {"cluster": cluster, "score": score}
-        for cluster, score in sorted(cluster_scores.items(), key=lambda item: (-item[1], item[0]))
-    ]
-    return {
-        "scope": "active_birth_time_rectification_scoring",
-        "schema_version": 1,
-        "answered_count": len(applied),
-        "candidate_cluster_rankings": rankings,
-        "next_round": next_round,
-        "next_round_questions": [question for question in unanswered if question.get("round") == next_round],
-        "applied_scoring": applied,
-        "unknown_question_ids": unknown_ids,
-        "invalid_answers": invalid_answers,
-        "boundary": "This narrows candidate clusters only; final rectification requires scoring answers against actual candidate chart differences.",
-    }
-
-
 def main() -> int:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--birth-time", required=True, help="Approximate local birth time, YYYY-MM-DD HH:MM")