Agent/examples_how/overall_derivation/tools/find_pattern.py

"""
查找 Pattern Tool - 从 pattern 库中获取符合条件概率阈值的 pattern

功能：
- 账号：读取 input/{账号}/处理后数据/pattern/pattern.json，条件概率基于账号人设树；
  元素与帖子选题点匹配走账号 match_data / point_match，并支持人设树子节点、兄弟节点扩展。
- 平台库：读取 input/xiaohongshu/pattern/processed_edge_data.json，条件概率基于 xiaohongshu/tree；
  元素匹配仅使用 input/xiaohongshu/match_data/{post_id}_匹配_all.json。
"""

import json
import sys
from pathlib import Path
from typing import Any

# 保证直接运行或作为包加载时都能解析 utils / tools（IDE 可跳转）
_root = Path(__file__).resolve().parent.parent
if str(_root) not in sys.path:
    sys.path.insert(0, str(_root))
from utils.conditional_ratio_calc import (
    build_node_index_for_tree_dir,
    calc_pattern_conditional_ratio,
    calc_pattern_conditional_ratio_with_index,
)
from tools.point_match import (
    DEFAULT_MATCH_THRESHOLD,
    _load_match_data,
    match_derivation_to_post_points,
)
from tools.find_tree_node import _load_trees

try:
    from agent.tools import tool, ToolResult, ToolContext
except ImportError:
    def tool(*args, **kwargs):
        return lambda f: f
    ToolResult = None  # 仅用 main() 测核心逻辑时可无 agent
    ToolContext = None

# 与 pattern_data_process 一致的 key 定义
TOP_KEYS = [
    "depth_max_with_name",
    "depth_mixed",
    "depth_max_concrete",
    "depth2_medium",
    "depth1_abstract",
    "depth_max_minus_1",
    "depth_max_minus_2",
    "depth_3",
    "depth_4",
]
SUB_KEYS = ["two_x", "one_x", "zero_x"]

_BASE_INPUT = Path(__file__).resolve().parent.parent / "input"

# 排序时「已推导选题点 ↔ pattern 元素」在 match_data 中的高分优先阈值（与账号段原逻辑一致）
_MATCH_PRIOR_MIN_SCORE = 0.8

_PLATFORM_TREE_DIR = _BASE_INPUT / "xiaohongshu" / "tree"
_PLATFORM_PATTERN_FILE = _BASE_INPUT / "xiaohongshu" / "pattern" / "processed_edge_data.json"


def _build_node_info(account_name: str) -> dict[str, dict]:
    """
    构建人设树节点信息映射: node_name -> {
        "type": 节点 _type（"class" / "ID" 等）,
        "children": 子节点名称列表（仅分类节点有值）,
        "siblings": 兄弟节点名称列表（不含自身）,
    }
    """
    node_info: dict[str, dict] = {}

    def _walk(node_dict: dict):
        children_dict = node_dict.get("children") or {}
        child_entries = [(n, c) for n, c in children_dict.items() if isinstance(c, dict)]
        child_names = [n for n, _ in child_entries]
        for name, child in child_entries:
            sub_children = child.get("children") or {}
            sub_child_names = [n for n, c in sub_children.items() if isinstance(c, dict)]
            node_info[name] = {
                "type": child.get("_type", ""),
                "children": sub_child_names,
                "siblings": [n for n in child_names if n != name],
            }
            _walk(child)

    for _dim_name, root in _load_trees(account_name):
        _walk(root)
    return node_info


def _pattern_file(account_name: str) -> Path:
    """pattern 库文件：../input/{account_name}/处理后数据/pattern/pattern.json"""
    return _BASE_INPUT / account_name / "处理后数据" / "pattern" / "pattern.json"


def _platform_pattern_file() -> Path:
    """平台库 pattern：../input/xiaohongshu/pattern/processed_edge_data.json"""
    return _PLATFORM_PATTERN_FILE


def _slim_pattern(p: dict) -> tuple[float, int, list[str], int]:
    """提取 name 列表（去重保序）、support、length、post_count。"""
    names = [item["name"] for item in (p.get("items") or [])]
    seen = set()
    unique = []
    for n in names:
        if n not in seen:
            seen.add(n)
            unique.append(n)
    support = round(float(p.get("support", 0)), 4)
    length = int(p.get("length", 0))
    post_count = int(p.get("post_count", 0))
    return support, length, unique, post_count


def _merge_and_dedupe(patterns: list[dict]) -> list[dict]:
    """
    按 items 的 name 集合去重（不区分顺序），留 support 最大；
    输出格式保留 s、l、i（nameA+nameB+nameC）及 post_count，供条件概率计算使用。
    """
    key_to_best: dict[tuple, tuple[float, int, int]] = {}
    for p in patterns:
        support, length, unique, post_count = _slim_pattern(p)
        if not unique:
            continue
        key = tuple(sorted(unique))
        if key not in key_to_best or support > key_to_best[key][0]:
            key_to_best[key] = (support, length, post_count)
    out = []
    for k, (s, l, post_count) in key_to_best.items():
        out.append({
            "s": s,
            "l": l,
            "i": "+".join(k),
            "post_count": post_count,
        })
    out.sort(key=lambda x: x["s"] * x["l"], reverse=True)
    return out


def _load_and_merge_patterns(account_name: str) -> list[dict]:
    """读取 pattern 库 JSON，按 TOP_KEYS/SUB_KEYS 合并为列表并做合并、去重。"""
    path = _pattern_file(account_name)
    if not path.is_file():
        return []
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    all_patterns = []
    for top in TOP_KEYS:
        if top not in data:
            continue
        block = data[top]
        for sub in SUB_KEYS:
            all_patterns.extend(block.get(sub) or [])
    return _merge_and_dedupe(all_patterns)


def _load_and_merge_platform_patterns() -> list[dict]:
    """读取平台库 pattern JSON，结构与账号库相同，合并去重。"""
    path = _platform_pattern_file()
    if not path.is_file():
        return []
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    all_patterns = []
    for top in TOP_KEYS:
        if top not in data:
            continue
        block = data[top]
        for sub in SUB_KEYS:
            all_patterns.extend(block.get(sub) or [])
    return _merge_and_dedupe(all_patterns)


def _load_platform_match_pair_lookup(post_id: str) -> dict[tuple[str, str], float]:
    """
    xiaohongshu/match_data/{post_id}_匹配_all.json
    -> (帖子选题点, 人设树节点名) -> 最高 match_score（跨 dimension 合并）。
    """
    lookup: dict[tuple[str, str], float] = {}
    if not post_id:
        return lookup
    path = _BASE_INPUT / "xiaohongshu" / "match_data" / f"{post_id}_匹配_all.json"
    if not path.is_file():
        return lookup
    try:
        with open(path, "r", encoding="utf-8") as f:
            data = json.load(f)
    except Exception:
        return lookup
    if not isinstance(data, list):
        return lookup
    for item in data:
        if not isinstance(item, dict):
            continue
        topic = item.get("name")
        personas = item.get("match_personas")
        if topic is None or not isinstance(personas, list):
            continue
        topic_s = str(topic).strip()
        if not topic_s:
            continue
        for mp in personas:
            if not isinstance(mp, dict):
                continue
            elem = mp.get("name")
            score = mp.get("match_score")
            if elem is None or score is None:
                continue
            elem_s = str(elem).strip()
            if not elem_s:
                continue
            try:
                sc = float(score)
            except (TypeError, ValueError):
                continue
            key = (topic_s, elem_s)
            if key not in lookup or sc > lookup[key]:
                lookup[key] = sc
    return lookup


def _platform_element_post_match_map(
    post_id: str,
    match_score_threshold: float,
) -> dict[str, dict[str, float]]:
    """
    平台库：节点名称（不区分 dimension）-> {帖子选题点: 最高分}，
    仅保留 match_score >= match_score_threshold 的对。
    """
    out: dict[str, dict[str, float]] = {}
    if not post_id:
        return out
    path = _BASE_INPUT / "xiaohongshu" / "match_data" / f"{post_id}_匹配_all.json"
    if not path.is_file():
        return out
    try:
        with open(path, "r", encoding="utf-8") as f:
            data = json.load(f)
    except Exception:
        return out
    if not isinstance(data, list):
        return out
    thr = float(match_score_threshold)
    for item in data:
        if not isinstance(item, dict):
            continue
        topic = item.get("name")
        personas = item.get("match_personas")
        if topic is None or not isinstance(personas, list):
            continue
        topic_s = str(topic).strip()
        if not topic_s:
            continue
        for mp in personas:
            if not isinstance(mp, dict):
                continue
            elem = mp.get("name")
            score = mp.get("match_score")
            if elem is None or score is None:
                continue
            try:
                sc = float(score)
            except (TypeError, ValueError):
                continue
            if sc < thr:
                continue
            elem_s = str(elem).strip()
            if not elem_s:
                continue
            bucket = out.setdefault(elem_s, {})
            prev = bucket.get(topic_s)
            if prev is None or sc > prev:
                bucket[topic_s] = sc
    return out


def _parse_derived_list(derived_items: list[dict[str, str]]) -> list[tuple[str, str]]:
    """将 agent 传入的 [{"topic": "x", "source_node": "y"}, ...] 转为 DerivedItem 列表。"""
    out = []
    for item in derived_items:
        if isinstance(item, dict):
            topic = item.get("topic") or item.get("已推导的选题点")
            source = item.get("source_node") or item.get("推导来源人设树节点")
            if topic is not None and source is not None:
                out.append((str(topic).strip(), str(source).strip()))
        elif isinstance(item, (list, tuple)) and len(item) >= 2:
            out.append((str(item[0]).strip(), str(item[1]).strip()))
    return out


def get_patterns_by_conditional_ratio(
    account_name: str,
    derived_list: list[tuple[str, str]],
    conditional_ratio_threshold: float,
    top_n: int,
    post_id: str = "",
) -> list[dict[str, Any]]:
    """
    从 pattern 库中获取条件概率 >= 阈值的 pattern，按以下优先级排序后返回 top_n 条：
      1. pattern 元素中直接包含已推导选题点（topic）的排最前；
      2. pattern 元素与任意已推导选题点的匹配分 >= 0.8 的次之（从 match_data 文件读取，
         key 为 (帖子选题点, 人设树节点)，pattern 元素视为人设树节点）；
      3. 按条件概率降序；
      4. 按 length 降序。
    derived_list 为空时，条件概率使用 pattern 自身的 support（s）。
    返回每项：pattern名称（nameA+nameB+nameC）、条件概率。
    """
    merged = _load_and_merge_patterns(account_name)
    if not merged:
        return []
    base_dir = _BASE_INPUT
    scored: list[tuple[dict, float]] = []

    if not derived_list:
        # derived_items 为空：条件概率取 pattern 本身的 support (s)
        for p in merged:
            ratio = float(p.get("s", 0))
            if ratio >= conditional_ratio_threshold:
                scored.append((p, ratio))
    else:
        for p in merged:
            ratio = calc_pattern_conditional_ratio(
                account_name, derived_list, p, base_dir=base_dir
            )
            if ratio >= conditional_ratio_threshold:
                scored.append((p, ratio))

    derived_topics = {topic for topic, _ in derived_list} if derived_list else set()

    # 次优先：从 match_data 文件加载 (帖子选题点, 人设树节点) -> 匹配分，
    # 用已推导选题点（topic）作为帖子选题点，pattern 元素作为人设树节点，
    # 检查是否存在匹配分 >= 0.8 的组合。
    match_lookup: dict[tuple[str, str], float] = {}
    if derived_topics and post_id:
        match_lookup = _load_match_data(account_name, post_id)

    def _sort_key(x: tuple[dict, float]) -> tuple:
        p, ratio = x
        elements = set(p["i"].split("+"))
        has_derived = bool(elements & derived_topics)
        has_high_match = False
        if not has_derived and match_lookup:
            for elem in elements:
                for dt in derived_topics:
                    if match_lookup.get((dt, elem), 0.0) >= _MATCH_PRIOR_MIN_SCORE:
                        has_high_match = True
                        break
                if has_high_match:
                    break
        return (not has_derived, not has_high_match, -ratio, -p["l"])

    scored.sort(key=_sort_key)
    result = []
    for p, ratio in scored[:top_n]:
        result.append({
            "pattern名称": p["i"],
            "条件概率": round(ratio, 6),
        })
    return result


def get_platform_patterns_by_conditional_ratio(
    derived_list: list[tuple[str, str]],
    conditional_ratio_threshold: float,
    top_n: int,
    post_id: str = "",
) -> list[dict[str, Any]]:
    """
    平台库 pattern：数据来自 xiaohongshu/pattern/processed_edge_data.json，
    条件概率基于 xiaohongshu/tree 的节点索引（与账号侧 calc_pattern 规则一致）。
    排序优先级规则与 get_patterns_by_conditional_ratio 一致，高分参照 xiaohongshu/match_data。
    """
    merged = _load_and_merge_platform_patterns()
    if not merged:
        return []
    platform_index = build_node_index_for_tree_dir(_PLATFORM_TREE_DIR)
    scored: list[tuple[dict, float]] = []

    if not derived_list:
        for p in merged:
            ratio = float(p.get("s", 0))
            if ratio >= conditional_ratio_threshold:
                scored.append((p, ratio))
    else:
        for p in merged:
            ratio = calc_pattern_conditional_ratio_with_index(derived_list, p, platform_index)
            if ratio >= conditional_ratio_threshold:
                scored.append((p, ratio))

    derived_topics = {topic for topic, _ in derived_list} if derived_list else set()
    match_lookup: dict[tuple[str, str], float] = {}
    if derived_topics and post_id:
        match_lookup = _load_platform_match_pair_lookup(post_id)

    def _sort_key(x: tuple[dict, float]) -> tuple:
        p, ratio = x
        elements = set(p["i"].split("+"))
        has_derived = bool(elements & derived_topics)
        has_high_match = False
        if not has_derived and match_lookup:
            for elem in elements:
                for dt in derived_topics:
                    if match_lookup.get((dt, elem), 0.0) >= _MATCH_PRIOR_MIN_SCORE:
                        has_high_match = True
                        break
                if has_high_match:
                    break
        return (not has_derived, not has_high_match, -ratio, -p["l"])

    scored.sort(key=_sort_key)
    result = []
    for p, ratio in scored[:top_n]:
        result.append({
            "pattern名称": p["i"],
            "条件概率": round(ratio, 6),
        })
    return result


def _attach_platform_pattern_post_matches(
    items: list[dict[str, Any]],
    post_id: str,
    match_score_threshold: float,
) -> None:
    """就地写入 帖子选题点匹配：仅使用 xiaohongshu/match_data，元素为节点名（跨 dimension 聚合）。"""
    if not items or not post_id:
        for it in items:
            it["帖子选题点匹配"] = "无"
        return
    elem_map = _platform_element_post_match_map(post_id, float(match_score_threshold))
    for item in items:
        pattern_matches: list[dict[str, Any]] = []
        for elem in item["pattern名称"].split("+"):
            elem = elem.strip()
            if not elem:
                continue
            for post_topic, sc in (elem_map.get(elem) or {}).items():
                pattern_matches.append({
                    "pattern元素": elem,
                    "帖子选题点": post_topic,
                    "匹配分数": round(sc, 6),
                })
        distinct_post_points = len({m["帖子选题点"] for m in pattern_matches})
        item["帖子选题点匹配"] = (
            pattern_matches if distinct_post_points >= 2 else "无"
        )


@tool()
async def find_pattern(
    account_name: str,
    post_id: str,
    derived_items: list[dict[str, str]],
    conditional_ratio_threshold: float,
    top_n: int = 100,
    match_score_threshold: float = DEFAULT_MATCH_THRESHOLD,
) -> ToolResult:
    """
    按条件概率阈值从 pattern 库筛选：第一节为账号 pattern，第二节为平台库 pattern（xiaohongshu/pattern）。
    账号段帖子匹配走账号 match_data + point_match；平台段元素匹配仅走 xiaohongshu/match_data。

    Args:
    account_name : 账号名，用于定位该账号的 pattern 库。
    post_id : 帖子ID。
    derived_items : 已推导选题点列表，可为空。
    conditional_ratio_threshold : 条件概率阈值。
    top_n : 账号段与平台段各自最多返回条数（各自经匹配过滤后可能更少）。
    match_score_threshold : 帖子选题点匹配分阈值。

    Returns:
    ToolResult：output 分「账号 pattern」「平台库 pattern」两段；平台段已排除与账号段 pattern 名称完全相同的项。
    """
    pattern_path = _pattern_file(account_name)
    if not pattern_path.is_file():
        return ToolResult(
            title="Pattern 库不存在",
            output=f"pattern 文件不存在: {pattern_path}",
            error="Pattern file not found",
        )
    try:
        derived_list = _parse_derived_list(derived_items or [])
        thr = float(match_score_threshold)

        # ---------- 账号 pattern（原逻辑：match_data + 子节点/兄弟扩展）----------
        items_account = get_patterns_by_conditional_ratio(
            account_name, derived_list, conditional_ratio_threshold, top_n, post_id
        )
        if items_account and post_id:
            all_elements: list[str] = []
            seen_elements: set[str] = set()
            for item in items_account:
                for elem in item["pattern名称"].split("+"):
                    elem = elem.strip()
                    if elem and elem not in seen_elements:
                        all_elements.append(elem)
                        seen_elements.add(elem)
            matched_results = await match_derivation_to_post_points(
                all_elements, account_name, post_id, match_threshold=thr
            )
            elem_match_map: dict[str, list] = {}
            for m in matched_results:
                elem_match_map.setdefault(m["推导选题点"], []).append({
                    "帖子选题点": m["帖子选题点"],
                    "匹配分数": m["匹配分数"],
                })
            for item in items_account:
                pattern_matches = []
                for elem in item["pattern名称"].split("+"):
                    elem = elem.strip()
                    for post_match in elem_match_map.get(elem, []):
                        pattern_matches.append({
                            "pattern元素": elem,
                            "帖子选题点": post_match["帖子选题点"],
                            "匹配分数": post_match["匹配分数"],
                        })
                distinct_post_points = len({m["帖子选题点"] for m in pattern_matches})
                item["帖子选题点匹配"] = (
                    pattern_matches if distinct_post_points >= 2 else "无"
                )

        items_account = [
            x for x in items_account
            if isinstance(x.get("帖子选题点匹配"), list)
        ]

        if items_account and post_id:
            node_info_map = _build_node_info(account_name)
            all_candidates_set: set[str] = set()
            item_unmatched_info: list[list[tuple[str, list[str], str]]] = []

            for item in items_account:
                pattern_matches = item.get("帖子选题点匹配", [])
                matched_elems = (
                    {m["pattern元素"] for m in pattern_matches}
                    if isinstance(pattern_matches, list) else set()
                )
                all_elems = [e.strip() for e in item["pattern名称"].split("+")]
                unmatched = [e for e in all_elems if e not in matched_elems]

                elem_candidates: list[tuple[str, list[str], str]] = []
                for elem in unmatched:
                    info = node_info_map.get(elem)
                    if not info:
                        continue
                    if info["type"] == "class" and info["children"]:
                        candidates = info["children"]
                        expand_type = "子节点"
                    else:
                        candidates = info["siblings"]
                        expand_type = "兄弟节点"
                    if candidates:
                        elem_candidates.append((elem, candidates, expand_type))
                        all_candidates_set.update(candidates)
                item_unmatched_info.append(elem_candidates)

            if all_candidates_set:
                candidate_matches = await match_derivation_to_post_points(
                    list(all_candidates_set), account_name, post_id, match_threshold=thr
                )
                cand_match_map: dict[str, list[tuple[str, float]]] = {}
                for m in candidate_matches:
                    cand_match_map.setdefault(m["推导选题点"], []).append(
                        (m["帖子选题点"], m["匹配分数"])
                    )
                for item, elem_cands in zip(items_account, item_unmatched_info):
                    for elem, candidates, expand_type in elem_cands:
                        best_cand, best_pp, best_sc = None, None, -1.0
                        for cand in candidates:
                            for pp, sc in cand_match_map.get(cand, []):
                                if sc > best_sc:
                                    best_cand, best_pp, best_sc = cand, pp, sc
                        if best_cand is not None:
                            item["帖子选题点匹配"].append({
                                "pattern元素": elem,
                                "帖子选题点": best_pp,
                                "匹配分数": best_sc,
                                "扩展节点": best_cand,
                                "扩展类型": expand_type,
                            })

        for item in items_account:
            matches = item.get("帖子选题点匹配")
            if not isinstance(matches, list):
                continue
            best_by_pp: dict[str, dict] = {}
            for m in matches:
                pp = m["帖子选题点"]
                if pp not in best_by_pp or m["匹配分数"] > best_by_pp[pp]["匹配分数"]:
                    best_by_pp[pp] = m
            item["帖子选题点匹配"] = list(best_by_pp.values())

        account_pattern_names = {str(x.get("pattern名称", "")).strip() for x in items_account}

        # ---------- 平台库 pattern（xiaohongshu/tree 条件概率 + xiaohongshu/match_data 匹配）----------
        items_platform: list[dict[str, Any]] = []
        if post_id:
            items_platform = get_platform_patterns_by_conditional_ratio(
                derived_list, conditional_ratio_threshold / 5, top_n, post_id
            )
            _attach_platform_pattern_post_matches(items_platform, post_id, thr)
            items_platform = [
                x for x in items_platform
                if isinstance(x.get("帖子选题点匹配"), list)
            ]
            items_platform = [
                x for x in items_platform
                if str(x.get("pattern名称", "")).strip() not in account_pattern_names
            ]
            for item in items_platform:
                matches = item.get("帖子选题点匹配")
                if not isinstance(matches, list):
                    continue
                best_by_pp: dict[str, dict] = {}
                for m in matches:
                    pp = m["帖子选题点"]
                    if pp not in best_by_pp or m["匹配分数"] > best_by_pp[pp]["匹配分数"]:
                        best_by_pp[pp] = m
                item["帖子选题点匹配"] = list(best_by_pp.values())

        def _format_pattern_block(xs: list[dict[str, Any]]) -> list[str]:
            lines: list[str] = []
            for x in xs:
                match_info = x.get("帖子选题点匹配", "无")
                if isinstance(match_info, list):
                    match_str = "、".join(
                        (
                            f"{m['扩展节点']}({m['pattern元素']}的{m['扩展类型']})→{m['帖子选题点']}({m['匹配分数']})"
                            if "扩展节点" in m else
                            f"{m['pattern元素']}→{m['帖子选题点']}({m['匹配分数']})"
                        )
                        for m in match_info
                    )
                else:
                    match_str = str(match_info)
                lines.append(
                    f"- {x['pattern名称']}\t条件概率={x['条件概率']}\t帖子选题点匹配={match_str}"
                )
            return lines

        lines_out: list[str] = []
        lines_out.append(
            "【优先使用】第一节为账号 pattern；第二节为平台库 pattern。"
        )
        lines_out.append("")
        lines_out.append("—— 账号 pattern ——")
        if not items_account:
            lines_out.append(
                f"（无：未找到条件概率 >= {conditional_ratio_threshold} 且满足帖子选题点匹配条件的 pattern）"
            )
        else:
            lines_out.extend(_format_pattern_block(items_account))
        lines_out.append("")
        lines_out.append("—— 平台库 pattern ——")
        if not items_platform:
            lines_out.append(
                "（无：未找到达标 pattern）"
            )
        else:
            lines_out.extend(_format_pattern_block(items_platform))

        output = "\n".join(lines_out)
        return ToolResult(
            title=f"符合条件概率的 Pattern ({account_name}, 阈值={conditional_ratio_threshold})",
            output=output,
            metadata={
                "account_name": account_name,
                "conditional_ratio_threshold": conditional_ratio_threshold,
                "match_score_threshold": thr,
                "top_n": top_n,
                "account_pattern_count": len(items_account),
                "platform_pattern_count": len(items_platform),
                "count": len(items_account) + len(items_platform),
            },
        )
    except Exception as e:
        return ToolResult(
            title="查找 Pattern 失败",
            output=str(e),
            error=str(e),
        )


def main() -> None:
    """本地测试：用家有大志账号、已推导选题点，查询符合条件概率阈值的 pattern（含帖子匹配）。"""
    import asyncio

    account_name = "阿里多多酱"
    post_id = "6915dfc400000000070224d9"
    # 已推导选题点，每项：已推导的选题点 + 推导来源人设树节点
    # derived_items = [
    #     {"topic": "分享", "source_node": "分享"},
    #     {"topic": "植入方式", "source_node": "植入方式"},
    #     {"topic": "叙事结构", "source_node": "叙事结构"},
    # ]
    derived_items = derived_items = [{"topic":"推广","source_node":"推广"},{"topic":"视觉调性","source_node":"视觉调性"}]
    conditional_ratio_threshold = 0.2
    top_n = 2000

    # 1）直接调用核心函数（不含帖子匹配，仅验证排序逻辑）
    # derived_list = _parse_derived_list(derived_items)
    # items = get_patterns_by_conditional_ratio(
    #     account_name, derived_list, conditional_ratio_threshold, top_n, post_id
    # )
    # print(f"账号: {account_name}, 阈值: {conditional_ratio_threshold}, top_n: {top_n}")
    # print(f"共 {len(items)} 条 pattern:\n")
    # for x in items:
    #     print(f"  - {x['pattern名称']}\t条件概率={x['条件概率']}")

    # 2）有 agent 时通过 tool 接口再跑一遍（含帖子选题点匹配）
    if ToolResult is not None:
        async def run_tool():
            result = await find_pattern(
                account_name=account_name,
                post_id=post_id,
                derived_items=derived_items,
                conditional_ratio_threshold=conditional_ratio_threshold,
                top_n=top_n,
            )
            print("\n--- Tool 返回 ---")
            print(result.output)
        asyncio.run(run_tool())


if __name__ == "__main__":
    main()