Agent/examples_how/overall_derivation/tools/point_match.py

"""
选题点匹配 Tool - 判断推导选题点是否与帖子中的选题点匹配

功能：读取帖子选题点列表，与推导选题点做相似度计算，返回 combined_score >= 阈值的匹配对。
"""

import json
import sys
from pathlib import Path
from typing import Any, List, Optional

# 保证直接运行或作为包加载时都能解析 utils（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.similarity_calc import similarity_matrix

try:
    from agent.tools import tool, ToolResult, ToolContext
except ImportError:
    def tool(*args, **kwargs):
        return lambda f: f
    ToolResult = None
    ToolContext = None

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

# 默认匹配阈值
DEFAULT_MATCH_THRESHOLD = 0.6


def _post_topic_file(account_name: str, post_id: str) -> Path:
    """帖子选题点文件：../input/{account_name}/处理后数据/post_topic/{post_id}.json"""
    return _BASE_INPUT / account_name / "处理后数据" / "post_topic" / f"{post_id}.json"


def _match_data_file(account_name: str, post_id: str) -> Path:
    """帖子选题点与人设树节点匹配结果文件：../input/{account_name}/处理后数据/match_data/{post_id}_匹配_all.json"""
    return _BASE_INPUT / account_name / "处理后数据" / "match_data" / f"{post_id}_匹配_all.json"


def _load_match_data(
    account_name: str, post_id: str
) -> dict[tuple[str, str], float]:
    """
    从匹配文件中读取 (帖子选题点, 人设树节点) -> 匹配分数。
    文件为 JSON 数组，每项 name 为帖子选题点，match_personas 中 name 为人设树节点，match_score 为分数。
    """
    path = _match_data_file(account_name, post_id)
    if not path.is_file():
        return {}
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    if not isinstance(data, list):
        return {}
    lookup: dict[tuple[str, str], float] = {}
    for item in data:
        if not isinstance(item, dict):
            continue
        post_point = item.get("name")
        personas = item.get("match_personas")
        if post_point is None or not isinstance(personas, list):
            continue
        post_point = str(post_point).strip()
        if not post_point:
            continue
        for mp in personas:
            if not isinstance(mp, dict):
                continue
            persona_name = mp.get("name")
            score = mp.get("match_score")
            if persona_name is None or score is None:
                continue
            persona_name = str(persona_name).strip()
            try:
                lookup[(post_point, persona_name)] = float(score)
            except (TypeError, ValueError):
                continue
    return lookup


def _load_post_topic_points(account_name: str, post_id: str) -> List[str]:
    """从 post_topic JSON 读取帖子选题点列表。文件内容为字符串数组。"""
    path = _post_topic_file(account_name, post_id)
    if not path.is_file():
        return []
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    if not isinstance(data, list):
        return []
    return [str(x).strip() for x in data if x and str(x).strip()]


def _to_derivation_points(derivation_output_points: List[str]) -> List[str]:
    """
    从推导选题点字符串列表中筛出非空并 strip，返回去重后的列表。
    兼容 "叙事编排+商业融入+物品" 格式：先按 "+" 拆分，再展开为独立选题点，最终去重（保持首次出现顺序）。
    """
    seen: set[str] = set()
    result: List[str] = []
    for s in derivation_output_points:
        if s is None:
            continue
        for part in str(s).split("+"):
            part = part.strip()
            if part and part not in seen:
                seen.add(part)
                result.append(part)
    return result


async def match_derivation_to_post_points(
    derivation_output_points: List[str],
    account_name: str,
    post_id: str,
    match_threshold: float = DEFAULT_MATCH_THRESHOLD,
) -> List[dict[str, Any]]:
    """
    判断推导选题点（视为人设树节点）是否与帖子选题点匹配，返回分数 >= match_threshold 的列表。
    优先从 ../input/{account_name}/match_data/{post_id}_匹配_all.json 读取匹配分数，
    文件中未出现的 (帖子选题点, 人设树节点) 再通过 similarity_matrix 计算。

    Returns:
        每项: {"推导选题点": str, "帖子选题点": str, "匹配分数": float}
    """
    post_points = _load_post_topic_points(account_name, post_id)
    derivation_points = _to_derivation_points(derivation_output_points)
    if not derivation_points:
        return []
    if not post_points:
        return []

    # 从匹配文件读取 (帖子选题点, 人设树节点) -> 匹配分数；derivation_points 当作人设树节点
    match_lookup = _load_match_data(account_name, post_id)
    scores: dict[tuple[str, str], float] = {}
    missing_pairs: list[tuple[str, str]] = []
    for d in derivation_points:
        for p in post_points:
            key = (p, d)
            if key in match_lookup:
                scores[(d, p)] = match_lookup[key]
            else:
                missing_pairs.append((d, p))

    # 文件中没有的 (推导选题点, 帖子选题点) 用 similarity_matrix 计算
    if missing_pairs:
        derivation_missing = list({d for d, _ in missing_pairs})
        post_missing = list({p for _, p in missing_pairs})
        items = await similarity_matrix(derivation_missing, post_missing)
        for row in items:
            d, p = row["phrase_a"], row["phrase_b"]
            scores[(d, p)] = row["combined_score"]

    matched = []
    for (d, p), score in scores.items():
        if score >= match_threshold:
            matched.append({
                "推导选题点": d,
                "帖子选题点": p,
                "匹配分数": round(score, 6),
            })
    return matched


@tool()
async def point_match(
    derivation_output_points: List[str],
    account_name: str,
    post_id: str,
    match_threshold: float = DEFAULT_MATCH_THRESHOLD,
) -> ToolResult:
    """
    判断推导选题点与帖子选题点是否匹配，返回达到阈值的匹配对。

    Args:
    derivation_output_points : 推导选题点字符串列表。
    account_name : 账号名，用于定位 input 下的账号目录。
    post_id : 帖子ID，用于定位该帖的选题点与匹配数据。
    match_threshold : 匹配分数阈值，分数 >= 该值视为匹配成功，默认 0.6。

    Returns:
    ToolResult：
        - title: 结果标题。
        - output: 可读的匹配结果文本（每行：推导选题点、帖子选题点、匹配分数）。
        - 出错时 error 为错误信息。
    """
    topic_path = _post_topic_file(account_name, post_id)
    if not topic_path.is_file():
        return ToolResult(
            title="帖子选题点文件不存在",
            output=f"帖子选题点文件不存在: {topic_path}",
            error="Post topic file not found",
        )
    try:
        derivation_points = _to_derivation_points(derivation_output_points)
        if not derivation_points:
            return ToolResult(
                title="参数无效",
                output="derivation_output_points 不能为空，且需为字符串列表",
                error="Invalid derivation_output_points",
            )
        matched = await match_derivation_to_post_points(
            derivation_output_points, account_name, post_id, match_threshold
        )
        if not matched:
            output = f"未找到分数 >= {match_threshold} 的匹配对"
        else:
            lines = [
                f"- 推导: {x['推导选题点']}\t帖子: {x['帖子选题点']}\t分数={x['匹配分数']}"
                for x in matched
            ]
            output = "\n".join(lines)
        return ToolResult(
            title=f"选题点匹配结果 ({account_name}, post_id={post_id})",
            output=output,
            metadata={
                "account_name": account_name,
                "post_id": post_id,
                "match_threshold": match_threshold,
                "count": len(matched),
            },
        )
    except Exception as e:
        return ToolResult(
            title="选题点匹配失败",
            output=str(e),
            error=str(e),
        )


def main() -> None:
    """本地测试：用家有大志账号、某帖子ID、推导选题点列表测试匹配。"""
    import asyncio

    account_name = "家有大志"
    post_id = "68fb6a5c000000000302e5de"
    derivation_output_points = ["分享+创意改造", "柴犬", "不存在的点"]

    async def run():
        matched = await match_derivation_to_post_points(
            derivation_output_points, account_name, post_id
        )
        print(f"账号: {account_name}, post_id: {post_id}")
        print(f"推导选题点: {derivation_output_points}")
        print(f"匹配成功 {len(matched)} 条:\n")
        for x in matched:
            print(f"  - 推导: {x['推导选题点']}\t帖子: {x['帖子选题点']}\t分数={x['匹配分数']}")
        if ToolResult is not None:
            result = await point_match(
                derivation_output_points=derivation_output_points,
                account_name=account_name,
                post_id=post_id,
            )
            print("\n--- Tool 返回 ---")
            print(result.output)

    asyncio.run(run())


if __name__ == "__main__":
    main()