how/script/data_processing/analyze_creation_pattern_v3.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
创作模式分析 V3（完整流程）

整合三步流程：
1. 数据准备：根据帖子图谱 + 人设图谱，提取待分析数据
2. 起点分析：AI分析创意起点（新版prompt）
3. 模式推导：基于共现关系的迭代推导

输入：帖子图谱 + 人设图谱
输出：完整的创作模式分析结果
"""

import asyncio
import json
from pathlib import Path
from typing import Dict, List, Optional, Set
import sys

# 添加项目根目录到路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

from lib.llm_cached import analyze, LLMConfig, AnalyzeResult
from lib.my_trace import set_trace_smith as set_trace
from script.data_processing.path_config import PathConfig


# ===== 配置 =====
TASK_NAME = "creation_pattern_v3"  # 缓存任务名称
OUTPUT_DIR_NAME = "creation_pattern_v3"  # 输出目录名称

MATCH_SCORE_THRESHOLD = 0.8  # 匹配分数阈值
GLOBAL_RATIO_THRESHOLD = 0.7  # 全局占比阈值（>=0.7 算常量）
ORIGIN_SCORE_THRESHOLD = 0.8  # 起点分数阈值


# ===== 数据加载 =====

def load_json(file_path: Path) -> Dict:
    """加载JSON文件"""
    with open(file_path, "r", encoding="utf-8") as f:
        return json.load(f)


def get_post_graph_files(config: PathConfig) -> List[Path]:
    """获取所有帖子图谱文件"""
    post_graph_dir = config.intermediate_dir / "post_graph"
    return sorted(post_graph_dir.glob("*_帖子图谱.json"))




# ===== 第一步：数据准备 =====

def extract_post_detail(post_graph: Dict) -> Dict:
    """提取帖子详情"""
    meta = post_graph.get("meta", {})
    post_detail = meta.get("postDetail", {})

    return {
        "postId": meta.get("postId", ""),
        "postTitle": meta.get("postTitle", ""),
        "body_text": post_detail.get("body_text", ""),
        "images": post_detail.get("images", []),
        "video": post_detail.get("video"),
        "publish_time": post_detail.get("publish_time", ""),
        "like_count": post_detail.get("like_count", 0),
        "collect_count": post_detail.get("collect_count", 0),
    }


def extract_analysis_nodes(post_graph: Dict, persona_graph: Dict) -> tuple:
    """
    提取待分析节点列表

    待分析节点 = 灵感点 + 目的点 + 关键点
    """
    nodes = post_graph.get("nodes", {})
    edges = post_graph.get("edges", {})
    persona_nodes = persona_graph.get("nodes", {})
    persona_index = persona_graph.get("index", {})

    # 1. 收集关键点信息
    keypoints = {}
    for node_id, node in nodes.items():
        if node.get("type") == "标签" and node.get("dimension") == "关键点":
            keypoints[node_id] = {
                "名称": node.get("name", ""),
                "描述": node.get("detail", {}).get("description", ""),
            }

    # 2. 分析支撑关系
    support_map = {}
    for edge_id, edge in edges.items():
        if edge.get("type") == "支撑":
            source_id = edge.get("source", "")
            target_id = edge.get("target", "")
            if source_id in keypoints:
                if target_id not in support_map:
                    support_map[target_id] = []
                support_map[target_id].append(keypoints[source_id])

    # 3. 分析关联关系
    relation_map = {}
    for edge_id, edge in edges.items():
        if edge.get("type") == "关联":
            source_id = edge.get("source", "")
            target_id = edge.get("target", "")
            source_name = nodes.get(source_id, {}).get("name", "")
            target_name = nodes.get(target_id, {}).get("name", "")

            if source_id not in relation_map:
                relation_map[source_id] = []
            relation_map[source_id].append(target_name)

            if target_id not in relation_map:
                relation_map[target_id] = []
            relation_map[target_id].append(source_name)

    # 4. 分析人设匹配
    match_map = {}
    persona_out_edges = persona_index.get("outEdges", {})

    def get_node_info(node_id: str) -> Optional[Dict]:
        """获取人设节点的标准信息"""
        node = persona_nodes.get(node_id, {})
        if not node:
            return None
        detail = node.get("detail", {})
        parent_path = detail.get("parentPath", [])
        return {
            "节点ID": node_id,
            "节点名称": node.get("name", ""),
            "节点分类": "/".join(parent_path) if parent_path else "",
            "节点维度": node.get("dimension", ""),
            "节点类型": node.get("type", ""),
            "人设全局占比": detail.get("probGlobal", 0),
            "父类下占比": detail.get("probToParent", 0),
        }

    def get_parent_category_id(node_id: str) -> Optional[str]:
        """通过属于边获取父分类节点ID"""
        belong_edges = persona_out_edges.get(node_id, {}).get("属于", [])
        for edge in belong_edges:
            target_id = edge.get("target", "")
            target_node = persona_nodes.get(target_id, {})
            if target_node.get("type") == "分类":
                return target_id
        return None

    for edge_id, edge in edges.items():
        if edge.get("type") == "匹配":
            source_id = edge.get("source", "")
            target_id = edge.get("target", "")

            if source_id.startswith("帖子:") and target_id.startswith("人设:"):
                match_score = edge.get("score", 0)
                persona_node = persona_nodes.get(target_id, {})

                if persona_node:
                    node_type = persona_node.get("type", "")
                    match_node_info = get_node_info(target_id)
                    if not match_node_info:
                        continue

                    if node_type == "标签":
                        category_id = get_parent_category_id(target_id)
                    else:
                        category_id = target_id

                    category_info = None
                    if category_id:
                        category_node = persona_nodes.get(category_id, {})
                        if category_node:
                            category_detail = category_node.get("detail", {})
                            category_path = category_detail.get("parentPath", [])
                            category_info = {
                                "节点ID": category_id,
                                "节点名称": category_node.get("name", ""),
                                "节点分类": "/".join(category_path) if category_path else "",
                                "节点维度": category_node.get("dimension", ""),
                                "节点类型": "分类",
                                "人设全局占比": category_detail.get("probGlobal", 0),
                                "父类下占比": category_detail.get("probToParent", 0),
                                "历史共现分类": [],
                            }

                            co_occur_edges = persona_out_edges.get(category_id, {}).get("分类共现", [])
                            co_occur_edges_sorted = sorted(co_occur_edges, key=lambda x: x.get("score", 0), reverse=True)
                            for co_edge in co_occur_edges_sorted[:5]:
                                co_target_id = co_edge.get("target", "")
                                co_score = co_edge.get("score", 0)
                                co_node = persona_nodes.get(co_target_id, {})
                                if co_node:
                                    co_detail = co_node.get("detail", {})
                                    co_path = co_detail.get("parentPath", [])
                                    category_info["历史共现分类"].append({
                                        "节点ID": co_target_id,
                                        "节点名称": co_node.get("name", ""),
                                        "节点分类": "/".join(co_path) if co_path else "",
                                        "节点维度": co_node.get("dimension", ""),
                                        "节点类型": "分类",
                                        "人设全局占比": co_detail.get("probGlobal", 0),
                                        "父类下占比": co_detail.get("probToParent", 0),
                                        "共现度": round(co_score, 4),
                                    })

                    if source_id not in match_map:
                        match_map[source_id] = []
                    match_map[source_id].append({
                        "匹配节点": match_node_info,
                        "匹配分数": round(match_score, 4),
                        "所属分类": category_info,
                    })

    # 5. 构建待分析节点列表
    analysis_nodes = []
    for node_id, node in nodes.items():
        if node.get("type") == "标签" and node.get("domain") == "帖子":
            dimension = node.get("dimension", "")
            if dimension in ["灵感点", "目的点", "关键点"]:
                match_info = match_map.get(node_id)

                analysis_nodes.append({
                    "节点ID": node_id,
                    "节点名称": node.get("name", ""),
                    "节点分类": node.get("category", ""),
                    "节点维度": dimension,
                    "节点类型": node.get("type", ""),
                    "节点描述": node.get("detail", {}).get("description", ""),
                    "人设匹配": match_info,
                })

    # 6. 构建关系列表
    relation_list = []

    for edge_id, edge in edges.items():
        if edge.get("type") == "支撑":
            source_id = edge.get("source", "")
            target_id = edge.get("target", "")
            if source_id in keypoints:
                relation_list.append({
                    "来源节点": source_id,
                    "目标节点": target_id,
                    "关系类型": "支撑",
                })

    seen_relations = set()
    for edge_id, edge in edges.items():
        if edge.get("type") == "关联":
            source_id = edge.get("source", "")
            target_id = edge.get("target", "")
            key = tuple(sorted([source_id, target_id]))
            if key not in seen_relations:
                seen_relations.add(key)
                relation_list.append({
                    "来源节点": source_id,
                    "目标节点": target_id,
                    "关系类型": "关联",
                })

    return analysis_nodes, relation_list


def prepare_analysis_data(post_graph: Dict, persona_graph: Dict) -> Dict:
    """
    准备完整的分析数据

    输出扁平化的节点列表 + 独立的人设共现关系数据
    """
    analysis_nodes, relation_list = extract_analysis_nodes(post_graph, persona_graph)

    # 扁平化节点，提取人设共现关系数据
    flat_nodes = []
    persona_co_occur = {}  # {分类ID: {名称, 共现分类列表}}

    for node in analysis_nodes:
        # 基础节点字段
        flat_node = {
            "节点ID": node["节点ID"],
            "节点名称": node["节点名称"],
            "节点分类": node.get("节点分类", ""),
            "节点维度": node["节点维度"],
            "节点描述": node.get("节点描述", ""),
            "是否已知": False,
            "发现编号": None,
        }

        # 提取人设匹配信息（list格式，支持多个匹配）
        match_list = node.get("人设匹配") or []
        if match_list:
            flat_node["人设匹配"] = []
            for match_info in match_list:
                category_info = match_info.get("所属分类")
                category_id = category_info.get("节点ID") if category_info else None

                # 保留完整的匹配信息，但去掉历史共现分类（拆到外面）
                clean_match = {
                    "匹配节点": match_info.get("匹配节点"),
                    "匹配分数": match_info.get("匹配分数", 0),
                }
                if category_info:
                    # 复制所属分类，但不包含历史共现分类
                    clean_category = {k: v for k, v in category_info.items() if k != "历史共现分类"}
                    clean_match["所属分类"] = clean_category

                flat_node["人设匹配"].append(clean_match)

                # 收集人设共现关系（去重）- 从历史共现分类拆出来
                if category_id and category_id not in persona_co_occur:
                    co_occur_list = category_info.get("历史共现分类", [])
                    if co_occur_list:
                        persona_co_occur[category_id] = [
                            {
                                "节点ID": c.get("节点ID"),
                                "节点名称": c.get("节点名称"),
                                "节点分类": c.get("节点分类", ""),
                                "节点维度": c.get("节点维度", ""),
                                "节点类型": c.get("节点类型", ""),
                                "人设全局占比": c.get("人设全局占比", 0),
                                "父类下占比": c.get("父类下占比", 0),
                                "共现度": c.get("共现度", 0),
                            }
                            for c in co_occur_list
                            if c.get("节点ID")
                        ]
        else:
            flat_node["人设匹配"] = []

        flat_nodes.append(flat_node)

    return {
        "帖子详情": extract_post_detail(post_graph),
        "节点列表": flat_nodes,
        "关系列表": relation_list,
        "人设共现关系": persona_co_occur,
    }


# ===== 第二步：起点分析（新版prompt） =====

def get_best_match(node: Dict) -> Optional[Dict]:
    """获取节点的最佳人设匹配（分数最高的）"""
    match_list = node.get("人设匹配") or []
    if not match_list:
        return None
    return max(match_list, key=lambda m: m.get("匹配分数", 0))


def get_match_score(node: Dict) -> float:
    """获取节点的最高人设匹配分数"""
    best_match = get_best_match(node)
    if best_match:
        return best_match.get("匹配分数", 0)
    return 0


def get_category_id(node: Dict) -> Optional[str]:
    """获取节点的所属分类ID（最佳匹配的）"""
    best_match = get_best_match(node)
    if best_match:
        category = best_match.get("所属分类")
        if category:
            return category.get("节点ID")
    return None


def get_all_category_ids(node: Dict) -> List[str]:
    """获取节点所有匹配的分类ID"""
    match_list = node.get("人设匹配") or []
    result = []
    for m in match_list:
        category = m.get("所属分类")
        if category and category.get("节点ID"):
            result.append(category.get("节点ID"))
    return result


def get_category_global_ratio(node: Dict) -> float:
    """获取节点所属分类的人设全局占比（最佳匹配的）"""
    best_match = get_best_match(node)
    if best_match:
        category = best_match.get("所属分类")
        if category:
            return category.get("人设全局占比", 0)
    return 0


def is_persona_constant(node: Dict) -> bool:
    """判断节点是否为人设常量（匹配分数 >= 0.8 且 分类全局占比 >= 0.8）"""
    match_score = get_match_score(node)
    global_ratio = get_category_global_ratio(node)
    return match_score >= MATCH_SCORE_THRESHOLD and global_ratio >= GLOBAL_RATIO_THRESHOLD


def build_origin_context(nodes: List[Dict]) -> Dict:
    """构造AI分析的上下文（新版格式）"""

    # 所有创意标签
    all_tags = []
    for node in nodes:
        all_tags.append({
            "名称": node["节点名称"],
            "人设匹配度": round(get_match_score(node), 2),
            "所属分类全局占比": round(get_category_global_ratio(node), 2),
        })

    # 起点候选集（灵感点 + 目的点）
    candidates = [
        node["节点名称"]
        for node in nodes
        if node["节点维度"] in ["灵感点", "目的点"]
    ]

    return {
        "all_tags": all_tags,
        "candidates": candidates,
    }


def format_origin_prompt(context: Dict) -> str:
    """格式化起点分析的prompt（新版）"""
    all_tags = context["all_tags"]
    candidates = context["candidates"]

    # 创意标签列表
    tags_text = ""
    for tag in all_tags:
        tags_text += f"- {tag['名称']}\n"
        tags_text += f"  人设匹配度: {tag['人设匹配度']} | 所属分类全局占比: {tag['所属分类全局占比']}\n\n"

    # 起点候选集（一行）
    candidates_text = "、".join(candidates)

    prompt = f"""# Role

你是小红书爆款内容的"逆向工程"专家。你的核心能力是透过内容的表象，还原创作者最初的脑回路。

# Task

我提供一组笔记的【创意标签】和一个【起点候选集】。

请推理出哪些选项是真正的**创意起点**。

# Input Data

## 创意标签

{tags_text}
## 起点候选集

{candidates_text}

# 推理约束

- 无法被其他项或人设推理出的点，即为起点（推理关系局限在起点候选集中）
- 包含/被包含关系代表一种顺序：由大节点推导出被包含节点
- 目的推理手段
- 实质推理形式
- 和人设匹配度越低的帖子是起点概率越大，证明这个起点具备外部性

# Output Format

请输出一个标准的 JSON 格式。

- Key: 候选集中的词。
- Value: 一个对象，包含：
  - `score`: 0.0 到 1.0 的浮点数（代表是起点的可能性）。
  - `analysis`: 一句话推理"""

    return prompt


async def analyze_origin(nodes: List[Dict], force_llm: bool = False) -> Dict:
    """
    执行起点分析

    输入: 节点列表
    输出: 节点列表（加了起点分析、是否已知、发现编号字段）+ 中间结果
    """
    context = build_origin_context(nodes)
    prompt = format_origin_prompt(context)

    print(f"\n  起点候选: {len(context['candidates'])} 个")

    result = await analyze(
        prompt=prompt,
        task_name=f"{TASK_NAME}/origin",
        force=force_llm,
        parse_json=True,
    )

    # 把分析结果合并到节点
    llm_result = result.data or {}
    output_nodes = []

    # 同一个步骤出来的节点使用相同的发现编号
    step_order = 1  # 起点分析步骤的编号

    for node in nodes:
        new_node = dict(node)  # 复制原节点
        name = node["节点名称"]

        if name in llm_result:
            score = llm_result[name].get("score", 0)
            analysis = llm_result[name].get("analysis", "")
            # 加起点分析
            new_node["起点分析"] = {
                "分数": score,
                "说明": analysis,
            }
            # 高分起点标记为已知（同一步骤的节点使用相同编号）
            if score >= ORIGIN_SCORE_THRESHOLD:
                new_node["是否已知"] = True
                new_node["发现编号"] = step_order
        else:
            new_node["起点分析"] = None

        output_nodes.append(new_node)

    return {
        "输入上下文": {
            "创意标签": context["all_tags"],
            "起点候选": context["candidates"],
        },
        "中间结果": llm_result,
        "输出节点": output_nodes,
        "cache_hit": result.cache_hit,
        "model": result.model_name,
        "log_url": result.log_url,
    }


# ===== 第三步：模式推导 =====

def derive_patterns(
    nodes: List[Dict],
    persona_co_occur: Dict[str, Dict],
) -> Dict:
    """
    基于共现关系的迭代推导

    输入: 带起点分析的节点列表 + 人设共现关系数据
    输出: 节点列表（加了推导轮次、未知原因字段）+ 推导边列表
    """
    node_by_name: Dict[str, Dict] = {n["节点名称"]: n for n in nodes}

    # 构建共现查找表 {节点ID: {共现节点ID: 共现度}}
    co_occur_lookup = {}
    for cat_id, co_occur_list in persona_co_occur.items():
        co_occur_lookup[cat_id] = {
            c["节点ID"]: c["共现度"]
            for c in co_occur_list
        }

    # 1. 初始化已知点集合（已经是已知的节点）
    known_names: Set[str] = set()
    node_round: Dict[str, int] = {}  # {节点名称: 加入轮次}

    for node in nodes:
        if node.get("是否已知"):
            known_names.add(node["节点名称"])
            node_round[node["节点名称"]] = 0

    unknown_names: Set[str] = set(node_by_name.keys()) - known_names
    edges: List[Dict] = []

    # 2. 迭代推导
    round_num = 0
    new_known_this_round = known_names.copy()

    while new_known_this_round:
        round_num += 1
        new_known_next_round: Set[str] = set()

        for known_name in new_known_this_round:
            known_node = node_by_name.get(known_name)
            if not known_node:
                continue

            if get_match_score(known_node) < MATCH_SCORE_THRESHOLD:
                continue

            # 获取该节点所属分类的共现列表
            known_cat_id = get_category_id(known_node)
            if not known_cat_id or known_cat_id not in co_occur_lookup:
                continue

            co_occur_map = co_occur_lookup[known_cat_id]

            for unknown_name in list(unknown_names):
                unknown_node = node_by_name.get(unknown_name)
                if not unknown_node:
                    continue

                if get_match_score(unknown_node) < MATCH_SCORE_THRESHOLD:
                    continue

                # 检查未知节点的分类是否在已知节点的共现列表中
                unknown_cat_id = get_category_id(unknown_node)
                if unknown_cat_id and unknown_cat_id in co_occur_map:
                    co_occur_score = co_occur_map[unknown_cat_id]
                    new_known_next_round.add(unknown_name)
                    node_round[unknown_name] = round_num

                    edges.append({
                        "来源": known_node["节点ID"],
                        "目标": unknown_node["节点ID"],
                        "关系类型": "共现推导",
                        "推导轮次": round_num,
                        "共现分类ID": unknown_cat_id,
                        "共现度": co_occur_score,
                    })

        known_names.update(new_known_next_round)
        unknown_names -= new_known_next_round
        new_known_this_round = new_known_next_round

        if not new_known_next_round:
            break

    # 3. 构建输出节点（只更新是否已知、发现编号）
    # 先找出当前最大发现编号
    max_order = 0
    for node in nodes:
        if node.get("发现编号") and node["发现编号"] > max_order:
            max_order = node["发现编号"]

    # 按推导轮次排序新发现的节点，分配发现编号
    new_known_by_round = {}
    for name, r in node_round.items():
        if r > 0:  # 排除起点（轮次0）
            if r not in new_known_by_round:
                new_known_by_round[r] = []
            new_known_by_round[r].append(name)

    # 分配发现编号（同一轮次的节点使用相同编号）
    order_map = {}
    for r in sorted(new_known_by_round.keys()):
        step_order = max_order + r  # 同一轮次使用相同编号
        for name in new_known_by_round[r]:
            order_map[name] = step_order

    output_nodes = []
    for node in nodes:
        new_node = dict(node)
        name = node["节点名称"]

        # 如果是新推导出来的（非起点），更新已知状态和发现编号
        if name in node_round and node_round[name] > 0:
            new_node["是否已知"] = True
            new_node["发现编号"] = order_map.get(name)

        output_nodes.append(new_node)

    return {
        "输出节点": output_nodes,
        "推导边列表": edges,
        "推导轮次": round_num,
    }


# ===== 第四步：下一步分析 =====

def build_next_step_context(known_nodes: List[Dict], unknown_nodes: List[Dict], all_nodes: List[Dict]) -> Dict:
    """构造下一步分析的上下文"""

    # 已知点信息（按发现顺序排序）
    known_sorted = sorted(known_nodes, key=lambda n: n.get("发现编号") or 999)
    known_info = []
    for n in known_sorted:
        info = {
            "名称": n["节点名称"],
            "维度": n["节点维度"],
            "分类": n.get("节点分类", ""),
            "人设匹配度": round(get_match_score(n), 2),
            "人设全局占比": round(get_category_global_ratio(n), 2),
            "发现编号": n.get("发现编号"),
        }
        # 如果有起点分析，加上
        if n.get("起点分析"):
            info["起点说明"] = n["起点分析"].get("说明", "")
        known_info.append(info)

    # 未知点信息（不含描述）
    unknown_info = []
    for n in unknown_nodes:
        unknown_info.append({
            "名称": n["节点名称"],
            "维度": n["节点维度"],
            "分类": n.get("节点分类", ""),
            "人设匹配度": round(get_match_score(n), 2),
            "人设全局占比": round(get_category_global_ratio(n), 2),
        })

    # 人设常量（从全部节点中筛选）
    constants = [
        n["节点名称"]
        for n in all_nodes
        if is_persona_constant(n)
    ]

    return {
        "known_nodes": known_info,
        "unknown_nodes": unknown_info,
        "constants": constants,
    }


def format_next_step_prompt(context: Dict) -> str:
    """格式化下一步分析的prompt"""

    known_text = ""
    for i, n in enumerate(context["known_nodes"], 1):
        known_text += f"{i}. {n['名称']} ({n['维度']})\n"
        known_text += f"   分类: {n['分类']} | 人设匹配度: {n['人设匹配度']} | 人设全局占比: {n['人设全局占比']}\n"
        if n.get("起点说明"):
            known_text += f"   起点说明: {n['起点说明']}\n"
        known_text += "\n"

    unknown_text = ""
    for n in context["unknown_nodes"]:
        unknown_text += f"- {n['名称']} ({n['维度']})\n"
        unknown_text += f"  分类: {n['分类']} | 人设匹配度: {n['人设匹配度']} | 人设全局占比: {n['人设全局占比']}\n\n"

    constants = context.get("constants", [])
    constants_text = "、".join(constants) if constants else "无"

    prompt = f"""# Role
你是小红书爆款内容的"逆向工程"专家。你的任务是还原创作者的思维路径。

# Task
基于已知的创意点，推理哪些未知点最可能是创作者**下一步直接想到**的点。
可以有多个点同时被想到（如果它们在逻辑上是并列的）。

## 已知点（按发现顺序）
{known_text}
## 未知点（待推理）
{unknown_text}
## 人设常量
{constants_text}

# 推理约束
1. 创作者的思维是有逻辑的：先有动机/目的，再想形式/手法
2. 关键点通常是为了支撑灵感点或目的点
3. 人设常量是创作者固有的风格，不需要推理
4. 只输出"下一步直接能想到"的点，不是所有未知点

# Output Format
输出 JSON，对每个未知点评分：
- Key: 未知点名称
- Value: 对象，包含：
  - `score`: 0.0-1.0（下一步被想到的可能性）
  - `from`: 从哪个已知点推导出来（已知点名称）
  - `reason`: 如何从该已知点推导出来（一句话）"""

    return prompt


async def analyze_next_step(
    nodes: List[Dict],
    force_llm: bool = False
) -> Dict:
    """
    执行下一步分析

    输入: 节点列表（有已知和未知）
    输出: 最可能的下一步点列表
    """
    # 分离已知和未知
    known_nodes = [n for n in nodes if n.get("是否已知")]
    unknown_nodes = [n for n in nodes if not n.get("是否已知")]

    if not unknown_nodes:
        return {
            "输入上下文": {"已知点": [], "未知点": [], "人设常量": []},
            "中间结果": [],
            "下一步点": [],
        }

    context = build_next_step_context(known_nodes, unknown_nodes, nodes)
    prompt = format_next_step_prompt(context)

    print(f"\n  已知点: {len(known_nodes)} 个")
    print(f"  未知点: {len(unknown_nodes)} 个")

    result = await analyze(
        prompt=prompt,
        task_name=f"{TASK_NAME}/next_step",
        force=force_llm,
        parse_json=True,
    )

    # 解析结果（现在是 {name: {score, from, reason}} 格式）
    llm_result = result.data or {}

    # 构建候选列表，按分数排序
    candidates = []
    for name, info in llm_result.items():
        candidates.append({
            "节点名称": name,
            "可能性分数": info.get("score", 0),
            "推导来源": info.get("from", ""),
            "推理说明": info.get("reason", ""),
        })
    candidates.sort(key=lambda x: x["可能性分数"], reverse=True)

    return {
        "输入上下文": {
            "已知点": context["known_nodes"],
            "未知点": context["unknown_nodes"],
            "人设常量": context["constants"],
        },
        "中间结果": llm_result,
        "下一步候选": candidates,
        "cache_hit": result.cache_hit,
        "model": result.model_name,
        "log_url": result.log_url,
    }


# ===== 完整流程 =====

def save_result(post_id: str, post_detail: Dict, steps: List, config: PathConfig) -> Path:
    """保存结果到文件"""
    output_dir = config.intermediate_dir / OUTPUT_DIR_NAME
    output_dir.mkdir(parents=True, exist_ok=True)
    output_file = output_dir / f"{post_id}_创作模式.json"

    result = {
        "帖子详情": post_detail,
        "步骤列表": steps,
    }
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

    print(f"  [已保存] {output_file.name}")
    return output_file


async def process_single_post(
    post_file: Path,
    persona_graph: Dict,
    config: PathConfig,
    force_llm: bool = False,
    max_step: int = 3,
) -> Dict:
    """
    处理单个帖子

    Args:
        force_llm: 强制重新调用LLM（跳过LLM缓存）
        max_step: 最多运行到第几步 (1=数据准备, 2=起点分析, 3=模式推导)
    """
    post_graph = load_json(post_file)
    post_id = post_graph.get("meta", {}).get("postId", "unknown")

    print(f"\n{'=' * 60}")
    print(f"处理帖子: {post_id}")
    print("-" * 60)

    steps = []

    # ===== 步骤1：数据准备 =====
    print("\n[步骤1] 数据准备...")
    data = prepare_analysis_data(post_graph, persona_graph)
    post_detail = data["帖子详情"]
    nodes_step1 = data["节点列表"]
    relations_step1 = data["关系列表"]
    persona_co_occur = data["人设共现关系"]

    # 步骤1所有节点都是新的
    new_known_step1 = [n["节点名称"] for n in nodes_step1 if n.get("是否已知")]

    step1 = {
        "步骤": "数据准备",
        "输入": {
            "帖子图谱": str(post_file.name),
            "人设图谱": "人设图谱.json",
        },
        "输出": {
            "新的已知节点": new_known_step1,
            "新的边": [],
            "节点列表": nodes_step1,
            "边列表": relations_step1,
        },
        "人设共现关系": persona_co_occur,
        "摘要": {
            "节点数": len(nodes_step1),
            "边数": len(relations_step1),
            "人设共现数": len(persona_co_occur),
        },
    }
    steps.append(step1)
    print(f"  节点数: {len(nodes_step1)}")
    print(f"  关系数: {len(relations_step1)}")
    print(f"  人设共现数: {len(persona_co_occur)}")

    # 步骤1完成，保存
    save_result(post_id, post_detail, steps, config)

    if max_step == 1:
        return {"帖子详情": post_detail, "步骤列表": steps}

    # ===== 步骤2：起点分析 =====
    print("\n[步骤2] 起点分析...")
    origin_result = await analyze_origin(nodes_step1, force_llm=force_llm)
    nodes_step2 = origin_result["输出节点"]

    # 统计高分起点
    def get_origin_score(node):
        analysis = node.get("起点分析")
        if analysis:
            return analysis.get("分数", 0)
        return 0

    high_score_origins = [
        (n["节点名称"], get_origin_score(n))
        for n in nodes_step2
        if get_origin_score(n) >= 0.7
    ]

    # 新发现的已知节点（起点）
    new_known_nodes = [n["节点名称"] for n in nodes_step2 if n.get("是否已知")]

    step2 = {
        "步骤": "起点分析",
        "输入": {
            "节点列表": nodes_step1,
            "创意标签": origin_result["输入上下文"]["创意标签"],
            "起点候选": origin_result["输入上下文"]["起点候选"],
        },
        "中间结果": origin_result["中间结果"],
        "输出": {
            "新的已知节点": new_known_nodes,
            "新的边": [],
            "节点列表": nodes_step2,
            "边列表": relations_step1,  # 边没变化
        },
        "摘要": {
            "新已知数": len(new_known_nodes),
            "model": origin_result["model"],
            "cache_hit": origin_result["cache_hit"],
            "log_url": origin_result.get("log_url"),
        },
    }
    steps.append(step2)

    print(f"  高分起点 (>=0.7): {len(high_score_origins)} 个")
    for name, score in sorted(high_score_origins, key=lambda x: -x[1]):
        print(f"    ★ {name}: {score:.2f}")

    # 步骤2完成，保存
    save_result(post_id, post_detail, steps, config)

    if max_step == 2:
        return {"帖子详情": post_detail, "步骤列表": steps}

    # ===== 步骤3：模式推导 =====
    print("\n[步骤3] 模式推导...")
    derivation_result = derive_patterns(nodes_step2, persona_co_occur)
    nodes_step3 = derivation_result["输出节点"]
    edges = derivation_result["推导边列表"]

    # 统计
    known_count = sum(1 for n in nodes_step3 if n.get("是否已知"))
    unknown_count = len(nodes_step3) - known_count

    # 新发现的已知节点（本步骤推导出来的，不包括之前的起点）
    prev_known = {n["节点名称"] for n in nodes_step2 if n.get("是否已知")}
    new_known_nodes = [n["节点名称"] for n in nodes_step3 if n.get("是否已知") and n["节点名称"] not in prev_known]

    # 合并边列表（原有边 + 推导边）
    all_edges = relations_step1 + edges

    step3 = {
        "步骤": "模式推导",
        "输入": {
            "节点列表": nodes_step2,
            "人设共现关系": persona_co_occur,
        },
        "输出": {
            "新的已知节点": new_known_nodes,
            "新的边": edges,
            "节点列表": nodes_step3,
            "边列表": all_edges,
        },
        "摘要": {
            "已知点数": known_count,
            "新已知数": len(new_known_nodes),
            "新边数": len(edges),
            "未知点数": unknown_count,
        },
    }
    steps.append(step3)

    print(f"  已知点: {known_count} 个")
    print(f"  推导边: {len(edges)} 条")
    print(f"  未知点: {unknown_count} 个")

    # 步骤3完成，保存
    save_result(post_id, post_detail, steps, config)

    if max_step == 3:
        return {"帖子详情": post_detail, "步骤列表": steps}

    # ===== 步骤4：下一步分析 =====
    print("\n[步骤4] 下一步分析...")
    next_step_result = await analyze_next_step(nodes_step3, force_llm=force_llm)

    # 获取候选列表
    candidates = next_step_result["下一步候选"]

    # 筛选高分候选 (>= 0.8)
    NEXT_STEP_THRESHOLD = 0.8
    high_score_candidates = [c for c in candidates if c["可能性分数"] >= NEXT_STEP_THRESHOLD]

    # 构建节点名称到节点的映射
    node_by_name = {n["节点名称"]: n for n in nodes_step3}

    # 找出当前最大发现编号
    max_order = max((n.get("发现编号") or 0) for n in nodes_step3)

    # 更新节点：把高分候选标记为已知（同一步骤的节点使用相同编号）
    nodes_step4 = []
    new_known_names = []
    step_order = max_order + 1  # 同一步骤的节点使用相同编号

    for node in nodes_step3:
        new_node = dict(node)
        name = node["节点名称"]

        # 检查是否在高分候选中
        matching = [c for c in high_score_candidates if c["节点名称"] == name]
        if matching and not node.get("是否已知"):
            new_node["是否已知"] = True
            new_node["发现编号"] = step_order  # 同一步骤使用相同编号
            new_known_names.append(name)

        nodes_step4.append(new_node)

    # 创建新的边（推导边）
    new_edges = []
    for c in high_score_candidates:
        target_node = node_by_name.get(c["节点名称"])
        source_name = c["推导来源"]
        source_node = node_by_name.get(source_name)
        if target_node and source_node:
            new_edges.append({
                "来源": source_node["节点ID"],
                "目标": target_node["节点ID"],
                "关系类型": "AI推导",
                "可能性分数": c["可能性分数"],
                "推理说明": c["推理说明"],
            })

    # 合并边列表
    all_edges_step4 = all_edges + new_edges

    step4 = {
        "步骤": "下一步分析",
        "输入": {
            "已知点": next_step_result["输入上下文"]["已知点"],
            "未知点": next_step_result["输入上下文"]["未知点"],
            "人设常量": next_step_result["输入上下文"]["人设常量"],
        },
        "中间结果": next_step_result["中间结果"],
        "输出": {
            "新的已知节点": new_known_names,
            "新的边": new_edges,
            "节点列表": nodes_step4,
            "边列表": all_edges_step4,
        },
        "摘要": {
            "已知点数": sum(1 for n in nodes_step4 if n.get("是否已知")),
            "新已知数": len(new_known_names),
            "新边数": len(new_edges),
            "未知点数": sum(1 for n in nodes_step4 if not n.get("是否已知")),
            "model": next_step_result.get("model"),
            "cache_hit": next_step_result.get("cache_hit"),
            "log_url": next_step_result.get("log_url"),
        },
    }
    steps.append(step4)

    # 打印高分候选
    print(f"  候选数: {len(candidates)} 个")
    print(f"  高分候选 (>={NEXT_STEP_THRESHOLD}): {len(high_score_candidates)} 个")
    for c in high_score_candidates:
        print(f"    ★ {c['节点名称']} ({c['可能性分数']:.2f}) ← {c['推导来源']}")
        print(f"      {c['推理说明']}")

    # 步骤4完成，保存
    save_result(post_id, post_detail, steps, config)

    if max_step == 4:
        return {"帖子详情": post_detail, "步骤列表": steps}

    # ===== 循环：步骤3→步骤4 直到全部已知 =====
    iteration = 1
    current_nodes = nodes_step4
    current_edges = all_edges_step4
    MAX_ITERATIONS = 10  # 防止无限循环

    while True:
        # 检查是否还有未知节点
        unknown_count = sum(1 for n in current_nodes if not n.get("是否已知"))
        if unknown_count == 0:
            print(f"\n[完成] 所有节点已变为已知")
            break

        if iteration > MAX_ITERATIONS:
            print(f"\n[警告] 达到最大迭代次数 {MAX_ITERATIONS}，停止循环")
            break

        # ===== 迭代步骤3：共现推导 =====
        print(f"\n[迭代{iteration}-步骤3] 模式推导...")
        derivation_result = derive_patterns(current_nodes, persona_co_occur)
        nodes_iter3 = derivation_result["输出节点"]
        edges_iter3 = derivation_result["推导边列表"]

        # 统计新推导的
        prev_known_names = {n["节点名称"] for n in current_nodes if n.get("是否已知")}
        new_known_step3 = [n["节点名称"] for n in nodes_iter3 if n.get("是否已知") and n["节点名称"] not in prev_known_names]
        new_edges_step3 = edges_iter3  # derive_patterns 返回的是本轮新增的边

        all_edges_iter3 = current_edges + new_edges_step3

        step_iter3 = {
            "步骤": f"迭代{iteration}-模式推导",
            "输入": {
                "节点列表": current_nodes,
                "人设共现关系": persona_co_occur,
            },
            "输出": {
                "新的已知节点": new_known_step3,
                "新的边": new_edges_step3,
                "节点列表": nodes_iter3,
                "边列表": all_edges_iter3,
            },
            "摘要": {
                "已知点数": sum(1 for n in nodes_iter3 if n.get("是否已知")),
                "新已知数": len(new_known_step3),
                "新边数": len(new_edges_step3),
                "未知点数": sum(1 for n in nodes_iter3 if not n.get("是否已知")),
            },
        }
        steps.append(step_iter3)

        print(f"  新已知: {len(new_known_step3)} 个")
        print(f"  新边: {len(new_edges_step3)} 条")

        save_result(post_id, post_detail, steps, config)

        # 检查是否还有未知
        unknown_after_step3 = sum(1 for n in nodes_iter3 if not n.get("是否已知"))
        if unknown_after_step3 == 0:
            print(f"\n[完成] 所有节点已变为已知")
            break

        # ===== 迭代步骤4：AI推导 =====
        print(f"\n[迭代{iteration}-步骤4] 下一步分析...")
        next_step_result = await analyze_next_step(nodes_iter3, force_llm=force_llm)
        candidates_iter4 = next_step_result["下一步候选"]
        high_score_iter4 = [c for c in candidates_iter4 if c["可能性分数"] >= NEXT_STEP_THRESHOLD]

        # 更新节点（同一步骤的节点使用相同编号）
        node_by_name_iter4 = {n["节点名称"]: n for n in nodes_iter3}
        max_order_iter4 = max((n.get("发现编号") or 0) for n in nodes_iter3)
        nodes_iter4 = []
        new_known_iter4 = []
        step_order_iter4 = max_order_iter4 + 1  # 同一步骤的节点使用相同编号

        for node in nodes_iter3:
            new_node = dict(node)
            name = node["节点名称"]
            matching = [c for c in high_score_iter4 if c["节点名称"] == name]
            if matching and not node.get("是否已知"):
                new_node["是否已知"] = True
                new_node["发现编号"] = step_order_iter4  # 同一步骤使用相同编号
                new_known_iter4.append(name)
            nodes_iter4.append(new_node)

        # 创建新边
        new_edges_iter4 = []
        for c in high_score_iter4:
            target_node = node_by_name_iter4.get(c["节点名称"])
            source_node = node_by_name_iter4.get(c["推导来源"])
            if target_node and source_node:
                new_edges_iter4.append({
                    "来源": source_node["节点ID"],
                    "目标": target_node["节点ID"],
                    "关系类型": "AI推导",
                    "可能性分数": c["可能性分数"],
                    "推理说明": c["推理说明"],
                })

        all_edges_iter4 = all_edges_iter3 + new_edges_iter4

        step_iter4 = {
            "步骤": f"迭代{iteration}-下一步分析",
            "输入": {
                "已知点": next_step_result["输入上下文"]["已知点"],
                "未知点": next_step_result["输入上下文"]["未知点"],
                "人设常量": next_step_result["输入上下文"]["人设常量"],
            },
            "中间结果": next_step_result["中间结果"],
            "输出": {
                "新的已知节点": new_known_iter4,
                "新的边": new_edges_iter4,
                "节点列表": nodes_iter4,
                "边列表": all_edges_iter4,
            },
            "摘要": {
                "已知点数": sum(1 for n in nodes_iter4 if n.get("是否已知")),
                "新已知数": len(new_known_iter4),
                "新边数": len(new_edges_iter4),
                "未知点数": sum(1 for n in nodes_iter4 if not n.get("是否已知")),
                "model": next_step_result.get("model"),
                "cache_hit": next_step_result.get("cache_hit"),
            },
        }
        steps.append(step_iter4)

        print(f"  新已知: {len(new_known_iter4)} 个")
        print(f"  新边: {len(new_edges_iter4)} 条")

        save_result(post_id, post_detail, steps, config)

        # 如果这轮没有新进展，停止
        if len(new_known_step3) == 0 and len(new_known_iter4) == 0:
            print(f"\n[停止] 本轮无新进展，停止循环")
            break

        # 更新状态，进入下一轮
        current_nodes = nodes_iter4
        current_edges = all_edges_iter4
        iteration += 1

    return {"帖子详情": post_detail, "步骤列表": steps}


# ===== 主函数 =====

async def main(
    post_id: str = None,
    all_posts: bool = False,
    force_llm: bool = False,
    max_step: int = 3,
):
    """主函数"""
    _, log_url = set_trace()

    config = PathConfig()

    print(f"账号: {config.account_name}")
    print(f"Trace URL: {log_url}")
    print(f"输出目录: {OUTPUT_DIR_NAME}")

    # 加载人设图谱
    persona_graph_file = config.intermediate_dir / "人设图谱.json"
    if not persona_graph_file.exists():
        print(f"错误: 人设图谱文件不存在: {persona_graph_file}")
        return

    persona_graph = load_json(persona_graph_file)
    print(f"人设图谱节点数: {len(persona_graph.get('nodes', {}))}")

    # 获取帖子图谱文件
    post_graph_files = get_post_graph_files(config)
    if not post_graph_files:
        print("错误: 没有找到帖子图谱文件")
        return

    # 确定要处理的帖子
    if post_id:
        target_file = next(
            (f for f in post_graph_files if post_id in f.name),
            None
        )
        if not target_file:
            print(f"错误: 未找到帖子 {post_id}")
            return
        files_to_process = [target_file]
    elif all_posts:
        files_to_process = post_graph_files
    else:
        files_to_process = [post_graph_files[0]]

    print(f"待处理帖子数: {len(files_to_process)}")

    # 处理
    results = []
    for i, post_file in enumerate(files_to_process, 1):
        print(f"\n{'#' * 60}")
        print(f"# 处理帖子 {i}/{len(files_to_process)}")
        print(f"{'#' * 60}")

        result = await process_single_post(
            post_file=post_file,
            persona_graph=persona_graph,
            config=config,
            force_llm=force_llm,
            max_step=max_step,
        )
        results.append(result)

    # 汇总
    print(f"\n{'#' * 60}")
    print(f"# 完成! 共处理 {len(results)} 个帖子")
    print(f"{'#' * 60}")
    print(f"Trace: {log_url}")

    print("\n汇总:")
    for result in results:
        post_id = result["帖子详情"]["postId"]
        steps = result.get("步骤列表", [])
        num_steps = len(steps)

        if num_steps == 1:
            step1_summary = steps[0].get("摘要", {})
            print(f"  {post_id}: 节点数={step1_summary.get('节点数', 0)} (仅数据准备)")
        elif num_steps == 2:
            step2_summary = steps[1].get("摘要", {})
            print(f"  {post_id}: 起点={step2_summary.get('新已知数', 0)} (未推导)")
        elif num_steps == 3:
            step3_summary = steps[2].get("摘要", {})
            print(f"  {post_id}: 已知={step3_summary.get('已知点数', 0)}, "
                  f"未知={step3_summary.get('未知点数', 0)}")
        elif num_steps >= 4:
            step4_summary = steps[3].get("摘要", {})
            print(f"  {post_id}: 已知={step4_summary.get('已知点数', 0)}, "
                  f"新已知={step4_summary.get('新已知数', 0)}, "
                  f"新边={step4_summary.get('新边数', 0)}, "
                  f"未知={step4_summary.get('未知点数', 0)}")
        else:
            print(f"  {post_id}: 无步骤数据")


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="创作模式分析 V3")
    parser.add_argument("--post-id", type=str, help="帖子ID")
    parser.add_argument("--all-posts", action="store_true", help="处理所有帖子")
    parser.add_argument("--force-llm", action="store_true", help="强制重新调用LLM（跳过LLM缓存）")
    parser.add_argument("--step", type=int, default=5, choices=[1, 2, 3, 4, 5],
                        help="运行到第几步 (1=数据准备, 2=起点分析, 3=模式推导, 4=下一步分析, 5=完整循环)")
    args = parser.parse_args()

    asyncio.run(main(
        post_id=args.post_id,
        all_posts=args.all_posts,
        force_llm=args.force_llm,
        max_step=args.step,
    ))