extreme
752323510b
- Renamed and updated agent classes for film processing, changing from FilmEntityExtractor and FilmShotlistStoryboarder to FilmEntityExtractorAgent and FilmShotlistStoryboarderAgent for consistency. - Modified the dependencies in various API routes to utilize the new agent classes, ensuring proper integration with the updated architecture. - Enhanced the README documentation to reflect changes in unit testing and integration testing for the updated agents. - Streamlined the agent base class to improve the handling of prompts and outputs, facilitating better interaction with the language model. - Removed deprecated skill loading methods and updated the output handling to align with the new agent structure.
660 lines
29 KiB
Python
660 lines
29 KiB
Python
"""脚本处理 Agent：分镜分割、信息提取、实体合并、变体分析、一致性检查、输出编译。"""
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from __future__ import annotations
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import json
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from typing import Any
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from langchain_core.prompts import PromptTemplate
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from app.chains.agents.base import AgentBase, _extract_json_from_text
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from app.schemas.skills.script_processing import (
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OutputCompileResult,
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ScriptConsistencyCheckResult,
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ScriptDivisionResult,
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ScriptOptimizationResult,
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ShotElementExtractionResult,
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StudioScriptExtractionDraft,
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EntityMergeResult,
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VariantAnalysisResult,
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ShotDivision,
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)
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# PromptTemplates（固化在本模块，供各 Agent 子类通过 @property 返回）
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_SCRIPT_DIVIDER_SYSTEM_PROMPT = """\
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你是\"剧本分镜师\"。将完整剧本分割为多个镜头。每个镜头应是完整的连贯场景。
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为每个镜头提供：
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- index（镜头序号，章节内唯一；从 1 开始）
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- start_line、end_line
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- shot_name（镜头名称/镜头标题，分镜名；一句话描述该镜头画面/动作；不要把它当作场景名）
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- script_excerpt（镜头对应的剧本摘录/文本）
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- scene_name（场景名称，必须与 shots 中 scene_name 的含义一致；不要把 shot_name 当成 scene_name）
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- time_of_day
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- character_names_in_text（角色名/称呼，弱信息；稳定ID会在后续合并阶段统一分配）
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严格区分字段含义：
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- shot_name = 分镜名/镜头标题
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- scene_name = 场景名
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只输出 JSON，符合 ScriptDivisionResult 结构。
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"""
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SCRIPT_DIVIDER_PROMPT = PromptTemplate(
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input_variables=["script_text"],
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template="## 输入脚本\n{script_text}\n\n## 输出\n",
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)
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_ELEMENT_EXTRACTOR_SYSTEM_PROMPT = """\
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你是\"镜头元素提取员\"。从单个镜头提取关键信息：
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- character_keys、scene_keys、costume_keys、prop_keys（弱ID/归一化名；稳定ID由后续合并阶段统一分配）
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- characters_detailed（每个角色含 character_key/name_in_text/appearance/clothing/accessories/state + raw_* 溯源，可选 evidence）
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- props_detailed（每个道具含 prop_key/name_in_text/description/state/interaction + raw_text，可选 evidence）
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- scene_detailed（scene_key/name/location_detail/atmosphere/time_weather/raw_description_text，可选 evidence）
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- dialogue_lines（结构化对白，字段与 schemas.DialogueLine 对齐）
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- actions、shot_type_hints、confidence_breakdown
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其中 dialogue_lines 每项必须包含 text/line_mode；建议包含 index/speaker_character_id/target_character_id（若可判定）。
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严格按照原文，不要编造。只输出 JSON，符合 ShotElementExtractionResult 结构。
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"""
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ELEMENT_EXTRACTOR_PROMPT = PromptTemplate(
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input_variables=["index", "shot_text", "context_summary", "shot_division_json"],
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template="镜头号: {index}\n分镜元信息(来自上一步): {shot_division_json}\n上文: {context_summary}\n\n## 镜头文本\n{shot_text}\n\n## 输出\n",
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)
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_ENTITY_MERGER_SYSTEM_PROMPT = """\
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你是\"实体合并师\"。合并多镜头提取结果，统一实体定义，为每个实体分配ID，识别变体和冲突。
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请输出 EntityMergeResult，merged_library 中至少包含 characters/locations/scenes/props 四类。
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每个实体条目（EntityEntry）需包含：
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- 通用：id/name/type/description/aliases/normalized_name/confidence/first_appearance/evidence/first_shot/appearances/variants
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- 角色（type=character）：尽量补充 costume_note、traits
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- 地点（type=location）：尽量补充 location_type
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- 道具（type=prop）：尽量补充 category、owner_character_id
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variants 使用 {variant_key, description, affected_shots, evidence} 的最小结构。
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当提供 previous_merge_json 与 conflict_resolutions_json 时，表示这是一次“重试合并”：你必须参考上一次的合并结果与冲突解决建议，优先消解 conflicts；必要时可调整实体合并/拆分策略，但要保持 ID 尽量稳定（除非建议明确要求变更）。
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只输出 JSON，符合 EntityMergeResult 结构。
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"""
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ENTITY_MERGER_PROMPT = PromptTemplate(
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input_variables=[
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"all_extractions_json",
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"historical_library_json",
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"script_division_json",
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"previous_merge_json",
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"conflict_resolutions_json",
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],
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template=(
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"## 脚本分镜(来自上一步)\n{script_division_json}\n\n"
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"## 所有镜头提取结果\n{all_extractions_json}\n\n"
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"## 历史实体库\n{historical_library_json}\n\n"
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"## 上一次合并结果（可选，用于重试）\n{previous_merge_json}\n\n"
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"## 冲突解决建议（可选，用于重试）\n{conflict_resolutions_json}\n\n"
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"## 输出\n"
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),
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)
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_VARIANT_ANALYZER_SYSTEM_PROMPT = """\
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你是\"变体分析师\"。分析实体变体（特别是角色服装变化），构建时间线，生成变体建议。
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输出 VariantAnalysisResult：costume_timelines.timeline_entries 使用 {shot_index, scene_id, costume_note, changes, evidence}；variant_suggestions 可带 evidence。
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只输出 JSON，符合 VariantAnalysisResult 结构。
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"""
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VARIANT_ANALYZER_PROMPT = PromptTemplate(
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input_variables=["merged_library_json", "all_extractions_json", "script_division_json"],
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template="## 脚本分镜(来自上一步)\n{script_division_json}\n\n## 合并后的实体库\n{merged_library_json}\n\n## 所有镜头提取结果\n{all_extractions_json}\n\n## 输出\n",
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)
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_CONSISTENCY_CHECKER_SYSTEM_PROMPT = """\
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你是\"一致性检查员\"。只做一件事：检测原文中是否把“同一个角色”在不同段落/镜头中赋予了不同的身份或行为主体，导致角色混淆（例如：同名不同人、代词指代混乱、行为归属错位）。
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输出 ScriptConsistencyCheckResult：
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- issues: 每条问题必须包含 character_candidates、description、suggestion；尽量给出 affected_lines（start_line/end_line）。
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- has_issues: issues 非空则为 true
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只输出 JSON。
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"""
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CONSISTENCY_CHECKER_PROMPT = PromptTemplate(
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input_variables=["script_text"],
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template="## 原文剧本\n{script_text}\n\n## 输出\n",
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)
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_SCRIPT_OPTIMIZER_SYSTEM_PROMPT = """\
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你是\"剧本优化师\"。仅当一致性检查发现角色混淆问题时，对原文进行最小改写以消除混淆。
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输入：
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- script_text：原文
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- consistency_json：一致性检查输出（ScriptConsistencyCheckResult）
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输出 ScriptOptimizationResult：
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- optimized_script_text：优化后的完整剧本文本（尽量少改，只改与 issues 相关的段落）
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- change_summary：逐条对应 issues 的改动摘要
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只输出 JSON。
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"""
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SCRIPT_OPTIMIZER_PROMPT = PromptTemplate(
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input_variables=["script_text", "consistency_json"],
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template="## 一致性检查结果\n{consistency_json}\n\n## 原文剧本\n{script_text}\n\n## 输出\n",
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)
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_SCRIPT_EXTRACTOR_SYSTEM_PROMPT = """\
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你是\"Studio 信息提取员\"。你的任务是：基于剧本文本与分镜结果，输出可直接导入 Studio 的草稿结构 StudioScriptExtractionDraft（注意：ID 由导入 API 生成，因此这里全部使用 name 做引用键）。
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输出 StudioScriptExtractionDraft：
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- project_id（必填）
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- chapter_id（必填）
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- script_text（必填）
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- characters: [{name, description, costume_name?, prop_names[], tags[]}]
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- scenes/props/costumes: [{name, description, tags[], prompt_template_id?, view_count}]
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- shots: [{index, title, script_excerpt, scene_name?, character_names[], prop_names[], costume_names[], dialogue_lines[], actions[]}]
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- dialogue_lines: [{index, text, line_mode, speaker_name?, target_name?}]
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强约束：
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- 同名实体在输出中只出现一次（全局去重）；shots 中引用必须使用同一名称
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- shots.index 必须覆盖并对应输入分镜中的 index（不要跳号）
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- 不要输出任何 id 字段（包括 char_001 等），由导入 API 生成
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一致性强约束（必须严格遵守，否则导入会失败）：
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- 先输出全局 characters/scenes/props/costumes 列表，再输出 shots；并把它们视为“字典”。
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- shots[*].character_names / prop_names / costume_names / scene_name 只能从对应全局列表的 name 中选择（完全一致的字符串），禁止生成任何未在全局列表中出现的新名字。
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- 禁止“同义名/括号变体/临时称呼”漂移：例如禁止在 shots 中写「女子（群）」但在 characters 中没有该条目；禁止「仙女A」与「仙女 A」混用。
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- 遇到群体角色/泛指角色（如“女子（群）”“群众”“村民们”）：必须在 characters 列表中创建一条同名角色（name 完全一致），并在 shots 中引用该 name。
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- 对于难以确定是否同一角色的称呼：宁可在 characters 里拆成两条不同 name，也不要在 shots 中凭空换名。
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- 输出 shots 之前，必须做“全集校验”并补齐缺失：所有 shots[*] 中出现的 character_names/prop_names/costume_names/scene_name 的名字集合，必须都能在对应全局列表（characters/props/costumes/scenes）的 name 中找到；如果有缺失，必须在全局列表中补齐对应条目（描述可最小化，但 name 必须完全一致），禁止用别名替换来绕过。
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- 角色名/场景名必须原样保留字符细节：包括全角/半角括号、空格、标点，不要自动做任何规范化或替换（例如不能把「女子（群）」改成「女子(群)」或「女子 （群）」）。
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- 严格区分：shots[*].title 是“镜头标题”（一句话描述该镜头画面/动作），不要拿它当作 scenes 的 scene 名；shots[*].scene_name 才是场景名称，必须来自 scenes 全局列表的 name。
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输入：
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- project_id
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- chapter_id
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- script_text
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- script_division_json（ScriptDivisionResult）
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- consistency_json（可选）
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只输出 JSON。
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"""
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SCRIPT_EXTRACTOR_PROMPT = PromptTemplate(
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input_variables=["project_id", "chapter_id", "script_text", "script_division_json", "consistency_json"],
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template=(
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"## project_id\n{project_id}\n\n"
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"## chapter_id\n{chapter_id}\n\n"
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"## 一致性检查（可选）\n{consistency_json}\n\n"
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"## 分镜结果\n{script_division_json}\n\n"
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"## 剧本文本\n{script_text}\n\n"
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"## 输出\n"
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),
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)
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_OUTPUT_COMPILER_SYSTEM_PROMPT = """\
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你是\"输出编译员\"。汇总所有Agent输出，生成完整项目JSON、可导出表格、项目总结。
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输出 OutputCompileResult，其中 project_json 必须严格符合 ProjectCinematicBreakdown schema（至少包含 source_id/chunks/characters/locations/props/scenes/shots/transitions/notes/uncertainties）。
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只输出 JSON。
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"""
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OUTPUT_COMPILER_PROMPT = PromptTemplate(
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input_variables=["division_json", "all_extractions_json", "merge_json", "variant_json", "consistency_json"],
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template="## 分镜结果\n{division_json}\n\n## 所有逐镜提取\n{all_extractions_json}\n\n## 实体合并\n{merge_json}\n\n## 变体分析\n{variant_json}\n\n## 一致性检查\n{consistency_json}\n\n## 输出\n",
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)
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# ============================================================================
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# 1. ScriptDividerAgent - 剧本自动分镜
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# ============================================================================
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class ScriptDividerAgent(AgentBase[ScriptDivisionResult]):
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"""剧本自动分镜：输入完整剧本文本，输出分镜列表。"""
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@property
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def system_prompt(self) -> str:
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return _SCRIPT_DIVIDER_SYSTEM_PROMPT
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@property
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def prompt_template(self) -> PromptTemplate:
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return SCRIPT_DIVIDER_PROMPT
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@property
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def output_model(self) -> type[ScriptDivisionResult]:
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return ScriptDivisionResult
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def format_output(self, raw: str) -> ScriptDivisionResult:
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"""
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更强的兜底解析：
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LLM 可能输出：
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- 正常结构：{shots:[...], total_shots:N}
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- 包裹结构：{"ScriptDivisionResult": {...}}
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- 直接列表：[{...}, {...}]（视为 shots）
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"""
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json_str = _extract_json_from_text(raw)
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data: Any = json.loads(json_str)
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if isinstance(data, list):
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data = {"shots": data}
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elif isinstance(data, dict) and "ScriptDivisionResult" in data:
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inner = data.get("ScriptDivisionResult")
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if isinstance(inner, list):
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data = {"shots": inner}
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elif isinstance(inner, dict):
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data = inner
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else:
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data = {"shots": []}
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if isinstance(data, dict):
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data = self._normalize(data)
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return self.output_model.model_validate(data) # type: ignore[arg-type]
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def divide_script(self, *, script_text: str) -> ScriptDivisionResult:
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return self.extract(script_text=script_text)
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async def adivide_script(self, *, script_text: str) -> ScriptDivisionResult:
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return await self.aextract(script_text=script_text)
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def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
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"""规范化脚本分割结果。"""
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data = dict(data)
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# 兼容：LLM 可能输出 {"ScriptDivisionResult": {...}} 或 {"ScriptDivisionResult": [...]}
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if "ScriptDivisionResult" in data:
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inner = data.get("ScriptDivisionResult")
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if isinstance(inner, list):
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data = {"shots": inner}
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elif isinstance(inner, dict):
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data = dict(inner)
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else:
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data = {"shots": []}
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if "shots" in data and isinstance(data["shots"], list):
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shots = []
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for idx, shot in enumerate(data["shots"]):
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shot_dict: dict[str, Any] = (
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dict(shot) if isinstance(shot, dict) else {"script_excerpt": str(shot), "shot_name": ""}
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)
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if "index" not in shot_dict:
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shot_dict["index"] = idx + 1
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# 兼容：LLM 可能用 title/shot_title 代替 shot_name
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if "shot_name" not in shot_dict:
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if "title" in shot_dict:
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shot_dict["shot_name"] = str(shot_dict.pop("title"))
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elif "shot_title" in shot_dict:
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shot_dict["shot_name"] = str(shot_dict.pop("shot_title"))
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shot_dict.setdefault("shot_name", "")
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# 兼容旧字段：character_ids -> character_names_in_text（此阶段为弱信息）
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if "character_ids" in shot_dict and "character_names_in_text" not in shot_dict:
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val = shot_dict.get("character_ids")
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if isinstance(val, list):
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shot_dict["character_names_in_text"] = [str(x) for x in val]
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shot_dict.pop("character_ids", None)
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shots.append(shot_dict)
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data["shots"] = shots
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if "total_shots" not in data and "shots" in data:
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data["total_shots"] = len(data["shots"])
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return data
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# ============================================================================
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# 2. ShotElementExtractorAgent - 逐镜信息提取（兼容旧流程）
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# ============================================================================
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class ShotElementExtractorAgent(AgentBase[ShotElementExtractionResult]):
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"""[兼容] 逐镜信息提取：输入单镜文本+上文摘要，输出该镜的结构化提取结果。"""
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@property
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def system_prompt(self) -> str:
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return _ELEMENT_EXTRACTOR_SYSTEM_PROMPT
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@property
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def prompt_template(self) -> PromptTemplate:
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return ELEMENT_EXTRACTOR_PROMPT
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@property
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def output_model(self) -> type[ShotElementExtractionResult]:
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return ShotElementExtractionResult
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def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
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"""规范化元素提取结果（升级版结构）。"""
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data = dict(data)
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# 兼容：缺失 shot_division
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if "shot_division" not in data:
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data["shot_division"] = None
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if "elements" not in data or not isinstance(data["elements"], dict):
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data["elements"] = {}
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elements = data["elements"]
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# 兼容旧字段名：*_ids -> *_keys
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legacy_key_map = {
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"character_ids": "character_keys",
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"scene_ids": "scene_keys",
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"costume_ids": "costume_keys",
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"prop_ids": "prop_keys",
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}
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for old, new in legacy_key_map.items():
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if old in elements and new not in elements:
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val = elements.get(old)
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if isinstance(val, list):
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elements[new] = [str(x) for x in val]
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elements.pop(old, None)
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# 兼容旧字段名：dialog_lines -> dialogue_lines
|
||
if "dialog_lines" in elements and "dialogue_lines" not in elements:
|
||
elements["dialogue_lines"] = elements.pop("dialog_lines")
|
||
|
||
for key in (
|
||
"character_keys",
|
||
"scene_keys",
|
||
"costume_keys",
|
||
"prop_keys",
|
||
"characters_detailed",
|
||
"props_detailed",
|
||
"dialogue_lines",
|
||
"actions",
|
||
"shot_type_hints",
|
||
):
|
||
if key not in elements or not isinstance(elements[key], list):
|
||
elements[key] = []
|
||
|
||
if "scene_detailed" not in elements:
|
||
elements["scene_detailed"] = None
|
||
elif elements["scene_detailed"] is not None and not isinstance(elements["scene_detailed"], dict):
|
||
elements["scene_detailed"] = None
|
||
|
||
if "confidence_breakdown" not in elements or not isinstance(elements["confidence_breakdown"], dict):
|
||
elements["confidence_breakdown"] = {}
|
||
|
||
# 兼容旧结构：characters_detailed/props_detailed/scene_detailed 里字段名 *_id -> *_key
|
||
for c in elements.get("characters_detailed", []) or []:
|
||
if isinstance(c, dict) and "character_id" in c and "character_key" not in c:
|
||
c["character_key"] = str(c.pop("character_id"))
|
||
if isinstance(c, dict) and "evidence" not in c:
|
||
c["evidence"] = []
|
||
for p in elements.get("props_detailed", []) or []:
|
||
if isinstance(p, dict) and "prop_id" in p and "prop_key" not in p:
|
||
p["prop_key"] = str(p.pop("prop_id"))
|
||
if isinstance(p, dict) and "evidence" not in p:
|
||
p["evidence"] = []
|
||
sd = elements.get("scene_detailed")
|
||
if isinstance(sd, dict) and "scene_id" in sd and "scene_key" not in sd:
|
||
sd["scene_key"] = str(sd.pop("scene_id"))
|
||
if isinstance(sd, dict) and "evidence" not in sd:
|
||
sd["evidence"] = []
|
||
|
||
# 兼容旧对白行结构：补齐 schemas.DialogueLine 的可选字段
|
||
dl = elements.get("dialogue_lines")
|
||
if isinstance(dl, list):
|
||
for line in dl:
|
||
if not isinstance(line, dict):
|
||
continue
|
||
if "index" not in line and "order" in line:
|
||
line["index"] = line.pop("order")
|
||
if "evidence" not in line:
|
||
line["evidence"] = []
|
||
# 确保 line_mode 合法字符串（由 schema Literal 校验）
|
||
if "line_mode" not in line:
|
||
line["line_mode"] = "DIALOGUE"
|
||
|
||
data["elements"] = elements
|
||
|
||
if "confidence" not in data:
|
||
data["confidence"] = None
|
||
|
||
return data
|
||
|
||
|
||
# ============================================================================
|
||
# 2b. ElementExtractorAgent - 项目级信息提取（新流程最终输出）
|
||
# ============================================================================
|
||
|
||
class ElementExtractorAgent(AgentBase[StudioScriptExtractionDraft]):
|
||
"""项目级信息提取（最终输出）：输入剧本文本 + 分镜结果，产出全局实体表 + 逐镜关联。"""
|
||
|
||
@property
|
||
def system_prompt(self) -> str:
|
||
return _SCRIPT_EXTRACTOR_SYSTEM_PROMPT
|
||
|
||
@property
|
||
def prompt_template(self) -> PromptTemplate:
|
||
return SCRIPT_EXTRACTOR_PROMPT
|
||
|
||
@property
|
||
def output_model(self) -> type[StudioScriptExtractionDraft]:
|
||
return StudioScriptExtractionDraft
|
||
|
||
def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
|
||
data = dict(data)
|
||
data.setdefault("project_id", "")
|
||
data.setdefault("chapter_id", "")
|
||
data.setdefault("script_text", "")
|
||
for k in ("characters", "scenes", "props", "costumes", "shots"):
|
||
if k not in data or not isinstance(data[k], list):
|
||
data[k] = []
|
||
return data
|
||
|
||
|
||
# ============================================================================
|
||
# 3. EntityMergerAgent - 跨镜静态合并 + 基础画像生成
|
||
# ============================================================================
|
||
|
||
class EntityMergerAgent(AgentBase[EntityMergeResult]):
|
||
"""跨镜合并 + 基础画像生成：输入全部分镜提取结果+历史实体库，输出合并后的库。"""
|
||
|
||
@property
|
||
def system_prompt(self) -> str:
|
||
return _ENTITY_MERGER_SYSTEM_PROMPT
|
||
|
||
@property
|
||
def prompt_template(self) -> PromptTemplate:
|
||
return ENTITY_MERGER_PROMPT
|
||
|
||
@property
|
||
def output_model(self) -> type[EntityMergeResult]:
|
||
return EntityMergeResult
|
||
|
||
def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
|
||
"""规范化实体合并结果。"""
|
||
data = dict(data)
|
||
if "merged_library" not in data:
|
||
data["merged_library"] = {
|
||
"characters": [],
|
||
"locations": [],
|
||
"scenes": [],
|
||
"props": [],
|
||
"total_entries": 0,
|
||
}
|
||
lib = data["merged_library"]
|
||
# 兼容旧字段：缺 locations 时补空；旧结构可能没有 scenes
|
||
if "locations" not in lib:
|
||
lib["locations"] = []
|
||
if "scenes" not in lib:
|
||
lib["scenes"] = []
|
||
if "total_entries" not in lib:
|
||
lib["total_entries"] = sum(
|
||
len(lib.get(k, []) or [])
|
||
for k in ("characters", "locations", "scenes", "props")
|
||
)
|
||
# 兼容旧 variants 结构：dict[] -> EntityVariant[]（最小补齐）
|
||
for bucket_name in ("characters", "locations", "scenes", "props"):
|
||
bucket = lib.get(bucket_name)
|
||
if not isinstance(bucket, list):
|
||
continue
|
||
for ent in bucket:
|
||
if not isinstance(ent, dict):
|
||
continue
|
||
if "variants" in ent and isinstance(ent["variants"], list):
|
||
new_vars = []
|
||
for v in ent["variants"]:
|
||
if isinstance(v, dict):
|
||
if "variant_key" not in v:
|
||
v["variant_key"] = v.get("id") or v.get("key") or "variant"
|
||
if "affected_shots" not in v:
|
||
v["affected_shots"] = []
|
||
if "evidence" not in v:
|
||
v["evidence"] = []
|
||
new_vars.append(v)
|
||
else:
|
||
new_vars.append(
|
||
{
|
||
"variant_key": "variant",
|
||
"description": str(v),
|
||
"affected_shots": [],
|
||
"evidence": [],
|
||
}
|
||
)
|
||
ent["variants"] = new_vars
|
||
if "merge_stats" not in data:
|
||
data["merge_stats"] = {}
|
||
if "conflicts" not in data or not isinstance(data["conflicts"], list):
|
||
data["conflicts"] = []
|
||
return data
|
||
|
||
|
||
# ============================================================================
|
||
# 4. VariantAnalyzerAgent - 服装/外形变体检测与建议
|
||
# ============================================================================
|
||
|
||
class VariantAnalyzerAgent(AgentBase[VariantAnalysisResult]):
|
||
"""服装/外形变体检测与建议：输入实体库+全镜提取，输出变体分析结果。"""
|
||
|
||
@property
|
||
def system_prompt(self) -> str:
|
||
return _VARIANT_ANALYZER_SYSTEM_PROMPT
|
||
|
||
@property
|
||
def prompt_template(self) -> PromptTemplate:
|
||
return VARIANT_ANALYZER_PROMPT
|
||
|
||
@property
|
||
def output_model(self) -> type[VariantAnalysisResult]:
|
||
return VariantAnalysisResult
|
||
|
||
def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
|
||
"""规范化变体分析结果。"""
|
||
data = dict(data)
|
||
if "costume_timelines" not in data or not isinstance(data["costume_timelines"], list):
|
||
data["costume_timelines"] = []
|
||
if "variant_suggestions" not in data or not isinstance(data["variant_suggestions"], list):
|
||
data["variant_suggestions"] = []
|
||
if "chapter_variants" not in data or not isinstance(data["chapter_variants"], dict):
|
||
data["chapter_variants"] = {}
|
||
# 补齐可选 evidence 字段，避免 strict schema 校验失败
|
||
for tl in data.get("costume_timelines", []) or []:
|
||
if not isinstance(tl, dict):
|
||
continue
|
||
entries = tl.get("timeline_entries")
|
||
if isinstance(entries, list):
|
||
for e in entries:
|
||
if isinstance(e, dict) and "evidence" not in e:
|
||
e["evidence"] = []
|
||
for s in data.get("variant_suggestions", []) or []:
|
||
if isinstance(s, dict) and "evidence" not in s:
|
||
s["evidence"] = []
|
||
return data
|
||
|
||
|
||
# ============================================================================
|
||
# 5. ConsistencyCheckerAgent - 文本一致性检查
|
||
# ============================================================================
|
||
|
||
class ConsistencyCheckerAgent(AgentBase[ScriptConsistencyCheckResult]):
|
||
"""一致性检查（角色混淆）：输入原文，检测同一角色身份/行为混淆并给出修改建议。"""
|
||
|
||
@property
|
||
def system_prompt(self) -> str:
|
||
return _CONSISTENCY_CHECKER_SYSTEM_PROMPT
|
||
|
||
@property
|
||
def prompt_template(self) -> PromptTemplate:
|
||
return CONSISTENCY_CHECKER_PROMPT
|
||
|
||
@property
|
||
def output_model(self) -> type[ScriptConsistencyCheckResult]:
|
||
return ScriptConsistencyCheckResult
|
||
|
||
def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
|
||
"""规范化一致性检查结果（角色混淆）。"""
|
||
data = dict(data)
|
||
if "issues" not in data or not isinstance(data["issues"], list):
|
||
data["issues"] = []
|
||
for it in data["issues"]:
|
||
if isinstance(it, dict):
|
||
it.setdefault("issue_type", "character_confusion")
|
||
it.setdefault("character_candidates", [])
|
||
it.setdefault("affected_lines", None)
|
||
it.setdefault("evidence", [])
|
||
if "has_issues" not in data:
|
||
data["has_issues"] = len(data["issues"]) > 0
|
||
if "summary" not in data:
|
||
data["summary"] = None
|
||
return data
|
||
|
||
|
||
class ScriptOptimizerAgent(AgentBase[ScriptOptimizationResult]):
|
||
"""剧本优化 Agent：输入一致性检查输出 + 原文，输出优化后的剧本。"""
|
||
|
||
@property
|
||
def system_prompt(self) -> str:
|
||
return _SCRIPT_OPTIMIZER_SYSTEM_PROMPT
|
||
|
||
@property
|
||
def prompt_template(self) -> PromptTemplate:
|
||
return SCRIPT_OPTIMIZER_PROMPT
|
||
|
||
@property
|
||
def output_model(self) -> type[ScriptOptimizationResult]:
|
||
return ScriptOptimizationResult
|
||
|
||
def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
|
||
data = dict(data)
|
||
if "optimized_script_text" not in data:
|
||
data["optimized_script_text"] = ""
|
||
if "change_summary" not in data:
|
||
data["change_summary"] = ""
|
||
return data
|
||
|
||
|
||
# ============================================================================
|
||
# 6. OutputCompilerAgent - 最终输出打包
|
||
# ============================================================================
|
||
|
||
class OutputCompilerAgent(AgentBase[OutputCompileResult]):
|
||
"""最终输出打包：输入所有Agent状态，输出完整项目JSON + 表格数据。"""
|
||
|
||
@property
|
||
def system_prompt(self) -> str:
|
||
return _OUTPUT_COMPILER_SYSTEM_PROMPT
|
||
|
||
@property
|
||
def prompt_template(self) -> PromptTemplate:
|
||
return OUTPUT_COMPILER_PROMPT
|
||
|
||
@property
|
||
def output_model(self) -> type[OutputCompileResult]:
|
||
return OutputCompileResult
|
||
|
||
def _normalize(self, data: dict[str, Any]) -> dict[str, Any]:
|
||
"""规范化输出编译结果。"""
|
||
data = dict(data)
|
||
# 严格输出：project_json 必须能被 ProjectCinematicBreakdown 校验
|
||
if "project_json" not in data or not isinstance(data["project_json"], dict):
|
||
data["project_json"] = {}
|
||
pj = data["project_json"]
|
||
if isinstance(pj, dict):
|
||
# 补齐 ProjectCinematicBreakdown 必填字段
|
||
pj.setdefault("source_id", "unknown_source")
|
||
pj.setdefault("chunks", [])
|
||
pj.setdefault("characters", [])
|
||
pj.setdefault("locations", [])
|
||
pj.setdefault("props", [])
|
||
pj.setdefault("scenes", [])
|
||
pj.setdefault("shots", [])
|
||
pj.setdefault("transitions", [])
|
||
pj.setdefault("notes", [])
|
||
pj.setdefault("uncertainties", [])
|
||
if "tables" not in data or not isinstance(data["tables"], list):
|
||
data["tables"] = []
|
||
if "export_stats" not in data or not isinstance(data["export_stats"], dict):
|
||
data["export_stats"] = {
|
||
"total_tables": len(data["tables"]),
|
||
"total_rows": sum(t.get("row_count", 0) for t in data["tables"]),
|
||
}
|
||
return data
|
||
|