2084 lines
93 KiB
Python
2084 lines
93 KiB
Python
"""全能创作 · Agent 编排循环(契约 §3/§4)。
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和 script_agent.py 的根本区别:那边是「单次结构化出稿」,模型只会写脚本;
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这边是**真 function calling 循环** —— 模型自己决定这一轮该反问用户、该查素材,
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还是该出图/出片。
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铁律(踩过就回不来的三条):
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1. **视频 5–10 分钟,绝不在 SSE 里等。** 生成工具立刻返回 task_id,落一条
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`generating` 消息,发 `task` 事件,收流。前端轮询完成后原地换成 `result`。
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2. **`ask_user` 一旦被调用就中断循环。** 反问的意义是等人回答,继续跑下去
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等于自问自答。
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3. **一条用户消息最多计费生成一次。** 对话式会放大调用量,一句「多做几版」
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能烧掉一堆积分。
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SSE 事件见契约 §3。
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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from collections.abc import Iterator
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from dataclasses import dataclass
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from django.core.serializers.json import DjangoJSONEncoder
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from django.db import transaction
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from .creation import append_message, pin_refs
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from .creation_presets import preset_guidance
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from .mentions import TYPE_LABELS, infer_field_types, resolve_refs, search_mentions
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from .models import CreationConversation, CreationMessage, ModelConfig
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from .services import build_provider, get_default_model, get_seed_text_model, resolve_text_model
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logger = logging.getLogger(__name__)
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# 单条用户消息的循环上限。8 轮足够「查素材 → 反问 → 写方案 → 出图」,
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# 再多基本是模型在原地打转。
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MAX_TOOL_ROUNDS = 8
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# 单条用户消息最多触发一次计费生成(契约 §4)
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MAX_BILLED_GENERATIONS = 1
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# 记忆压缩(契约 §5):超过这么多条消息就把最老的一批压成一段摘要,
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# 只保留最近 KEEP_RECENT_MESSAGES 条原文。
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COMPRESS_AFTER_MESSAGES = 24
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KEEP_RECENT_MESSAGES = 12
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# 攒够这么多条没压过的消息才重压一次。没有它的话,过了阈值以后**每一轮都要多花
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# 一次模型调用**去重压那么两三句话 —— 长会话的成本会翻倍。
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COMPRESS_MIN_BATCH = 8
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FIELD_TYPES = ("single", "multi", "text", "asset")
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# 顶栏下拉里的展示名 → 火山模型名。前端给的是人看的label,submit_free_video 只认真名。
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VIDEO_MODEL_BY_LABEL = {
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"Seedance 2.5": "doubao-seedance-2-5-260628",
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"Seedance 2.0": "doubao-seedance-2-0-260128",
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"Seedance 2.0 Fast": "doubao-seedance-2-0-fast-260128",
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"Seedance 2.0 Mini": "doubao-seedance-2-0-mini-260615",
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}
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DEFAULT_VIDEO_MODEL = "doubao-seedance-2-5-260628"
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IMAGE_MODEL_BY_LABEL = {
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"Seedream5.0": "volcano",
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"Seedream-5.0-pro": "volcano",
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"YQ image2": "gpt-image",
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"影擎-Image2": "gpt-image",
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}
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# 「智能时长」= 交给我们定,取一个口播讲得完又不烧钱的中间值
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SMART_DURATION = 15
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# 全能创作 video_prompt 写作规范:从专业创作 / 一键成片(口播脚本)抽硬规则,
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# 适配「整段出片指令」而不是 ScriptDraft JSON。只要求口径接近,不改工具形态。
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_OMNI_VIDEO_PROMPT_RULES = """
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【出片脚本写法 · 对齐专业口播】
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表现形式默认**口播带货**,像真人在讲刚遇到的一件事,不要详情页朗读或主播念稿。
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video_prompt 必须能被出片模型直接执行,按时间轴写秒级分镜,建议结构:
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- 片头写清:总时长、画幅、整体光线/色调、口播语气(口语、有停顿与立场)
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- 然后按「0-3s / 3-8s / …」逐段写,每段同时写清这五项(不能省):
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1. 景别(大特写/特写/近景/中景/全景;一条片里至少切两次景别)
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2. 机位(平视/俯拍/仰拍/过肩/桌面视角)
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3. 运镜(手持跟拍/推近/拉远/横摇/环绕/固定 —— 每段至少一个运镜词)
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4. 动作(谁、哪只手、对什么、做什么;要连贯可拍,禁止「展示质感」等抽象词)
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5. 信息变化(这几秒画面上多了/变了什么)
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- 人声原文单独写清,必须用「人声(仅音频,由人物口型与配音表达,不出现在画面上):…」,禁止写成「口播:…」(裸写口播会被出片模型烧成字幕);字数贴近会话时长:大约 5.0–5.7 字/秒,
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15 秒约 75–85 字;太短撑不满,太长会赶。
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- 钩子段画面不要「对着镜头说话」静态开场;前 15 个口播字禁止「大家好/今天分享/给你们推荐」。
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- 全片只围绕一个具体情境推进一个主卖点;卖点要有看得见的证据(质地/前后变化/用法结果)。
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- CTA 像跟朋友说话;禁止小黄车/立即购买/闭眼入等平台指令腔。
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- **不要写字幕/花字/标题贴片/弹幕/角标/水印/购物浮层**,也不要写「无字幕」
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(否定说法也容易把字画上屏)。口播只存在于声音;包装上原有印刷字除外。
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- 已 @ 的角色/商品/场景参考图会自动附上,不要在 prompt 里重描长相;以图锁定性别年龄服装外形。
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- 用户说「商品说话 / 商品自述 / 商品拟人」时,默认采用**无脸拟人**:商品声音是画外角色声,
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商品本体不做口型、不新增卡通五官;性格靠整体倾斜、转向、弹跳、进退、镜头和音效表达。
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只有用户明确要求可见的卡通五官时才例外。
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- 同一场戏保持地点、光线、服装连续;要换环境就明确写下一时间段切换。
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"""
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_PRODUCT_VOICE_HINT_RE = re.compile(
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r"(商品|产品|包装|瓶|机身).{0,8}(说话|开口|自述|拟人)"
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r"|拟人.{0,8}(商品|产品|包装|瓶|机身)"
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)
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_VISIBLE_PRODUCT_FACE_RE = re.compile(
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r"(商品|产品|包装|瓶|机身).{0,12}(卡通)?(五官|眼睛|眼珠|嘴巴|嘴|口型)"
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r"|(卡通)?(五官|眼睛|眼珠|嘴巴|嘴|口型).{0,12}(商品|产品|包装|瓶|机身)"
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r"|(给|让).{0,12}(长出|加上|出现).{0,6}(卡通)?(五官|眼睛|眼珠|嘴巴|嘴|口型)"
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)
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PRODUCT_VOICE_VISUAL_GUARD = (
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"【商品角色表现·最高优先级】商品始终是参考图里的真实物件。包装正面、瓶身、机身和全部可见表面"
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"保持原有设计，只保留参考图本来就有的标签、图案与结构。拟人感完全通过整个商品轻微倾斜、"
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"转向、弹跳、进退，配合镜头、光影、环境反应和音效表达。商品台词采用画外角色声，声源不在"
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"画面内，商品保持完整物件形态；场景里的其他物件也保持真实原貌。整体采用精致实拍广告质感"
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"与克制幽默。前文若有改造商品外观的动作描述，统一改成商品整体运动的对应表达。"
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)
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def apply_product_voice_visual_guard(conversation: CreationConversation, prompt: str) -> str:
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"""商品拟人默认不长脸;用户明确要求可见卡通五官时尊重其创作选择。"""
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base = str(prompt or "").strip()
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recent_user_text = " ".join(
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conversation.messages.filter(
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role=CreationMessage.Role.USER,
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kind=CreationMessage.Kind.TEXT,
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).order_by("-seq").values_list("text", flat=True)[:12]
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)
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if _VISIBLE_PRODUCT_FACE_RE.search(recent_user_text):
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return base
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needs_guard = (
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conversation.preset == "商品拟人广告"
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or bool(_PRODUCT_VOICE_HINT_RE.search(recent_user_text))
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or bool(_PRODUCT_VOICE_HINT_RE.search(base))
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)
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if not needs_guard or PRODUCT_VOICE_VISUAL_GUARD in base:
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return base
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return f"{base}\n{PRODUCT_VOICE_VISUAL_GUARD}".strip()
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class AgentError(Exception):
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"""Agent 循环里的业务错误,已经是可以直接给用户看的中文。"""
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@dataclass
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class AgentContext:
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conversation: CreationConversation
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user: object
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model_config: ModelConfig
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generations_used: int = 0
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@property
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def team(self):
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return self.conversation.team
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@property
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def is_video(self) -> bool:
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return self.conversation.mode == CreationConversation.Mode.VIDEO
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_ASSET_PICK_LABEL = {
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"product": "换成哪个商品？",
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"character": "换成哪个角色？",
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"model": "换成哪个模特？",
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"scene": "换成哪个场景？",
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}
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_ASSET_PICK_PATTERNS = (
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("product", re.compile(r"(改|换|修改|更换|重新选|选(一个|个)?|挑).{0,8}商品")),
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("character", re.compile(r"(改|换|修改|更换|重新选|选(一个|个)?).{0,8}(角色|人物)")),
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("model", re.compile(r"(改|换|修改|更换|重新选|选(一个|个)?).{0,8}模特")),
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("scene", re.compile(r"(改|换|修改|更换|重新选|选(一个|个)?).{0,8}场景")),
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)
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_CHITCHAT_RE = re.compile(
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r"^\s*("
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r"hi|hello|hey|yo|hola|"
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r"你好呀?|您好|嗨|哈喽|嘿|"
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r"在吗|在不在|有人吗|"
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r"早+|早安|早上好|午安|晚安|"
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r"嗯+|哦+|噢+|额+|呃+|"
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r"好的?|行|可以|ok(?:ay)?|thanks?|thank\s*you|谢谢了?|感谢|"
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r"收到|知道了|明白了|了解"
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r")[\s!！.。~～？?…]*$",
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re.IGNORECASE,
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)
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_GREETING_RE = re.compile(
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r"^\s*("
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r"hi|hello|hey|yo|hola|"
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r"你好呀?|您好|嗨|哈喽|嘿|"
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r"在吗|在不在|有人吗|"
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r"早+|早安|早上好|午安|晚安"
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r")[\s!！.。~～？?…]*$",
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re.IGNORECASE,
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)
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def is_greeting(user_text: str) -> bool:
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"""单纯打招呼要立刻回应，不能为了分析引用素材去等模型。"""
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return bool(_GREETING_RE.match((user_text or "").strip()))
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def is_pure_chitchat(user_text: str) -> bool:
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"""纯打招呼 / 应答,没有创作意图。这类消息绝不能触发 write_strategy / write_plan。"""
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text = (user_text or "").strip()
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if not text or len(text) > 24:
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return False
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return bool(_CHITCHAT_RE.match(text))
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# 明确要做内容才算出方案/出图。闲聊、吐槽、问功能都不算。
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_CREATIVE_INTENT_RE = re.compile(
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r"("
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r"帮我做|帮我拍|帮我出|帮我写|帮我改|帮我生成|"
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r"创作[一两]?[条个张]?|创作(?:一条|个|短)?|"
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r"做[一两]?[条个张](?:视频|片|图|广告)?|拍[一两]?[条个](?:视频|片|广告)?|"
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r"出[一两]?[条个张](?:视频|片|图|广告)?|出片|出图|出方案|写方案|改方案|重写方案|重新写|"
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r"生成(?:一下|一张|几张|一条)?(?:视频|图|片|广告|脚本)?|做条|做个片|短视频|带货视频|短广告|广告片|带货片|"
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r"换卖点|改卖点|换剧情|改剧情|重做|重新出|按这个出|确认出片|"
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r"分镜|脚本|口播稿|storyboard|拟人|"
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r"(改|换|修改|更换).{0,8}(时长|秒数|比例|尺寸|画幅|分辨率|清晰度|模型)|"
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r"改成\s*\d+\s*秒|改成\s*\d+\s*[:：]\s*\d+|改成.{0,8}(竖屏|横屏|比例|分辨率)"
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r")",
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re.IGNORECASE,
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)
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# 创作 brief 常见搭配:动词 + 成片名词(「创作一条短广告」)
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_CREATIVE_VERB_RE = re.compile(r"创作|制作|拍摄|生成|做|拍|出|写|弄|来一条|来个")
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_CREATIVE_NOUN_RE = re.compile(r"视频|短片|短视频|广告|带货|出片|分镜|脚本|口播|主图|海报|成片")
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def has_creative_intent(user_text: str, refs: list | None = None) -> bool:
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"""用户本轮是否明确要做片/出图/改方案。
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没意图时不把 write_strategy / write_plan / generate_image 塞进 tools,
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避免模型把闲聊「整理」成方案卡。仅 @ 素材不算意图。
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"""
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text = (user_text or "").strip()
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if not text:
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return False
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if is_pure_chitchat(text):
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return False
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if _CREATIVE_INTENT_RE.search(text):
|
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return True
|
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# 「把商品…创作一条…短广告」这类 brief:同时有创作动词和成片名词
|
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if len(text) >= 8 and _CREATIVE_VERB_RE.search(text) and _CREATIVE_NOUN_RE.search(text):
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return True
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return False
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|
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|
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_CONTINUE_INTENT_RE = re.compile(
|
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r"^\s*(继续|继续做|接着|接着做|往下做|开始吧|开做吧|就这样|就按这个|按这个来|照这个做|直接做|直接来)[吧啊呀呢。.!！]*\s*$"
|
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)
|
||
|
||
|
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def is_continue_intent(user_text: str) -> bool:
|
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"""已有创作上下文时,这些短句是在授权继续,不是闲聊。"""
|
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return bool(_CONTINUE_INTENT_RE.match((user_text or "").strip()))
|
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|
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|
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def session_has_creative_context(conversation: CreationConversation) -> bool:
|
||
"""会话里是否已有可继续的创作进度(钉了素材 / 出过策略或方案)。
|
||
|
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这个信号只用于判断一句短回复是否承接创作,不拿来主动追问流程确认。
|
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"""
|
||
if conversation.pinned_refs:
|
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return True
|
||
if conversation.messages.filter(
|
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kind__in=(
|
||
CreationMessage.Kind.STRATEGY,
|
||
CreationMessage.Kind.PLAN,
|
||
CreationMessage.Kind.PROMPT_FILE,
|
||
CreationMessage.Kind.CONFIRM,
|
||
)
|
||
).exists():
|
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return True
|
||
# 追问发生在策略卡之前时,会话里可能还没有任何结构化产物。原始 brief 本身
|
||
# 就是创作上下文;不认它的话,刷新后一句「继续」会被当成空闲聊天。
|
||
recent_user_texts = conversation.messages.filter(
|
||
role=CreationMessage.Role.USER,
|
||
kind=CreationMessage.Kind.TEXT,
|
||
).order_by("-seq").values_list("text", flat=True)[:8]
|
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return any(has_creative_intent(item) for item in recent_user_texts)
|
||
|
||
|
||
def wanted_asset_pick(user_text: str, refs: list | None) -> str | None:
|
||
"""用户说「改商品」却没点名时,启动对应素材的自然确认。"""
|
||
if refs:
|
||
return None
|
||
text = user_text or ""
|
||
for type_, pattern in _ASSET_PICK_PATTERNS:
|
||
if pattern.search(text):
|
||
return type_
|
||
return None
|
||
|
||
|
||
def requested_asset_card_from_context(
|
||
conversation: CreationConversation,
|
||
user_text: str,
|
||
) -> str | None:
|
||
"""「发一下卡片我选」没有说类型时,沿用最近一次素材追问的类型。"""
|
||
text = str(user_text or "")
|
||
wants_card = re.search(
|
||
r"(发|打开|展示|看看|看下|给我).{0,10}(卡片|列表|商品库|素材库)"
|
||
r"|(卡片|列表).{0,10}(选|选择|看看|看下)",
|
||
text,
|
||
)
|
||
if not wants_card:
|
||
return None
|
||
recent = conversation.messages.filter(
|
||
kind=CreationMessage.Kind.ELICIT
|
||
).order_by("-seq")[:8]
|
||
for message in recent:
|
||
payload = message.payload or {}
|
||
fields = payload.get("pending_fields") or payload.get("fields") or []
|
||
for field in fields:
|
||
if not isinstance(field, dict):
|
||
continue
|
||
inferred = infer_field_types(field)
|
||
if len(inferred) == 1 and inferred[0] in TYPE_LABELS:
|
||
return inferred[0]
|
||
return None
|
||
|
||
|
||
|
||
VIDEO_MODELS = ["Seedance 2.5", "Seedance 2.0", "Seedance 2.0 Fast", "Seedance 2.0 Mini"]
|
||
IMAGE_MODELS = ["Seedream5.0", "YQ image2"]
|
||
RATIOS = ["16:9", "9:16", "4:3", "3:4", "1:1"]
|
||
RESOLUTIONS = ["480p", "720p", "1080p"]
|
||
VIDEO_DURATIONS = ["智能时长", "4 秒", "5 秒", "6 秒", "8 秒", "10 秒", "12 秒", "15 秒", "30 秒"]
|
||
IMAGE_COUNTS = ["1 张", "2 张", "4 张", "8 张"]
|
||
SESSION_PARAM_KEYS = ("duration", "ratio", "resolution", "video_model", "count")
|
||
_PARAM_TO_STORED = {"video_model": "model"}
|
||
|
||
_PARAM_PICK_LABEL = {
|
||
"duration": "想改成多少秒？",
|
||
"ratio": "想换成什么画幅？比如 9:16 竖屏或 16:9 横屏。",
|
||
"resolution": "想换成什么清晰度？直接说 480p、720p 或 1080p 就行。",
|
||
"video_model": "想换哪个模型？直接告诉我模型名就行。",
|
||
"count": "这次想出几张？",
|
||
}
|
||
|
||
|
||
def _param_options(key: str, is_video: bool) -> list[str]:
|
||
if key == "duration":
|
||
return VIDEO_DURATIONS
|
||
if key == "ratio":
|
||
return RATIOS
|
||
if key == "resolution":
|
||
return RESOLUTIONS
|
||
if key == "count":
|
||
return IMAGE_COUNTS
|
||
return VIDEO_MODELS if is_video else IMAGE_MODELS
|
||
|
||
|
||
def session_param_fields(keys: list[str], is_video: bool) -> list[dict]:
|
||
fields = []
|
||
# 对话式追问一次只问一件事，避免又退化成参数问卷。
|
||
for key in keys[:1]:
|
||
if key not in _PARAM_PICK_LABEL:
|
||
continue
|
||
options = [{"value": item, "label": item} for item in _param_options(key, is_video)]
|
||
fields.append({
|
||
"key": key,
|
||
"label": _PARAM_PICK_LABEL[key],
|
||
"type": "single",
|
||
"required": True,
|
||
"options": options,
|
||
})
|
||
return fields
|
||
|
||
|
||
def wanted_param_keys(user_text: str, *, is_video: bool) -> list[str]:
|
||
"""用户说「改时长」「换模型」时弹出参数卡。不和「改模特」抢。"""
|
||
text = user_text or ""
|
||
keys: list[str] = []
|
||
if re.search(r"(改|换|修改|更换).{0,8}(时长|秒数)|改成\s*\d+\s*秒", text):
|
||
keys.append("duration" if is_video else "count")
|
||
if re.search(r"(改|换|修改|更换).{0,8}(比例|尺寸|画幅)", text):
|
||
keys.append("ratio")
|
||
if re.search(r"(改|换|修改|更换).{0,8}(分辨率|清晰度)", text):
|
||
keys.append("resolution")
|
||
if re.search(r"(改|换|修改|更换).{0,8}模型", text) and "模特" not in text:
|
||
keys.append("video_model")
|
||
if re.search(r"(改|换|修改|更换).{0,8}张数", text):
|
||
keys.append("count")
|
||
if not keys and re.search(r"(改|换|修改).{0,6}(参数|设置|规格)", text):
|
||
keys = ["duration", "video_model", "ratio"] if is_video else ["count", "video_model", "ratio"]
|
||
seen = set()
|
||
out = []
|
||
for key in keys:
|
||
if key in seen:
|
||
continue
|
||
seen.add(key)
|
||
out.append(key)
|
||
return out
|
||
|
||
|
||
def apply_session_params(conversation, fields, answers: dict) -> bool:
|
||
"""追问卡里选出的时长/模型等写回会话参数。返回是否有改动。"""
|
||
current = dict(conversation.params or {})
|
||
field_by_key = {str(item.get("key") or ""): item for item in (fields or []) if isinstance(item, dict)}
|
||
changed = False
|
||
for key, raw in (answers or {}).items():
|
||
field = field_by_key.get(str(key)) or {}
|
||
if field.get("type") == "asset":
|
||
continue
|
||
stored = _PARAM_TO_STORED.get(str(key), str(key))
|
||
if stored not in {"model", "ratio", "resolution", "duration", "count"}:
|
||
continue
|
||
value = "、".join(raw) if isinstance(raw, list) else str(raw or "").strip()
|
||
if not value or current.get(stored) == value:
|
||
continue
|
||
current[stored] = value
|
||
changed = True
|
||
if changed:
|
||
conversation.params = current
|
||
conversation.save(update_fields=["params", "updated_at"])
|
||
return changed
|
||
|
||
|
||
def snapshot_session_params(conversation) -> dict:
|
||
params = conversation.params or {}
|
||
return {
|
||
"model": str(params.get("model") or ""),
|
||
"resolution": str(params.get("resolution") or ""),
|
||
"ratio": str(params.get("ratio") or ""),
|
||
"duration": str(params.get("duration") or ""),
|
||
"count": str(params.get("count") or params.get("duration") or ""),
|
||
}
|
||
|
||
|
||
def confirm_param_options(is_video: bool) -> dict:
|
||
return {
|
||
"model": VIDEO_MODELS if is_video else IMAGE_MODELS,
|
||
"resolution": RESOLUTIONS if is_video else [],
|
||
"ratio": RATIOS,
|
||
"duration": VIDEO_DURATIONS if is_video else [],
|
||
"count": IMAGE_COUNTS if not is_video else [],
|
||
}
|
||
|
||
|
||
def apply_confirm_params(conversation, incoming: dict | None) -> tuple[dict, bool]:
|
||
"""确认卡上改的参数写回会话。返回 (最新 params, 视频时长是否变了)。"""
|
||
current = dict(conversation.params or {})
|
||
old_duration = str(current.get("duration") or "")
|
||
changed = False
|
||
for key, raw in (incoming or {}).items():
|
||
if key not in {"model", "ratio", "resolution", "duration", "count"}:
|
||
continue
|
||
value = str(raw or "").strip()
|
||
if not value or current.get(key) == value:
|
||
continue
|
||
current[key] = value
|
||
changed = True
|
||
duration_changed = (
|
||
conversation.mode == CreationConversation.Mode.VIDEO
|
||
and str(current.get("duration") or "") != old_duration
|
||
and bool(str(current.get("duration") or ""))
|
||
and bool(old_duration)
|
||
)
|
||
if changed:
|
||
conversation.params = current
|
||
conversation.save(update_fields=["params", "updated_at"])
|
||
return snapshot_session_params(conversation), duration_changed
|
||
|
||
|
||
# ---------------------------------------------------------------- 工具 schema
|
||
|
||
def tool_schemas(context: AgentContext, *, allow_plan: bool = True) -> list[dict]:
|
||
"""给模型看的工具清单。图片会话不暴露 generate_video,反之亦然 ——
|
||
会话 mode 是定死的(契约 §0),把不该用的工具摆出来只会诱导模型走错路。
|
||
allow_plan=False 时隐藏 write_strategy / write_plan / generate_image,闲聊用不出来。"""
|
||
tools = [
|
||
{
|
||
"type": "function",
|
||
"function": {
|
||
"name": "ask_user",
|
||
"description": (
|
||
"缺少必要信息时向用户追问。"
|
||
"只在信息**确实缺失且无法合理推断**时用;能自己定的就自己定,别把用户当填表机器。"
|
||
"用户要选/改/换商品、角色、模特、场景时**必须**调这个工具,"
|
||
"type 用 asset 并填 asset_types。系统会以类Agent自然对话轻量询问是否需要发送商品列表(同时支持直接输入名字或由你推荐),避免一上来粗暴弹出大卡片打断交流。"
|
||
"一次只问 1 项,禁止问「要不要继续」「要不要生成」「是否开始创作」这类流程问题。"
|
||
),
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"fields": {
|
||
"type": "array",
|
||
"maxItems": 1,
|
||
"items": {
|
||
"type": "object",
|
||
"properties": {
|
||
"key": {"type": "string", "description": "英文标识,如 product / duration"},
|
||
"label": {"type": "string", "description": "问题原文,中文"},
|
||
"type": {"type": "string", "enum": list(FIELD_TYPES)},
|
||
"required": {"type": "boolean"},
|
||
"options": {
|
||
"type": "array",
|
||
"items": {
|
||
"type": "object",
|
||
"properties": {
|
||
"value": {"type": "string"},
|
||
"label": {"type": "string"},
|
||
},
|
||
"required": ["value", "label"],
|
||
},
|
||
},
|
||
"asset_types": {
|
||
"type": "array",
|
||
"items": {"type": "string", "enum": list(TYPE_LABELS)},
|
||
},
|
||
"placeholder": {"type": "string"},
|
||
},
|
||
"required": ["key", "label", "type"],
|
||
},
|
||
}
|
||
},
|
||
"required": ["fields"],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
"type": "function",
|
||
"function": {
|
||
"name": "search_library",
|
||
"description": "在团队的商品库/模特库/角色/场景/资产库里找素材。用户说了名字但没 @ 时用它找回来。",
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"query": {"type": "string"},
|
||
"types": {"type": "array", "items": {"type": "string", "enum": list(TYPE_LABELS)}},
|
||
},
|
||
"required": ["query"],
|
||
},
|
||
},
|
||
},
|
||
]
|
||
if not allow_plan:
|
||
# 闲聊轮次不给 ask_user,避免模型追问「要不要出片」;
|
||
# 换商品/改参数仍由 wanted_asset_pick / wanted_param_keys 兜底追问。
|
||
return [t for t in tools if t.get("function", {}).get("name") == "search_library"]
|
||
if context.is_video:
|
||
tools.append({
|
||
"type": "function",
|
||
"function": {
|
||
"name": "write_strategy",
|
||
"description": (
|
||
"写「创作策略理解」卡:说清这条片给谁看、他为什么会信、你想让他信什么、整体创作方向。"
|
||
"四个字段都必须写具体非空文案,禁止空字符串。"
|
||
"仅当用户明确要做片/出方案时,在 write_plan 之前调一次;打招呼或闲聊不要调。"
|
||
),
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"target": {"type": "string", "description": "这条视频给谁看,要具体到人群特征"},
|
||
"trust": {"type": "string", "description": "用户为什么相信,靠什么建立可信度"},
|
||
"belief": {"type": "string", "description": "希望用户看完相信什么"},
|
||
"direction": {"type": "string", "description": "创作方向一句话,说清是什么类型的片"},
|
||
},
|
||
"required": ["target", "trust", "belief", "direction"],
|
||
},
|
||
},
|
||
})
|
||
tools.append({
|
||
"type": "function",
|
||
"function": {
|
||
"name": "write_plan",
|
||
"description": (
|
||
"写「视频最终方案」卡并请用户确认。**仅当用户明确要做片/出方案/改方案时调用**;"
|
||
"打招呼或闲聊不要调。调完会等用户点确认,确认后平台直接按 video_prompt 出片。"
|
||
"usp / points / timeline 是给用户看的卡片正文,必须写满具体文案,禁止空着只交 video_prompt。"
|
||
"video_prompt 按系统里的「出片脚本写法」写成口播秒级分镜(专业创作同口径),不要只写大纲。"
|
||
"先调 write_strategy 再调它。"
|
||
),
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"usp": {"type": "string", "description": "主打卖点,全片只讲这一个核心价值"},
|
||
"points": {
|
||
"type": "array", "maxItems": 3, "items": {"type": "string"},
|
||
"description": "核心支撑卖点,最多 3 条",
|
||
},
|
||
"timeline": {
|
||
"type": "array",
|
||
"items": {
|
||
"type": "object",
|
||
"properties": {
|
||
"start": {"type": "number"}, "end": {"type": "number"},
|
||
"stage": {"type": "string", "description": "Hook / 过桥 / 正文 / CTA"},
|
||
"desc": {"type": "string"},
|
||
},
|
||
"required": ["start", "end", "stage"],
|
||
},
|
||
},
|
||
"voice_chars": {
|
||
"type": "array", "items": {"type": "integer"},
|
||
"description": "口播字数区间 [下限, 上限]",
|
||
},
|
||
"video_prompt": {
|
||
"type": "string",
|
||
"description": (
|
||
"交给出片模型的完整口播带货指令(对齐专业创作口径)。"
|
||
"必须含:总时长与画幅、整体光线色调、按 0-Ns 分段的秒级分镜"
|
||
"(每段写清景别/机位/运镜/具体动作/信息变化)、人声(仅音频抬头)原文、一个主卖点与可见证据、口语 CTA。禁止「口播:」字样。"
|
||
"禁止字幕/花字/贴片及「无字幕」字样;禁止详情页腔与「大家好」开场。"
|
||
"已 @ 素材会自动作参考图,勿重描长相。"
|
||
),
|
||
},
|
||
},
|
||
"required": ["usp", "points", "video_prompt"],
|
||
},
|
||
},
|
||
})
|
||
else:
|
||
tools.append({
|
||
"type": "function",
|
||
"function": {
|
||
"name": "generate_image",
|
||
"description": (
|
||
"生成图片。prompt 必须是完整、可独立执行的画面描述(主体/动作/环境/光线/构图/风格),"
|
||
"不要写成对用户说的话。已 @ 引用的素材会自动作为参考图带上,不用在 prompt 里重复描述它们的外观。"
|
||
),
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"prompt": {"type": "string"},
|
||
"count": {"type": "integer", "minimum": 1, "maximum": 4},
|
||
},
|
||
"required": ["prompt"],
|
||
},
|
||
},
|
||
})
|
||
return tools
|
||
|
||
|
||
# ---------------------------------------------------------------- 工具执行
|
||
|
||
|
||
def _coerce_fields(raw) -> list[dict]:
|
||
"""把模型给的 fields 规整成契约 §2 的 Field。脏数据丢弃而不是抛 ——
|
||
模型偶尔漏个 type 不该让整条对话崩掉。"""
|
||
fields: list[dict] = []
|
||
for item in (raw or [])[:1]:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
key = str(item.get("key") or "").strip()
|
||
label = str(item.get("label") or "").strip()
|
||
type_ = str(item.get("type") or "").strip()
|
||
if not key or not label or type_ not in FIELD_TYPES:
|
||
continue
|
||
field = {
|
||
"key": key,
|
||
"label": label,
|
||
"type": type_,
|
||
"required": bool(item.get("required", True)),
|
||
}
|
||
options = [
|
||
{"value": str(o.get("value")), "label": str(o.get("label"))}
|
||
for o in (item.get("options") or [])
|
||
if isinstance(o, dict) and o.get("value") and o.get("label")
|
||
]
|
||
if type_ in ("single", "multi"):
|
||
if not options:
|
||
continue # 单选/多选没选项 = 废卡,丢掉
|
||
field["options"] = options
|
||
if type_ == "asset":
|
||
asset_types = [t for t in (item.get("asset_types") or []) if t in TYPE_LABELS]
|
||
field["asset_types"] = asset_types or list(TYPE_LABELS)
|
||
if type_ == "text":
|
||
field["placeholder"] = str(item.get("placeholder") or "")
|
||
inferred = infer_field_types(field)
|
||
# 商品/角色这类必须出素材卡,文字单选钉不上参考图
|
||
if key in SESSION_PARAM_KEYS:
|
||
fields.append(field)
|
||
continue
|
||
if len(inferred) == 1 and inferred[0] in TYPE_LABELS and type_ != "asset":
|
||
field["type"] = "asset"
|
||
field["asset_types"] = inferred
|
||
field.pop("options", None)
|
||
field.pop("placeholder", None)
|
||
fields.append(field)
|
||
return fields
|
||
|
||
|
||
|
||
|
||
def _normalize_model_label(value: str) -> str:
|
||
return "".join(ch for ch in str(value or "").lower() if ch.isalnum())
|
||
|
||
|
||
def image_model_name(params: dict) -> str | None:
|
||
"""出图模型 label → 供应商模型名;目录优先,认不出返回 None 让下游用默认。"""
|
||
from django.db.models import Q
|
||
|
||
from .models import ModelConfig
|
||
|
||
label = str(params.get("model") or "").strip()
|
||
if not label:
|
||
return None
|
||
mapped = IMAGE_MODEL_BY_LABEL.get(label)
|
||
if mapped:
|
||
return mapped
|
||
hit = (
|
||
ModelConfig.objects.filter(capability=ModelConfig.Capability.IMAGE, status=ModelConfig.Status.ACTIVE)
|
||
.filter(Q(display_name=label) | Q(name=label))
|
||
.order_by("created_at")
|
||
.first()
|
||
)
|
||
return hit.name if hit else None
|
||
|
||
|
||
def video_model_name(params: dict) -> str:
|
||
"""会话参数里的模型 label → 供应商模型名。
|
||
|
||
先认历史写死映射,再按 ModelConfig.display_name / name 查目录 —— 后台新加模型不用改代码。
|
||
"""
|
||
from django.db.models import Q
|
||
|
||
from .models import ModelConfig
|
||
|
||
label = str(params.get("model") or "").strip()
|
||
if not label:
|
||
return DEFAULT_VIDEO_MODEL
|
||
mapped = VIDEO_MODEL_BY_LABEL.get(label)
|
||
if not mapped:
|
||
norm = _normalize_model_label(label)
|
||
for k, v in VIDEO_MODEL_BY_LABEL.items():
|
||
if _normalize_model_label(k) == norm:
|
||
mapped = v
|
||
break
|
||
if mapped:
|
||
return mapped
|
||
hit = (
|
||
ModelConfig.objects.filter(capability=ModelConfig.Capability.VIDEO, status=ModelConfig.Status.ACTIVE)
|
||
.filter(Q(display_name=label) | Q(name=label))
|
||
.order_by("created_at")
|
||
.first()
|
||
)
|
||
if hit is None:
|
||
hit = (
|
||
ModelConfig.objects.filter(capability=ModelConfig.Capability.VIDEO, status=ModelConfig.Status.ACTIVE)
|
||
.filter(Q(display_name__icontains=label) | Q(name__icontains=label))
|
||
.order_by("created_at")
|
||
.first()
|
||
)
|
||
return hit.name if hit else DEFAULT_VIDEO_MODEL
|
||
|
||
|
||
def video_duration(params: dict) -> int:
|
||
"""「15 秒」→ 15;「智能时长」/ 解析不出 → SMART_DURATION。"""
|
||
raw = str(params.get("duration") or "")
|
||
digits = "".join(ch for ch in raw if ch.isdigit())
|
||
if not digits:
|
||
return SMART_DURATION
|
||
return max(4, min(int(digits), 30))
|
||
|
||
|
||
def _image_count(params: dict, raw) -> int:
|
||
"""出图张数:首页选过「N 张」就用它,否则用模型传的 count,默认 1,上限 8。"""
|
||
label = str((params or {}).get("count") or (params or {}).get("duration") or "")
|
||
if "张" in label:
|
||
digits = "".join(ch for ch in label if ch.isdigit())
|
||
if digits:
|
||
raw = digits
|
||
try:
|
||
count = int(raw or 1)
|
||
except (TypeError, ValueError):
|
||
count = 1
|
||
return max(1, min(count, 8))
|
||
|
||
|
||
def _run_search_library(context: AgentContext, args: dict) -> dict:
|
||
results = search_mentions(
|
||
context.team,
|
||
q=str(args.get("query") or "").strip(),
|
||
types=[t for t in (args.get("types") or []) if t in TYPE_LABELS] or None,
|
||
limit=5,
|
||
)
|
||
return {
|
||
"results": [
|
||
{"type": r["type"], "id": r["id"], "name": r["name"], "kind": TYPE_LABELS[r["type"]]}
|
||
for r in results
|
||
]
|
||
}
|
||
|
||
|
||
def _run_generate_image(context: AgentContext, args: dict) -> tuple[dict, list]:
|
||
"""提交出图。返回 (给模型看的结果, AITask 列表)。
|
||
|
||
出图也是异步的(worker 出图 ~30s),所以这里同样只提交不等待 —— 和视频一条路子,
|
||
前端拿 task_id 轮询 GET /api/ai/generate-image/?ids=…
|
||
"""
|
||
from .services import enqueue_standalone_images
|
||
|
||
prompt = str(args.get("prompt") or "").strip()
|
||
if not prompt:
|
||
raise AgentError("生成失败:模型没有给出画面描述")
|
||
|
||
params = context.conversation.params or {}
|
||
resolved = resolve_refs(context.team, context.conversation.pinned_refs or [])
|
||
reference_image_ids = [r["asset_id"] for r in resolved.references if r.get("asset_id")]
|
||
count = _image_count(params, args.get("count"))
|
||
|
||
tasks = enqueue_standalone_images(
|
||
team=context.team,
|
||
user=context.user,
|
||
prompt=prompt,
|
||
mode="image",
|
||
count=count,
|
||
ratio=params.get("ratio") or None,
|
||
image_model=image_model_name(params) or params.get("model") or None,
|
||
reference_image_ids=reference_image_ids or None,
|
||
feature="omni_create",
|
||
)
|
||
context.generations_used += 1
|
||
return (
|
||
{"submitted": True, "count": len(tasks), "note": "已提交生成,结果稍后回填,不要重复提交"},
|
||
list(tasks),
|
||
)
|
||
|
||
|
||
def _video_submit_params(context: AgentContext, prompt: str) -> tuple[dict, list]:
|
||
"""拼 submit_free_video 的入参。references 直接用 resolve_refs 的产物 ——
|
||
它已经排好 角色 → 场景 → 商品 的顺序,那正是出片模型 @图N 的语义依据。"""
|
||
params = context.conversation.params or {}
|
||
resolved = resolve_refs(context.team, context.conversation.pinned_refs or [])
|
||
prompt = apply_product_voice_visual_guard(context.conversation, prompt)
|
||
submit = {
|
||
"prompt": prompt,
|
||
"feature": "omni_create",
|
||
"mode": "universal",
|
||
"model": video_model_name(params),
|
||
"aspect_ratio": params.get("ratio") or "9:16",
|
||
"resolution": params.get("resolution") or "720p",
|
||
"duration": video_duration(params),
|
||
"generate_audio": True,
|
||
"references": resolved.references,
|
||
}
|
||
return submit, resolved.references
|
||
|
||
|
||
def estimate_video_credits(context: AgentContext) -> int:
|
||
"""确认按钮旁的预计积分。算不出来返回 0,前端就不显示 ——
|
||
估价失败绝不能挡住出片(用户仍会在扣费环节看到真实数字)。"""
|
||
from apps.billing.pricing import quote_video_estimate
|
||
|
||
params = context.conversation.params or {}
|
||
submit, references = _video_submit_params(context, "")
|
||
model_config = ModelConfig.objects.filter(
|
||
name=submit["model"], capability=ModelConfig.Capability.VIDEO
|
||
).first()
|
||
if model_config is None:
|
||
return 0
|
||
try:
|
||
_tokens, quote = quote_video_estimate(
|
||
model_config,
|
||
aspect_ratio=submit["aspect_ratio"],
|
||
resolution=submit["resolution"],
|
||
duration=submit["duration"],
|
||
references=references,
|
||
team=context.team,
|
||
)
|
||
return int(quote.points)
|
||
except Exception: # noqa: BLE001 — 估价挂了不该挡住出片
|
||
logger.warning("omni create: video estimate failed", exc_info=True)
|
||
return 0
|
||
|
||
|
||
def estimate_image_credits(context: AgentContext) -> int:
|
||
"""出图确认卡预计积分:挂牌单价(含团队系数)逐张取整后再 × 张数,与 enqueue 逐任务预留同口径。"""
|
||
from apps.billing.pricing import quote_flat
|
||
from apps.ai.services import resolve_image_model, get_default_model
|
||
|
||
params = context.conversation.params or {}
|
||
model_name = image_model_name(params) or str(params.get("model") or "").strip() or None
|
||
model_config = resolve_image_model(model_name) if model_name else None
|
||
if model_config is None:
|
||
model_config = get_default_model(ModelConfig.Capability.IMAGE)
|
||
if model_config is None:
|
||
return 0
|
||
count = _image_count(params, None)
|
||
try:
|
||
per = quote_flat(model_config, units=1, team=context.team)
|
||
return int(per.points) * count
|
||
except Exception: # noqa: BLE001
|
||
logger.warning("omni create: image estimate failed", exc_info=True)
|
||
return 0
|
||
|
||
|
||
def submit_confirmed_video(*, conversation: CreationConversation, user, confirm_message: CreationMessage):
|
||
"""用户点了确认 → 直接按方案卡里存好的 video_prompt 出片。
|
||
|
||
**这里不再跑一轮模型**:方案已经确认过了,再让模型决定一次既费钱又可能它不调工具。
|
||
返回 (生成中消息, 错误文案),两者必有其一。
|
||
"""
|
||
from .free_video import submit_free_video
|
||
|
||
payload = confirm_message.payload or {}
|
||
prompt = str(payload.get("video_prompt") or "").strip()
|
||
if not prompt:
|
||
return None, "这条方案没有存下出片指令，请让我重新写一次方案。"
|
||
|
||
context = AgentContext(conversation=conversation, user=user, model_config=None)
|
||
submit, _references = _video_submit_params(context, prompt)
|
||
try:
|
||
task = submit_free_video(team=conversation.team, user=user, params=submit)
|
||
except ValueError as exc: # 校验类错误(时长/比例/额度),给用户看原文
|
||
return None, str(exc)
|
||
|
||
message = append_message(
|
||
conversation, role="assistant", kind=CreationMessage.Kind.GENERATING,
|
||
payload={"task_id": str(task.id), "kind": "video", "prompt": prompt}, task=task,
|
||
)
|
||
_remember_artifact(conversation, prompt, "video")
|
||
return message, ""
|
||
|
||
|
||
def submit_confirmed_image(*, conversation: CreationConversation, user, confirm_message: CreationMessage):
|
||
"""用户点了确认 → 按确认卡里存的画面描述出图。同样不跑一轮模型。"""
|
||
payload = confirm_message.payload or {}
|
||
prompt = str(payload.get("prompt") or payload.get("image_prompt") or "").strip()
|
||
if not prompt:
|
||
return None, "这条方案没有存下出图指令，请让我重新写一次。"
|
||
|
||
context = AgentContext(conversation=conversation, user=user, model_config=None)
|
||
try:
|
||
_result, tasks = _run_generate_image(context, {"prompt": prompt})
|
||
except AgentError as exc:
|
||
return None, str(exc)
|
||
except ValueError as exc:
|
||
return None, str(exc)
|
||
|
||
message = None
|
||
for task in tasks:
|
||
message = append_message(
|
||
conversation, role="assistant",
|
||
kind=CreationMessage.Kind.GENERATING,
|
||
payload={"task_id": str(task.id), "kind": "image", "prompt": prompt},
|
||
task=task,
|
||
)
|
||
if message is None:
|
||
return None, "出图没有提交成功，请再试一次。"
|
||
_remember_artifact(conversation, prompt, "image")
|
||
return message, ""
|
||
|
||
|
||
# ---------------------------------------------------------------- 提示词
|
||
|
||
|
||
|
||
def get_creation_chat_model(requested: ModelConfig | None = None) -> ModelConfig | None:
|
||
"""全能创作编排固定优先 Seed 2.1 Pro;显式传入的可用模型仍尊重用户选择。"""
|
||
if requested is not None:
|
||
return resolve_text_model(requested)
|
||
return get_seed_text_model() or resolve_text_model(None)
|
||
|
||
|
||
def _creation_model_sees_images(model_config: ModelConfig | None) -> bool:
|
||
"""对话模型能不能收图。参考图只在能看图时才塞进 chat messages,避免纯文本模型整轮失败。"""
|
||
if model_config is None:
|
||
return False
|
||
if getattr(model_config, "capability", "") == ModelConfig.Capability.VISION:
|
||
return True
|
||
name = str(getattr(model_config, "name", "") or "").lower()
|
||
# 豆包 Seed 2.x / 1.6 文本档都支持图文;vl / vision 后缀同理。
|
||
if name.startswith("doubao-seed-") or "vision" in name or name.endswith("-vl") or "-vl-" in name:
|
||
return True
|
||
metadata = model_config.metadata if isinstance(getattr(model_config, "metadata", None), dict) else {}
|
||
capabilities = metadata.get("capabilities") if isinstance(metadata.get("capabilities"), dict) else {}
|
||
features = {str(item) for item in capabilities.get("features") or []}
|
||
return bool({"vision", "image_input", "multimodal"} & features)
|
||
|
||
|
||
def _prefer_vision_text_model(current: ModelConfig | None, team, refs: list | None) -> ModelConfig | None:
|
||
"""有参考图时,尽量换成能看图的文本模型(豆包 Seed 等),否则聊天侧完全看不见男女。"""
|
||
if current is not None and _creation_model_sees_images(current):
|
||
return current
|
||
if not _ref_image_urls(team, refs):
|
||
return current
|
||
qs = (
|
||
ModelConfig.objects.select_related("provider")
|
||
.filter(
|
||
capability=ModelConfig.Capability.TEXT,
|
||
status=ModelConfig.Status.ACTIVE,
|
||
provider__status="active",
|
||
)
|
||
.order_by("created_at")
|
||
)
|
||
for candidate in qs:
|
||
if _creation_model_sees_images(candidate):
|
||
return candidate
|
||
return get_default_model(ModelConfig.Capability.VISION) or current
|
||
|
||
|
||
def _ref_image_urls(team, refs: list | None) -> list[str]:
|
||
resolved = resolve_refs(team, refs or [])
|
||
urls: list[str] = []
|
||
seen: set[str] = set()
|
||
for item in resolved.references:
|
||
url = str(item.get("url") or "").strip()
|
||
if not url or url in seen:
|
||
continue
|
||
seen.add(url)
|
||
urls.append(url)
|
||
return urls[:6]
|
||
|
||
|
||
def _attach_ref_images(messages: list[dict], image_urls: list[str]) -> list[dict]:
|
||
"""把锁定素材图挂到最近一条 user 消息上(OpenAI image_url 格式)。"""
|
||
if not image_urls or not messages:
|
||
return messages
|
||
note = (
|
||
f"【参考图·请亲眼看】下面 {len(image_urls)} 张是用户锁定的素材。"
|
||
"人物的性别、年龄段、发型、服装必须以图为准;看不清再问用户,禁止凭文件名猜测性别。"
|
||
)
|
||
out = [dict(message) for message in messages]
|
||
index = next((i for i in range(len(out) - 1, -1, -1) if out[i].get("role") == "user"), None)
|
||
if index is None:
|
||
content = [{"type": "text", "text": note}]
|
||
content.extend({"type": "image_url", "image_url": {"url": url}} for url in image_urls)
|
||
out.append({"role": "user", "content": content})
|
||
return out
|
||
last = dict(out[index])
|
||
raw = last.get("content")
|
||
if isinstance(raw, list):
|
||
content = list(raw)
|
||
text_bits = [str(item.get("text") or "") for item in content if isinstance(item, dict) and item.get("type") == "text"]
|
||
if not any(note[:8] in bit for bit in text_bits):
|
||
content.append({"type": "text", "text": note})
|
||
existing = {
|
||
(item.get("image_url") or {}).get("url")
|
||
for item in content
|
||
if isinstance(item, dict) and item.get("type") == "image_url"
|
||
}
|
||
content.extend(
|
||
{"type": "image_url", "image_url": {"url": url}}
|
||
for url in image_urls
|
||
if url not in existing
|
||
)
|
||
else:
|
||
content = [{"type": "text", "text": f"{raw or ''}\n\n{note}".strip()}]
|
||
content.extend({"type": "image_url", "image_url": {"url": url}} for url in image_urls)
|
||
last["content"] = content
|
||
out[index] = last
|
||
return out
|
||
|
||
|
||
def build_system_prompt(context: AgentContext, *, allow_plan: bool = True, has_context: bool = False) -> str:
|
||
conversation = context.conversation
|
||
params = conversation.params or {}
|
||
kind = "视频" if context.is_video else "图片"
|
||
lines = [
|
||
"你是影擎「全能创作」的创作 agent,帮电商商家做短视频和商品图。",
|
||
f"本次会话产出的是**{kind}**,这一点在整个会话里不会改变 —— 用户要另一种就请他新开一个创作。",
|
||
"",
|
||
"【怎么说话】",
|
||
"- 说人话,像个懂创作、会一起把事做完的同事;自然、简短、有判断,不要写成客服话术或需求确认清单。",
|
||
"- 禁止用「好的」「收到」「明白了」「有需要再说」「我将为你」起手;这些句子没有信息量,也很像机器人。",
|
||
"- 不要逐字复述用户刚说过的话。需要承接时,只说你的判断或下一步,例如「这个方向能做,我先把反转落在商品登场上。」",
|
||
"- 一次只推进一步,但不要把能直接做的事停在寒暄、确认或客套话上。",
|
||
"- 缺信息时一次只问一个真正影响结果的问题,不要把对话做成问卷。",
|
||
"- 缺少商品、模特、角色、场景等素材时：**绝不要直接弹出大块素材选择卡**打断对话。像懂创作、懂电商的专业伙伴一样先自然询问用户想推什么商品/用哪个角色，询问是否需要把商品列表发给他选，同时说明也可以直接输入商品名或由你推荐。",
|
||
"- 只有当用户明确说要发列表（如「发给我」「发列表」「给我看看」「我来选」）时，才展示可视化卡片。",
|
||
"- 用户直接打字输入商品名时，直接采纳该商品并继续推进创作方案，不要强迫用户去卡片里点选。",
|
||
"- 调性、受众、文案、时长等其他信息才用聊天追问,问题末尾顺手告诉用户下一句怎么回。",
|
||
"- 用户刚回答过你的问题时,直接沿着答案继续;不要复述答案,也不要额外回一句「收到」。",
|
||
"- 用户选择暂不提供某项素材时,把它当成明确授权:按已有信息和合理默认继续。除非任务客观上无法完成,否则不要再次追问同一素材。",
|
||
"- 用户说「你来定」「你帮我选」「随便」「都行」时,就是授权你做专业判断;直接选合理方案继续,不要把选择题再抛回去。",
|
||
"- 禁止问「要不要继续」「要不要生成」「是否开始创作」这类流程问题。用户已经提出创作需求且信息够了,就直接写策略/方案;真正扣积分前另有确认卡。",
|
||
"- 用户打招呼或闲聊(hi / 你好 / 在吗 / 你在干什么 / 嗯 / 好的 / ok):"
|
||
" **禁止**调用 write_strategy、write_plan、generate_image,不要「整理方案」或直接开写脚本。",
|
||
"- 会话里**还没有**商品/方向时:自然地告诉用户可以直接丢一句想法,别硬推销,也别用客服式结束语。",
|
||
"- 会话里**已有**商品或方向时:针对用户这句话本身自然回应;别用「要现在生成还是先调细节」把工作又抛回给用户。",
|
||
"- 没有明确创作指令时不要自己写策略卡/方案卡,也不要主动追问流程确认;等用户给出具体想法或修改。",
|
||
"",
|
||
"【什么时候反问】",
|
||
"- 只有信息**确实缺失且无法合理推断**时才调用 ask_user;能自己定的就自己定。",
|
||
"- ask_user 一次只问一件事。type=asset 会以类Agent自然追问轻量询问是否发送列表或由你推荐；其他类型显示普通聊天问题，让用户直接输入。",
|
||
"- 商品是谁、给谁看、什么调性 —— 这些缺了会直接影响成片,值得问。",
|
||
"- 让用户选商品/角色/模特/场景时,ask_user 必须用 type=asset 并填 asset_types。",
|
||
"- 用户说「改商品」「换角色」却没点名是哪个:立刻 ask_user 启动素材确认,不要强行出大卡片。",
|
||
"- 用户说改时长/模型/比例/分辨率但没给新值:立刻 ask_user,type=single 提供可识别的候选值;聊天里只显示问题,用户直接输入。回答后旧方案作废,必须按新参数重新 write_plan。",
|
||
"- 光线、构图、镜头这些专业判断是你的活,不要反过来问用户。",
|
||
]
|
||
if context.is_video:
|
||
lines.extend(["", _OMNI_VIDEO_PROMPT_RULES.strip()])
|
||
# 按会话时长给出口播字数锚点(与专业创作 narration_limit 同口径)
|
||
try:
|
||
dur = video_duration(params)
|
||
except Exception: # noqa: BLE001
|
||
dur = SMART_DURATION
|
||
lo = max(1, int(dur * 5.0))
|
||
hi = max(lo, min(85, int(dur * 5.7)))
|
||
lines.append(f"- 当前按约 {dur} 秒出片,口播建议 {lo}–{hi} 字;write_plan 的 voice_chars 填这个区间。")
|
||
lines.append("- 只有用户明确要做片、出方案、改方案、换卖点/剧情时才调用 write_strategy / write_plan;闲聊与打招呼绝对不要。真要写方案时必须先 write_strategy 再 write_plan,video_prompt 按上面的秒级分镜规范写满,不要只给大纲,也禁止只回「好的,有需要再说」。")
|
||
else:
|
||
lines.extend([
|
||
"",
|
||
"【出图】",
|
||
"- 决定出图时必须调用 generate_image,不要只口头说「我这就出图」。",
|
||
"- 一次用户消息只出一轮;张数用会话已定参数,不要自己加张。",
|
||
])
|
||
if not allow_plan:
|
||
lines.extend([
|
||
"",
|
||
"【本轮闸门】",
|
||
"- 用户本轮没有明确说「去做/出方案」。没有 write_strategy / write_plan / generate_image。",
|
||
"- **禁止**本轮直接写出策略卡或方案卡。",
|
||
])
|
||
if has_context:
|
||
lines.extend([
|
||
"- 会话已有素材或方向:针对用户本轮实际说的话自然回应;不要问要不要继续、要不要生成。",
|
||
"- 禁止只回「在呢」「有需要随时招呼」「收到」这种空话。",
|
||
])
|
||
else:
|
||
lines.extend([
|
||
"- 会话还没有创作进度:短回一句,告诉用户直接说想做什么即可;不要用客服式结束语,也不要硬推销出片。",
|
||
])
|
||
if params:
|
||
meta = "、".join(f"{k}:{v}" for k, v in params.items() if v)
|
||
if meta:
|
||
lines.append(f"\n【会话已定参数】{meta}(出片按这套;用户改动后必须按新值重写方案)")
|
||
if conversation.preset:
|
||
# 只给名字模型只能靠猜;把这个预设的拍法约束一起给它
|
||
guidance = preset_guidance(conversation.preset)
|
||
lines.append(f"\n【创作预设】{conversation.preset}")
|
||
if guidance:
|
||
lines.append(guidance)
|
||
lines.append("用户选了这个预设,就按它的拍法来;要偏离得先问过用户。")
|
||
|
||
resolved = resolve_refs(context.team, conversation.pinned_refs or [])
|
||
if resolved.facts:
|
||
lines.append("\n【本次会话已锁定的素材事实】")
|
||
lines.append(resolved.facts_text)
|
||
lines.append(
|
||
"以上素材的参考图会附给你看,出片时也会自动锁人锁物。"
|
||
"人物性别、年龄段、发型、服装、商品颜色外形必须以图为准;"
|
||
"图上看不清或没附图时,必须问用户,禁止凭文件名猜测男女。"
|
||
)
|
||
memory = conversation.memory or {}
|
||
if memory.get("summary"):
|
||
lines.append(f"\n【前情提要】{memory['summary']}")
|
||
artifacts = memory.get("artifacts") or []
|
||
if artifacts:
|
||
recent = artifacts[-3:]
|
||
lines.append("\n【本会话已生成过】")
|
||
for index, item in enumerate(recent, 1):
|
||
lines.append(f"{index}. {item.get('prompt', '')[:120]}")
|
||
lines.append(
|
||
"用户说「改成…」「换成…」时,是要在**最后一次生成**的基础上重新生成一版,"
|
||
"把改动合进完整 prompt 再调生成工具 —— 不要只写改动部分。"
|
||
)
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _answer_label(field: dict, raw) -> str:
|
||
"""追问卡的内部值翻成人能读的标签,避免把 UUID / gate key 喂回模型。"""
|
||
values = raw if isinstance(raw, list) else [raw]
|
||
options = {
|
||
str(item.get("value")): str(item.get("label") or item.get("value") or "")
|
||
for item in (field.get("options") or [])
|
||
if isinstance(item, dict)
|
||
}
|
||
return "、".join(options.get(str(value), str(value)) for value in values if value not in (None, ""))
|
||
|
||
|
||
def build_messages(context: AgentContext, *, allow_plan: bool = True, has_context: bool = False) -> list[dict]:
|
||
"""会话历史 → 模型消息。只喂对模型有意义的:文字、追问和它的答案、生成过什么。
|
||
策略卡/方案卡这类结构化产物压成一句话,原样塞 JSON 只会挤爆上下文。
|
||
|
||
长会话只喂最近 KEEP_RECENT_MESSAGES 条原文,更早的靠 system 里的【前情提要】
|
||
——摘要在 compress_memory() 里生成,不在这里现算。
|
||
"""
|
||
messages = [{"role": "system", "content": build_system_prompt(context, allow_plan=allow_plan, has_context=has_context)}]
|
||
history = list(context.conversation.messages.all())
|
||
if len(history) > COMPRESS_AFTER_MESSAGES:
|
||
history = history[-KEEP_RECENT_MESSAGES:]
|
||
for message in history:
|
||
if message.kind == CreationMessage.Kind.TEXT:
|
||
if message.text.strip():
|
||
messages.append({"role": message.role, "content": message.text})
|
||
elif message.kind == CreationMessage.Kind.ELICIT:
|
||
payload = message.payload or {}
|
||
answers = payload.get("answers") or {}
|
||
fields = [item for item in (payload.get("fields") or []) if isinstance(item, dict)]
|
||
field_by_key = {str(item.get("key") or ""): item for item in fields}
|
||
if payload.get("interaction") == "chat" or (
|
||
payload.get("interaction") == "asset_picker"
|
||
and payload.get("answered_via") == "chat"
|
||
):
|
||
question = message.text.strip() or str((fields[0] if fields else {}).get("label") or "").strip()
|
||
if question:
|
||
messages.append({"role": "assistant", "content": question})
|
||
# 聊天式回答本身就是下一条 TEXT user 消息,这里不再合成一条伪造答案。
|
||
continue
|
||
if answers:
|
||
if payload.get("phase") == "gate":
|
||
choice = str(answers.get("_asset_gate") or "")
|
||
if choice == "skip":
|
||
answer_text = "(用户选择暂不添加这项素材,要求按现有信息继续原任务)"
|
||
elif choice == "auto":
|
||
answer_text = "(用户希望你帮忙挑选/决定素材,按合理推荐继续推进)"
|
||
elif choice == "send":
|
||
answer_text = "(用户希望打开素材列表继续选择)"
|
||
else:
|
||
answer_text = f"(用户指定了素材：{choice})"
|
||
else:
|
||
joined = "；".join(
|
||
f"{field_by_key.get(str(key), {}).get('label') or key}："
|
||
f"{_answer_label(field_by_key.get(str(key), {}), value)}"
|
||
for key, value in answers.items()
|
||
)
|
||
answer_text = f"(用户完成了刚才的选择：{joined})"
|
||
messages.append({"role": "assistant", "content": "(我请用户补充了一项会影响创作的信息)"})
|
||
messages.append({"role": "user", "content": answer_text})
|
||
else:
|
||
messages.append({"role": "assistant", "content": "(我正在等用户回答刚才的问题)"})
|
||
elif message.kind in (CreationMessage.Kind.GENERATING, CreationMessage.Kind.RESULT):
|
||
prompt = (message.payload or {}).get("prompt") or ""
|
||
messages.append({"role": "assistant", "content": f"(我生成了一版,prompt:{prompt[:200]})"})
|
||
elif message.kind == CreationMessage.Kind.ERROR:
|
||
messages.append({"role": "assistant", "content": f"(上一次生成失败:{message.text})"})
|
||
if _creation_model_sees_images(context.model_config):
|
||
messages = _attach_ref_images(
|
||
messages,
|
||
_ref_image_urls(context.team, context.conversation.pinned_refs or []),
|
||
)
|
||
return messages
|
||
|
||
|
||
# ---------------------------------------------------------------- 流式循环
|
||
|
||
|
||
def _sse(event: dict) -> str:
|
||
"""一帧 SSE。**必须用 DjangoJSONEncoder** —— 消息里带 UUID(task 外键)和
|
||
datetime(created_at),标准 json.dumps 直接抛 TypeError,整条流当场断掉。"""
|
||
return f"data: {json.dumps(event, ensure_ascii=False, cls=DjangoJSONEncoder)}\n\n"
|
||
|
||
|
||
def _merge_tool_call_deltas(buffer: dict, deltas: list) -> None:
|
||
"""OpenAI 流式把一次 tool_call 的 arguments 拆成很多片,按 index 拼回来。
|
||
name 只在第一片出现,arguments 要逐片累加 —— 直接覆盖会只剩最后一个字符。"""
|
||
for delta in deltas or []:
|
||
if not isinstance(delta, dict):
|
||
continue
|
||
index = delta.get("index", 0)
|
||
slot = buffer.setdefault(index, {"name": "", "arguments": ""})
|
||
function = delta.get("function") or {}
|
||
if function.get("name"):
|
||
slot["name"] = function["name"]
|
||
if function.get("arguments"):
|
||
slot["arguments"] += function["arguments"]
|
||
|
||
|
||
def _parse_arguments(raw: str) -> dict:
|
||
"""解析工具 arguments。模型偶发包 markdown 围栏或夹杂前后缀,尽量救回 JSON。"""
|
||
text = (raw or "").strip()
|
||
if not text:
|
||
return {}
|
||
if text.startswith("```"):
|
||
text = text.strip("`")
|
||
if text.lower().startswith("json"):
|
||
text = text[4:].lstrip()
|
||
text = text.strip()
|
||
try:
|
||
parsed = json.loads(text)
|
||
except ValueError:
|
||
start, end = text.find("{"), text.rfind("}")
|
||
if start < 0 or end <= start:
|
||
return {}
|
||
try:
|
||
parsed = json.loads(text[start : end + 1])
|
||
except ValueError:
|
||
return {}
|
||
return parsed if isinstance(parsed, dict) else {}
|
||
|
||
|
||
def _pick_str(args: dict, *keys: str) -> str:
|
||
"""按候选键取非空字符串;兼容中文别名与嵌套一层 dict。"""
|
||
for key in keys:
|
||
value = args.get(key)
|
||
if isinstance(value, dict):
|
||
# 偶发 {"text": "..."} / {"value": "..."}
|
||
for nested in ("text", "value", "content", "desc", "description"):
|
||
inner = value.get(nested)
|
||
if isinstance(inner, str) and inner.strip():
|
||
return inner.strip()
|
||
continue
|
||
if value is None:
|
||
continue
|
||
text = str(value).strip()
|
||
if text:
|
||
return text
|
||
return ""
|
||
|
||
|
||
def _coerce_points(raw) -> list[str]:
|
||
"""points 规整成最多 3 条非空文案。兼容纯字符串、对象数组、dict。"""
|
||
if raw is None:
|
||
return []
|
||
items: list = []
|
||
if isinstance(raw, str):
|
||
text = raw.strip()
|
||
if not text:
|
||
return []
|
||
# 中文分号/换行拆条
|
||
for part in text.replace("\r", "\n").replace("；", "\n").split("\n"):
|
||
part = part.strip(" ·•-、，,")
|
||
if part:
|
||
items.append(part)
|
||
elif isinstance(raw, dict):
|
||
# {"P0": "...", "P1": "..."} 或 {"0": "..."}
|
||
for key in sorted(raw.keys(), key=lambda k: str(k)):
|
||
val = raw[key]
|
||
if isinstance(val, dict):
|
||
text = _pick_str(val, "text", "value", "content", "desc", "point", "label")
|
||
else:
|
||
text = str(val or "").strip()
|
||
if text:
|
||
items.append(text)
|
||
elif isinstance(raw, (list, tuple)):
|
||
for item in raw:
|
||
if isinstance(item, dict):
|
||
text = _pick_str(item, "text", "value", "content", "desc", "point", "label")
|
||
else:
|
||
text = str(item or "").strip()
|
||
if text:
|
||
items.append(text)
|
||
else:
|
||
text = str(raw).strip()
|
||
if text:
|
||
items.append(text)
|
||
# 过滤单字符噪声(字符串被误当成 list 迭代时的残留)
|
||
cleaned = [p for p in items if len(p) > 1]
|
||
return cleaned[:3]
|
||
|
||
|
||
def _coerce_voice_chars(raw, fallback: list[int] | None = None) -> list[int]:
|
||
"""voice_chars 必须是 [下限, 上限];模型常误塞语气文案或单个整数。"""
|
||
if isinstance(raw, (list, tuple)) and len(raw) >= 2:
|
||
try:
|
||
lo, hi = int(raw[0]), int(raw[1])
|
||
if lo > 0 and hi >= lo:
|
||
return [lo, hi]
|
||
except (TypeError, ValueError):
|
||
pass
|
||
if isinstance(raw, (int, float)) and int(raw) > 0:
|
||
n = int(raw)
|
||
return [max(1, n - 5), n + 5]
|
||
return list(fallback or [])
|
||
|
||
|
||
def _coerce_timeline(raw) -> list[dict]:
|
||
items: list[dict] = []
|
||
for item in (raw or []) if isinstance(raw, list) else []:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
try:
|
||
start = float(item.get("start"))
|
||
end = float(item.get("end"))
|
||
except (TypeError, ValueError):
|
||
continue
|
||
stage = str(item.get("stage") or "").strip()
|
||
if not stage:
|
||
continue
|
||
entry = {"start": start, "end": end, "stage": stage}
|
||
desc = str(item.get("desc") or item.get("description") or "").strip()
|
||
if desc:
|
||
entry["desc"] = desc
|
||
items.append(entry)
|
||
return items
|
||
|
||
|
||
def _default_plan_matrix(usp: str, points: list[str]) -> dict:
|
||
"""设计稿里的「卖点覆盖矩阵」:有 USP/支撑点就自动铺一版,避免方案卡干瘪。"""
|
||
rows = [{"point": "主打卖点 USP", "hits": [1, 3]}]
|
||
labels = ["体验卖点 P0", "视觉卖点 P0", "转化卖点 P0"]
|
||
for index, point in enumerate(points[:3]):
|
||
label = labels[index] if index < len(labels) else f"支撑卖点 P{index}"
|
||
# 点名用文案前缀,hits 错落分布到 4 镜
|
||
hit = (index % 4) + 1
|
||
rows.append({"point": label if not point else f"{label}", "hits": [hit]})
|
||
return {"shots": 4, "rows": rows}
|
||
|
||
|
||
def _coerce_strategy_args(args: dict) -> dict:
|
||
return {
|
||
"target": _pick_str(
|
||
args, "target", "audience", "who", "给谁看", "目标人群", "人群",
|
||
),
|
||
"trust": _pick_str(
|
||
args, "trust", "credibility", "为什么相信", "信任", "可信度",
|
||
),
|
||
"belief": _pick_str(
|
||
args, "belief", "希望相信", "想让他信什么", "认知", "takeaway",
|
||
),
|
||
"direction": _pick_str(
|
||
args, "direction", "创作方向", "方向", "style", "路线",
|
||
),
|
||
}
|
||
|
||
|
||
def _coerce_plan_card_args(args: dict) -> dict:
|
||
usp = _pick_str(args, "usp", "主打卖点", "卖点", "core_usp", "main_point")
|
||
points = _coerce_points(
|
||
args.get("points")
|
||
if args.get("points") is not None
|
||
else args.get("支撑点") or args.get("supports") or args.get("selling_points")
|
||
)
|
||
# 兼容 point1/point2/point3 展开写法(设计稿 blueprint)
|
||
if not points:
|
||
for key in ("point1", "point2", "point3", "P0", "P1", "P2"):
|
||
text = _pick_str(args, key)
|
||
if text:
|
||
points.append(text)
|
||
points = points[:3]
|
||
timeline = _coerce_timeline(args.get("timeline") or args.get("时间轴"))
|
||
matrix = args.get("matrix")
|
||
if not isinstance(matrix, dict) or not matrix.get("rows"):
|
||
matrix = _default_plan_matrix(usp, points) if (usp or points) else {}
|
||
return {
|
||
"usp": usp,
|
||
"points": points,
|
||
"timeline": timeline,
|
||
"matrix": matrix,
|
||
"voice_chars": args.get("voice_chars"),
|
||
}
|
||
|
||
|
||
def iter_creation_agent_events(
|
||
*,
|
||
conversation: CreationConversation,
|
||
user,
|
||
text: str,
|
||
refs: list[dict] | None = None,
|
||
model_config: ModelConfig | None = None,
|
||
record_user_message: bool = True,
|
||
force_creative_turn: bool = False,
|
||
continuation_instruction: str = "",
|
||
) -> Iterator[dict]:
|
||
"""一条用户消息 → 事件 dict 流(message/delta/tool/done/error)。消息已落库。"""
|
||
refs = refs or []
|
||
fast_greeting = record_user_message and is_greeting(text)
|
||
# 打招呼不依赖模型，模型未配置也能即时回应；其他消息保持原有的先校验模型行为。
|
||
if not fast_greeting:
|
||
model_config = get_creation_chat_model(model_config)
|
||
if model_config is None:
|
||
yield {"type": "error", "detail": "没有可用的文本模型,请先在模型库配置"}
|
||
return
|
||
context = AgentContext(conversation=conversation, user=user, model_config=model_config)
|
||
|
||
try:
|
||
user_message = None
|
||
with transaction.atomic():
|
||
pin_refs(conversation, refs)
|
||
if record_user_message:
|
||
user_message = append_message(conversation, role="user", text=text, refs=refs)
|
||
if user_message is not None:
|
||
yield {"type": "message", "message": _message_payload(user_message)}
|
||
# 单纯打招呼无论有没有参考素材、有没有既有上下文，都不值得等模型。
|
||
# 素材先收下，等用户说清用途再分析，避免「你好」卡住还冒出一大段建议。
|
||
if fast_greeting:
|
||
reply = append_message(
|
||
conversation,
|
||
role="assistant",
|
||
text=(
|
||
"你好，素材我收到了。想拿它做什么，直接说就行。"
|
||
if refs else "你好。想做什么，直接说就行。"
|
||
),
|
||
)
|
||
yield {"type": "message", "message": _message_payload(reply)}
|
||
yield {"type": "done"}
|
||
return
|
||
|
||
# 空会话里的简短应答：短回一句就够。已有商品/方向时走模型，
|
||
# 针对用户实际说的话自然回应，但不给方案工具。
|
||
has_context = session_has_creative_context(conversation)
|
||
if record_user_message and is_pure_chitchat(text) and not refs and not has_context:
|
||
reply = append_message(
|
||
conversation,
|
||
role="assistant",
|
||
text="我在。想做什么，直接丢一句想法给我就行。",
|
||
)
|
||
yield {"type": "message", "message": _message_payload(reply)}
|
||
yield {"type": "done"}
|
||
return
|
||
|
||
model_config = _prefer_vision_text_model(model_config, conversation.team, conversation.pinned_refs or [])
|
||
context.model_config = model_config
|
||
|
||
resolved = resolve_refs(context.team, refs)
|
||
if resolved.missing:
|
||
names = "、".join(r.get("name") or "某个素材" for r in resolved.missing)
|
||
note = append_message(
|
||
conversation, role="assistant",
|
||
text=f"有几个引用的素材已经找不到了({names}),我先按其余信息继续。",
|
||
)
|
||
yield {"type": "message", "message": _message_payload(note)}
|
||
|
||
# 压缩放在**建消息之前**:摘要要进这一轮的 system 提示词才有意义。
|
||
# 用户消息已经先回显了,所以这一小段等待不会看起来像卡住。
|
||
compress_memory(context)
|
||
|
||
allow_plan = (
|
||
force_creative_turn
|
||
or has_creative_intent(text, refs)
|
||
or (has_context and is_continue_intent(text))
|
||
)
|
||
provider = build_provider(model_config)
|
||
messages = build_messages(context, allow_plan=allow_plan, has_context=has_context)
|
||
if continuation_instruction.strip():
|
||
# 卡片答案已经在历史里,这里仅给本轮一个不落库的执行指令。这样既不会多出
|
||
# 一条伪造的用户气泡,也不会让模型把「跳过素材」误判成闲聊后停住。
|
||
messages.append({"role": "user", "content": continuation_instruction.strip()})
|
||
tools = tool_schemas(context, allow_plan=allow_plan)
|
||
|
||
from .creation import is_agent_cancel_requested
|
||
|
||
for _round in range(MAX_TOOL_ROUNDS):
|
||
if is_agent_cancel_requested(conversation.id):
|
||
# 用户终止:干净收束,不落 ERROR,已落库消息保留
|
||
yield {"type": "cancelled"}
|
||
return
|
||
text_buffer: list[str] = []
|
||
tool_buffer: dict = {}
|
||
for chunk in provider.chat_completion_stream(
|
||
model=model_config.name,
|
||
messages=messages,
|
||
endpoint=model_config.endpoint or "chat/completions",
|
||
extra_body={"tools": tools},
|
||
):
|
||
kind = chunk.get("type")
|
||
if kind == "reasoning":
|
||
yield {"type": "reasoning", "text": chunk.get("text", "")}
|
||
elif kind == "delta":
|
||
piece = chunk.get("text", "")
|
||
text_buffer.append(piece)
|
||
yield {"type": "delta", "text": piece}
|
||
elif kind == "tool_call":
|
||
_merge_tool_call_deltas(tool_buffer, chunk.get("tool_calls"))
|
||
|
||
said = "".join(text_buffer).strip()
|
||
calls = [tool_buffer[i] for i in sorted(tool_buffer) if tool_buffer[i].get("name")]
|
||
fallback_fields = None
|
||
allow_pick = False
|
||
if not calls:
|
||
requested_card = requested_asset_card_from_context(conversation, text)
|
||
wanted_pick = wanted_asset_pick(text, refs)
|
||
pick = requested_card or wanted_pick
|
||
param_keys = wanted_param_keys(text, is_video=context.is_video)
|
||
if pick:
|
||
fallback_fields = [{
|
||
"key": pick,
|
||
"label": _ASSET_PICK_LABEL.get(pick, _ASSET_CARD_LABELS.get(pick, "请选择素材")),
|
||
"type": "asset",
|
||
"required": True,
|
||
"asset_types": [pick],
|
||
}]
|
||
allow_pick = bool(requested_card)
|
||
elif param_keys:
|
||
fallback_fields = session_param_fields(param_keys, context.is_video)
|
||
elif allow_plan and not said:
|
||
# 部分推理模型会只给 reasoning_content 后结束本轮。不能让用户
|
||
# 只看到自己那条消息；没有商品上下文时，收敛成一条自然追问。
|
||
has_product = any(
|
||
isinstance(ref, dict) and ref.get("type") == "product"
|
||
for ref in (conversation.pinned_refs or [])
|
||
)
|
||
if not has_product:
|
||
fallback_fields = [{
|
||
"key": "product",
|
||
"label": "这条想推哪款商品？",
|
||
"type": "asset",
|
||
"required": True,
|
||
"asset_types": ["product"],
|
||
}]
|
||
|
||
# ask_user 自己会落一条可追踪的聊天问题。模型同时吐出的过渡文案不再
|
||
# 另存一条,否则界面会连续出现两遍几乎相同的问题。
|
||
asks_user = bool(fallback_fields) or any(call.get("name") == "ask_user" for call in calls)
|
||
if said and not asks_user:
|
||
bubble = append_message(conversation, role="assistant", text=said)
|
||
yield {"type": "message", "message": _message_payload(bubble)}
|
||
|
||
if not calls:
|
||
if fallback_fields:
|
||
result, _stop = _dispatch_tool(
|
||
context, "ask_user", {"fields": fallback_fields}, allow_pick=allow_pick
|
||
)
|
||
for event in result.get("_events", []):
|
||
yield event
|
||
break
|
||
|
||
messages.append({
|
||
"role": "assistant",
|
||
"content": said or None,
|
||
"tool_calls": [
|
||
{"id": f"call_{i}", "type": "function",
|
||
"function": {"name": c["name"], "arguments": c["arguments"]}}
|
||
for i, c in enumerate(calls)
|
||
],
|
||
})
|
||
|
||
stop = False
|
||
for index, call in enumerate(calls):
|
||
if is_agent_cancel_requested(conversation.id):
|
||
yield {"type": "cancelled"}
|
||
return
|
||
name = call["name"]
|
||
args = _parse_arguments(call["arguments"])
|
||
yield {"type": "tool", "id": name, "label": _TOOL_LABELS.get(name, name), "status": "running"}
|
||
try:
|
||
result, stop_after = _dispatch_tool(context, name, args)
|
||
except AgentError as exc:
|
||
yield {"type": "tool", "id": name, "label": _TOOL_LABELS.get(name, name), "status": "error"}
|
||
failure = append_message(
|
||
conversation, role="assistant",
|
||
kind=CreationMessage.Kind.ERROR, text=str(exc),
|
||
)
|
||
yield {"type": "message", "message": _message_payload(failure)}
|
||
yield {"type": "done"}
|
||
return
|
||
yield {"type": "tool", "id": name, "label": _TOOL_LABELS.get(name, name), "status": "done"}
|
||
for event in result.get("_events", []):
|
||
yield event
|
||
messages.append({
|
||
"role": "tool",
|
||
"tool_call_id": f"call_{index}",
|
||
"content": json.dumps(result.get("payload", {}), ensure_ascii=False),
|
||
})
|
||
stop = stop or stop_after
|
||
if stop:
|
||
break
|
||
|
||
yield {"type": "done"}
|
||
except Exception as exc: # noqa: BLE001 — SSE 里任何未捕获异常都会变成前端「白屏卡死」
|
||
logger.exception("creation agent stream failed: %s", exc)
|
||
yield {"type": "error", "detail": "生成过程出错了,请再试一次"}
|
||
|
||
|
||
|
||
def stream_creation_agent(
|
||
*,
|
||
conversation: CreationConversation,
|
||
user,
|
||
text: str,
|
||
refs: list[dict] | None = None,
|
||
model_config: ModelConfig | None = None,
|
||
record_user_message: bool = True,
|
||
force_creative_turn: bool = False,
|
||
continuation_instruction: str = "",
|
||
) -> Iterator[str]:
|
||
"""兼容旧 SSE 消费方(单测 / 调试)。生产路径走 Celery + poll。"""
|
||
for event in iter_creation_agent_events(
|
||
conversation=conversation,
|
||
user=user,
|
||
text=text,
|
||
refs=refs,
|
||
model_config=model_config,
|
||
record_user_message=record_user_message,
|
||
force_creative_turn=force_creative_turn,
|
||
continuation_instruction=continuation_instruction,
|
||
):
|
||
yield _sse(event)
|
||
|
||
|
||
def run_creation_agent_turn(
|
||
*,
|
||
conversation_id: str,
|
||
user_id: str,
|
||
text: str = "",
|
||
refs: list | None = None,
|
||
model_config_id: str | None = None,
|
||
record_user_message: bool = True,
|
||
force_creative_turn: bool = False,
|
||
continuation_instruction: str = "",
|
||
) -> str:
|
||
"""Celery worker 入口:跑完一轮 tool loop,消息落库;更新 agent_status;释放团队锁。
|
||
|
||
不复用 CreationMessage.kind=generating / AITask —— 那些是确认后出片/出图用的。
|
||
"""
|
||
from apps.accounts.models import User
|
||
|
||
from .creation import finish_agent_planning
|
||
|
||
conversation = (
|
||
CreationConversation.objects.select_related("team", "created_by")
|
||
.filter(id=conversation_id)
|
||
.first()
|
||
)
|
||
if conversation is None:
|
||
return conversation_id
|
||
user = User.objects.filter(id=user_id).first() or conversation.created_by
|
||
model_config = None
|
||
if model_config_id:
|
||
model_config = (
|
||
ModelConfig.objects.select_related("provider")
|
||
.filter(id=model_config_id, capability=ModelConfig.Capability.TEXT, status=ModelConfig.Status.ACTIVE)
|
||
.first()
|
||
)
|
||
|
||
awaiting_user = False
|
||
user_cancelled = False
|
||
try:
|
||
for event in iter_creation_agent_events(
|
||
conversation=conversation,
|
||
user=user,
|
||
text=text or "",
|
||
refs=refs or [],
|
||
model_config=model_config,
|
||
record_user_message=record_user_message,
|
||
force_creative_turn=force_creative_turn,
|
||
continuation_instruction=continuation_instruction or "",
|
||
):
|
||
if not isinstance(event, dict):
|
||
continue
|
||
if event.get("type") == "cancelled":
|
||
user_cancelled = True
|
||
awaiting_user = False
|
||
break
|
||
if event.get("type") == "message":
|
||
message = event.get("message") or {}
|
||
kind = message.get("kind") if isinstance(message, dict) else None
|
||
if kind in (CreationMessage.Kind.ELICIT, CreationMessage.Kind.CONFIRM):
|
||
awaiting_user = True
|
||
elif event.get("type") == "error":
|
||
detail = str(event.get("detail") or "生成过程出错了,请再试一次")
|
||
# 循环内多数错误已落 ERROR 消息;这里兜底一条,避免前端只看到卡在 planning
|
||
last = conversation.messages.order_by("-seq").first()
|
||
if last is None or last.kind != CreationMessage.Kind.ERROR:
|
||
append_message(
|
||
conversation,
|
||
role="assistant",
|
||
kind=CreationMessage.Kind.ERROR,
|
||
text=detail,
|
||
)
|
||
except Exception as exc: # noqa: BLE001 — worker 不能把异常冒成无限 planning
|
||
logger.exception("creation agent turn failed: %s", exc)
|
||
try:
|
||
append_message(
|
||
conversation,
|
||
role="assistant",
|
||
kind=CreationMessage.Kind.ERROR,
|
||
text="生成过程出错了,请再试一次",
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
logger.exception("creation agent turn: failed to append error message")
|
||
awaiting_user = False
|
||
finally:
|
||
try:
|
||
conversation.refresh_from_db(fields=["agent_status", "team_id"])
|
||
except Exception: # noqa: BLE001
|
||
pass
|
||
# 用户已终止时 API 侧多半已 idle+放锁;这里再收一次保证幂等,且不落入 awaiting_user
|
||
finish_agent_planning(
|
||
conversation,
|
||
awaiting_user=(awaiting_user and not user_cancelled),
|
||
)
|
||
return conversation_id
|
||
|
||
|
||
|
||
_TOOL_LABELS = {
|
||
"ask_user": "向你确认",
|
||
"search_library": "查找素材",
|
||
"generate_image": "生成图片",
|
||
"write_strategy": "梳理创作策略",
|
||
"write_plan": "编排视频方案",
|
||
}
|
||
|
||
|
||
_ASSET_CARD_LABELS = {
|
||
"product": "这条要展示哪款商品？",
|
||
"character": "这条想用哪个角色？",
|
||
"model": "这条想用哪位模特？",
|
||
"scene": "这条想放在哪个场景里？",
|
||
"asset": "这一步要使用哪项素材？",
|
||
}
|
||
|
||
_GATE_LABELS = {
|
||
"product": "这条想推哪款商品？可以直接告诉我商品名，或者需要我把商品库列表发给你选吗？",
|
||
"character": "这条想用哪个角色？可以直接告诉我角色名，或者需要我发角色列表给你选吗？",
|
||
"model": "这条想用哪位模特？可以直接告诉我，或者需要我把模特库发给你选吗？",
|
||
"scene": "这条想放在哪个场景里？可以直接告诉我，或者需要我把场景库发给你选吗？",
|
||
"asset": "这一步想用哪项素材？可以直接告诉我，或者需要我把素材列表发给你选吗？",
|
||
}
|
||
|
||
|
||
def _guided_elicit_text(field: dict) -> str:
|
||
"""追问既要问清一件事,也要让用户知道下一句怎么回。"""
|
||
label = str(field.get("label") or "这项你想怎么定？").strip()
|
||
if field.get("type") == "asset" or field.get("key") == "_asset_gate":
|
||
return label
|
||
if str(field.get("key") or "") in SESSION_PARAM_KEYS:
|
||
return label
|
||
return f"{label} 直接用一句话告诉我就行，不用整理成完整需求。"
|
||
|
||
|
||
|
||
def _elicit_payload_for_fields(
|
||
fields: list[dict], *, allow_pick: bool = False, is_video: bool = True
|
||
) -> dict:
|
||
"""素材问题默认出轻量对话闸门；用户明确要求发列表时才出 pick 选择卡。"""
|
||
field = dict(fields[0])
|
||
if field.get("type") == "asset":
|
||
asset_types = [item for item in (field.get("asset_types") or []) if item in _GATE_LABELS]
|
||
primary_type = asset_types[0] if asset_types else "product"
|
||
if allow_pick:
|
||
field["label"] = _ASSET_CARD_LABELS.get(primary_type, _ASSET_CARD_LABELS["asset"])
|
||
return {
|
||
"interaction": "asset_picker",
|
||
"phase": "pick",
|
||
"fields": [field],
|
||
"submitted": False,
|
||
"answers": {},
|
||
}
|
||
|
||
if primary_type == "product":
|
||
gate_label = (
|
||
"这条视频想推哪款商品？可以直接告诉我商品名，或者需要我把商品库列表发给你选吗？"
|
||
if is_video else
|
||
"这次想做哪款商品？可以直接告诉我商品名，或者需要我把商品库列表发给你选吗？"
|
||
)
|
||
else:
|
||
gate_label = _GATE_LABELS.get(primary_type, _GATE_LABELS["asset"])
|
||
|
||
gate_field = {
|
||
"key": "_asset_gate",
|
||
"label": gate_label,
|
||
"type": "text",
|
||
"required": False,
|
||
}
|
||
return {
|
||
# 这是 Agent 的一句自然追问，不是让用户点选流程的卡片。
|
||
# 只有用户明确要求发列表后，才会生成上面 allow_pick 分支的商品卡。
|
||
"interaction": "chat",
|
||
"phase": "gate",
|
||
"fields": [gate_field],
|
||
"pending_fields": [field],
|
||
"submitted": False,
|
||
"answers": {},
|
||
}
|
||
return {
|
||
"interaction": "chat",
|
||
"fields": [field],
|
||
"submitted": False,
|
||
"answers": {},
|
||
}
|
||
|
||
|
||
def _dispatch_tool(
|
||
context: AgentContext, name: str, args: dict, *, allow_pick: bool = False
|
||
) -> tuple[dict, bool]:
|
||
"""执行一个工具。返回 (结果, 是否中断循环)。
|
||
|
||
结果里的 `_events` 会原样转发给前端,`payload` 回喂给模型。
|
||
"""
|
||
if name == "ask_user":
|
||
fields = _coerce_fields(args.get("fields"))
|
||
if not fields:
|
||
return {"payload": {"error": "fields 不合法,请重新组织问题"}}, False
|
||
payload = _elicit_payload_for_fields(fields, allow_pick=allow_pick, is_video=context.is_video)
|
||
display_field = (payload.get("fields") or fields)[0]
|
||
message = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.ELICIT,
|
||
text=_guided_elicit_text(display_field),
|
||
payload=payload,
|
||
)
|
||
# 反问一旦发出就必须停,等人回答。继续跑等于自问自答。
|
||
return {
|
||
"payload": {"asked": True},
|
||
"_events": [{"type": "message", "message": _message_payload(message)}],
|
||
}, True
|
||
|
||
if name == "search_library":
|
||
return {"payload": _run_search_library(context, args)}, False
|
||
|
||
if name == "write_strategy":
|
||
strategy_payload = _coerce_strategy_args(args if isinstance(args, dict) else {})
|
||
# 空卡会落成「只有标签没有正文」——拒绝,让模型把四字段写满再调
|
||
if not all(strategy_payload.values()):
|
||
return {
|
||
"payload": {
|
||
"error": (
|
||
"创作策略卡四字段都不能为空:请填写具体的 target / trust / belief / direction"
|
||
"(给谁看、为什么信、希望他信什么、创作方向),不要留空。"
|
||
)
|
||
}
|
||
}, False
|
||
message = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.STRATEGY,
|
||
payload=strategy_payload,
|
||
)
|
||
# 策略卡只是「我理解对了吗」,不打断 —— 模型接着就该写方案
|
||
return {
|
||
"payload": {"written": True},
|
||
"_events": [{"type": "message", "message": _message_payload(message)}],
|
||
}, False
|
||
|
||
if name == "write_plan":
|
||
video_prompt = str(args.get("video_prompt") or "").strip()
|
||
if not video_prompt:
|
||
return {"payload": {"error": "video_prompt 不能为空,请把完整出片指令写进去"}}, False
|
||
card = _coerce_plan_card_args(args if isinstance(args, dict) else {})
|
||
if not card["usp"] or not card["points"]:
|
||
return {
|
||
"payload": {
|
||
"error": (
|
||
"方案卡缺正文:请填写非空的 usp(主打卖点)和 points(1–3 条核心支撑),"
|
||
"不要只写 video_prompt。卡片上要让用户看见卖点文案。"
|
||
)
|
||
}
|
||
}, False
|
||
duration = 15
|
||
try:
|
||
duration = int(float(str((context.conversation.params or {}).get("duration") or "15").replace("秒", "").strip() or "15"))
|
||
except (TypeError, ValueError):
|
||
duration = 15
|
||
lo = max(20, round(duration * 3.4))
|
||
hi = max(lo + 1, round(duration * 4))
|
||
plan_payload = {
|
||
"usp": card["usp"],
|
||
"points": card["points"],
|
||
"timeline": card["timeline"],
|
||
"matrix": card["matrix"],
|
||
"voice_chars": _coerce_voice_chars(card["voice_chars"], [lo, hi]),
|
||
"ref_count": len(context.conversation.pinned_refs or []),
|
||
}
|
||
events = []
|
||
plan = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.PLAN, payload=plan_payload,
|
||
)
|
||
events.append({"type": "message", "message": _message_payload(plan)})
|
||
|
||
prompt_file = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.PROMPT_FILE,
|
||
payload={"title": "视频生成Prompt.md", "body": video_prompt,
|
||
"ref_count": plan_payload["ref_count"]},
|
||
)
|
||
events.append({"type": "message", "message": _message_payload(prompt_file)})
|
||
|
||
credits = estimate_video_credits(context)
|
||
# video_prompt 存在确认卡里:用户点确认后直接照它出片,不再跑一轮模型
|
||
confirm = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.CONFIRM,
|
||
payload={
|
||
"kind": "video",
|
||
"label": "开始生成",
|
||
"estimated_credits": credits,
|
||
"video_prompt": video_prompt,
|
||
"submitted": False,
|
||
"params": snapshot_session_params(context.conversation),
|
||
"param_options": confirm_param_options(True),
|
||
},
|
||
)
|
||
events.append({"type": "message", "message": _message_payload(confirm)})
|
||
events.append({"type": "credits", "estimated": credits})
|
||
# 方案卡写着「仅需确认一次」—— 停在这里等人点,别自己往下出片
|
||
return {"payload": {"awaiting_confirmation": True}, "_events": events}, True
|
||
|
||
if name == "generate_image":
|
||
if context.generations_used >= MAX_BILLED_GENERATIONS:
|
||
# 一条用户消息只计费一次。模型想连出好几版时在这里挡住。
|
||
return {"payload": {"error": "本轮已经生成过一次了,请让用户看过再决定要不要改"}}, True
|
||
prompt = str(args.get("prompt") or "").strip()
|
||
if not prompt:
|
||
return {"payload": {"error": "生成失败:模型没有给出画面描述"}}, False
|
||
# 出图也走确认卡:用户先看当前模型/比例/张数,点了才提交。
|
||
credits = estimate_image_credits(context)
|
||
confirm = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.CONFIRM,
|
||
payload={
|
||
"kind": "image",
|
||
"label": "开始生成",
|
||
"estimated_credits": credits,
|
||
"prompt": prompt,
|
||
"submitted": False,
|
||
"params": snapshot_session_params(context.conversation),
|
||
"param_options": confirm_param_options(False),
|
||
},
|
||
)
|
||
events = [
|
||
{"type": "message", "message": _message_payload(confirm)},
|
||
{"type": "credits", "estimated": credits},
|
||
]
|
||
return {"payload": {"awaiting_confirmation": True}, "_events": events}, True
|
||
|
||
return {"payload": {"error": f"未知工具 {name}"}}, False
|
||
|
||
|
||
def _summarize_source(messages: list[CreationMessage]) -> str:
|
||
"""要压缩的那批消息 → 喂给模型的纯文本。只取有信息量的部分。"""
|
||
lines = []
|
||
for message in messages:
|
||
if message.kind == CreationMessage.Kind.TEXT and message.text.strip():
|
||
who = "用户" if message.role == "user" else "我"
|
||
lines.append(f"{who}:{message.text.strip()}")
|
||
elif message.kind == CreationMessage.Kind.ELICIT:
|
||
answers = (message.payload or {}).get("answers") or {}
|
||
if answers:
|
||
lines.append("用户确认:" + "；".join(f"{k}={v}" for k, v in answers.items()))
|
||
elif message.kind in (CreationMessage.Kind.GENERATING, CreationMessage.Kind.RESULT):
|
||
prompt = (message.payload or {}).get("prompt") or ""
|
||
if prompt:
|
||
lines.append(f"我生成了一版:{prompt[:120]}")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def compress_memory(context: AgentContext) -> None:
|
||
"""把早期消息压成一段摘要存进 conversation.memory.summary(契约 §5)。
|
||
|
||
只在消息数超过阈值时做;压缩失败**静默跳过** —— 摘要是锦上添花,
|
||
为它把整条对话打断不值得。压缩额外调一次模型,所以按 summarized_upto
|
||
记进度,同一批消息不重复压。
|
||
"""
|
||
conversation = context.conversation
|
||
history = list(conversation.messages.all())
|
||
if len(history) <= COMPRESS_AFTER_MESSAGES:
|
||
return
|
||
memory = dict(conversation.memory or {})
|
||
cutoff = len(history) - KEEP_RECENT_MESSAGES
|
||
if cutoff - int(memory.get("summarized_upto") or 0) < COMPRESS_MIN_BATCH:
|
||
return # 没压过的还不够一批,攒着 —— 每轮重压一次太贵
|
||
|
||
source = _summarize_source(history[:cutoff])
|
||
if not source.strip():
|
||
memory["summarized_upto"] = cutoff
|
||
conversation.memory = memory
|
||
conversation.save(update_fields=["memory", "updated_at"])
|
||
return
|
||
|
||
previous = memory.get("summary") or ""
|
||
instruction = (
|
||
"把下面这段创作对话压成一段中文摘要,150 字以内。"
|
||
"保留:用户明确提过的要求和否决过的方向、已确认的设定、生成过什么。"
|
||
"丢掉:寒暄、过程性的话。直接输出摘要正文,不要前言。\n\n"
|
||
+ (f"【已有摘要】{previous}\n\n" if previous else "")
|
||
+ f"【新增对话】\n{source}"
|
||
)
|
||
try:
|
||
provider = build_provider(context.model_config)
|
||
pieces = []
|
||
for chunk in provider.chat_completion_stream(
|
||
model=context.model_config.name,
|
||
messages=[{"role": "user", "content": instruction}],
|
||
endpoint=context.model_config.endpoint or "chat/completions",
|
||
):
|
||
if chunk.get("type") == "delta":
|
||
pieces.append(chunk.get("text", ""))
|
||
summary = "".join(pieces).strip()
|
||
except Exception: # noqa: BLE001 — 摘要失败不该打断对话
|
||
logger.warning("omni create: memory compression failed", exc_info=True)
|
||
return
|
||
if not summary:
|
||
return
|
||
memory["summary"] = summary[:400]
|
||
memory["summarized_upto"] = cutoff
|
||
conversation.memory = memory
|
||
conversation.save(update_fields=["memory", "updated_at"])
|
||
|
||
|
||
def _remember_artifact(conversation: CreationConversation, prompt: str, kind: str) -> None:
|
||
"""记进产物索引,让下一轮「把背景换成夜景」能定位到这一版(契约 §5)。"""
|
||
memory = dict(conversation.memory or {})
|
||
artifacts = list(memory.get("artifacts") or [])
|
||
artifacts.append({"prompt": prompt, "kind": kind})
|
||
memory["artifacts"] = artifacts[-10:]
|
||
conversation.memory = memory
|
||
conversation.save(update_fields=["memory", "updated_at"])
|
||
|
||
|
||
def _message_payload(message: CreationMessage) -> dict:
|
||
from .serializers import CreationMessageSerializer
|
||
|
||
return CreationMessageSerializer(message).data
|