1039 lines
46 KiB
Python
1039 lines
46 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
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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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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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def wanted_asset_pick(user_text: str, refs: list | None) -> str | None:
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"""用户说「改商品」却没 @ 时,应弹出素材卡,而不是让他自己填名字。"""
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if refs:
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return None
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text = user_text or ""
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for type_, pattern in _ASSET_PICK_PATTERNS:
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if pattern.search(text):
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return type_
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return None
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VIDEO_MODELS = ["Seedance 2.5", "Seedance 2.0", "Seedance 2.0 Fast", "Seedance 2.0 Mini"]
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IMAGE_MODELS = ["Seedream5.0", "YQ image2"]
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RATIOS = ["16:9", "9:16", "4:3", "3:4", "1:1"]
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RESOLUTIONS = ["480p", "720p", "1080p"]
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VIDEO_DURATIONS = ["智能时长", "4 秒", "5 秒", "6 秒", "8 秒", "10 秒", "12 秒", "15 秒", "30 秒"]
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IMAGE_COUNTS = ["1 张", "2 张", "4 张", "8 张"]
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SESSION_PARAM_KEYS = ("duration", "ratio", "resolution", "video_model", "count")
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_PARAM_TO_STORED = {"video_model": "model"}
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_PARAM_PICK_LABEL = {
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"duration": "改成多长？",
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"ratio": "改成什么比例？",
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"resolution": "改成什么分辨率？",
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"video_model": "换成哪个模型？",
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"count": "出几张？",
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}
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def _param_options(key: str, is_video: bool) -> list[str]:
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if key == "duration":
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return VIDEO_DURATIONS
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if key == "ratio":
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return RATIOS
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if key == "resolution":
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return RESOLUTIONS
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if key == "count":
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return IMAGE_COUNTS
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return VIDEO_MODELS if is_video else IMAGE_MODELS
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def session_param_fields(keys: list[str], is_video: bool) -> list[dict]:
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fields = []
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for key in keys[:3]:
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if key not in _PARAM_PICK_LABEL:
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continue
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options = [{"value": item, "label": item} for item in _param_options(key, is_video)]
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fields.append({
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"key": key,
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"label": _PARAM_PICK_LABEL[key],
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"type": "single",
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"required": True,
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"options": options,
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})
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return fields
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def wanted_param_keys(user_text: str, *, is_video: bool) -> list[str]:
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"""用户说「改时长」「换模型」时弹出参数卡。不和「改模特」抢。"""
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text = user_text or ""
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keys: list[str] = []
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if re.search(r"(改|换|修改|更换).{0,8}(时长|秒数)|改成\s*\d+\s*秒", text):
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keys.append("duration" if is_video else "count")
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if re.search(r"(改|换|修改|更换).{0,8}(比例|尺寸|画幅)", text):
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keys.append("ratio")
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if re.search(r"(改|换|修改|更换).{0,8}(分辨率|清晰度)", text):
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keys.append("resolution")
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if re.search(r"(改|换|修改|更换).{0,8}模型", text) and "模特" not in text:
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keys.append("video_model")
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if re.search(r"(改|换|修改|更换).{0,8}张数", text):
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keys.append("count")
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if not keys and re.search(r"(改|换|修改).{0,6}(参数|设置|规格)", text):
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keys = ["duration", "video_model", "ratio"] if is_video else ["count", "video_model", "ratio"]
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seen = set()
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out = []
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for key in keys:
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if key in seen:
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continue
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seen.add(key)
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out.append(key)
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return out
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def apply_session_params(conversation, fields, answers: dict) -> bool:
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"""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。"""
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current = dict(conversation.params or {})
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field_by_key = {str(item.get("key") or ""): item for item in (fields or []) if isinstance(item, dict)}
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changed = False
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for key, raw in (answers or {}).items():
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field = field_by_key.get(str(key)) or {}
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if field.get("type") == "asset":
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continue
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stored = _PARAM_TO_STORED.get(str(key), str(key))
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if stored not in {"model", "ratio", "resolution", "duration", "count"}:
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continue
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value = "、".join(raw) if isinstance(raw, list) else str(raw or "").strip()
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if not value or current.get(stored) == value:
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continue
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current[stored] = value
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changed = True
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if changed:
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conversation.params = current
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conversation.save(update_fields=["params", "updated_at"])
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return changed
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# ---------------------------------------------------------------- 工具 schema
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def tool_schemas(context: AgentContext) -> list[dict]:
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"""给模型看的工具清单。图片会话不暴露 generate_video,反之亦然 ——
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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。"""
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tools = [
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{
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"type": "function",
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"function": {
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"name": "ask_user",
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"description": (
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"缺少必要信息时反问用户。会在对话里渲染成可点选/可填写的卡片。"
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"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。"
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"用户要选/改/换商品、角色、模特、场景时**必须**调这个工具,"
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"type 用 asset 并填 asset_types;禁止只在气泡里问「换成哪个」。"
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"一次最多问 3 项。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"fields": {
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"type": "array",
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"maxItems": 3,
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"items": {
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"type": "object",
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"properties": {
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"key": {"type": "string", "description": "英文标识,如 product / duration"},
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"label": {"type": "string", "description": "问题原文,中文"},
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"type": {"type": "string", "enum": list(FIELD_TYPES)},
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"required": {"type": "boolean"},
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"options": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"value": {"type": "string"},
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"label": {"type": "string"},
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},
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"required": ["value", "label"],
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},
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},
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"asset_types": {
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"type": "array",
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"items": {"type": "string", "enum": list(TYPE_LABELS)},
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},
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"placeholder": {"type": "string"},
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},
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"required": ["key", "label", "type"],
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},
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}
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},
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"required": ["fields"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "search_library",
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"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。",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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"types": {"type": "array", "items": {"type": "string", "enum": list(TYPE_LABELS)}},
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},
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"required": ["query"],
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},
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},
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},
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]
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if context.is_video:
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tools.append({
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"type": "function",
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"function": {
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"name": "write_strategy",
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"description": (
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"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。"
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"在动手写方案之前调它一次,让用户先确认你理解对了。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"target": {"type": "string", "description": "这条视频给谁看,要具体到人群特征"},
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"trust": {"type": "string", "description": "用户为什么相信,靠什么建立可信度"},
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"belief": {"type": "string", "description": "希望用户看完相信什么"},
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"direction": {"type": "string", "description": "创作方向一句话,说清是什么类型的片"},
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},
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"required": ["target", "trust", "belief", "direction"],
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},
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},
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})
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tools.append({
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"type": "function",
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"function": {
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"name": "write_plan",
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"description": (
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"写「视频最终方案」卡并请用户确认。**这是出片前的最后一步**,调完会等用户点确认,"
|
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"确认后平台直接按 video_prompt 出片,你不会再有插话机会 —— 所以 video_prompt "
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"必须是完整、可独立执行的成片指令。先调 write_strategy 再调它。"
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),
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"parameters": {
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"type": "object",
|
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"properties": {
|
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"usp": {"type": "string", "description": "主打卖点,全片只讲这一个核心价值"},
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"points": {
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"type": "array", "maxItems": 3, "items": {"type": "string"},
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"description": "核心支撑卖点,最多 3 条",
|
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},
|
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"timeline": {
|
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"type": "array",
|
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"items": {
|
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"type": "object",
|
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"properties": {
|
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"start": {"type": "number"}, "end": {"type": "number"},
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"stage": {"type": "string", "description": "Hook / 过桥 / 正文 / CTA"},
|
||
"desc": {"type": "string"},
|
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},
|
||
"required": ["start", "end", "stage"],
|
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},
|
||
},
|
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"matrix": {
|
||
"type": "object",
|
||
"description": "卖点覆盖矩阵:哪个卖点落在第几个镜头",
|
||
"properties": {
|
||
"shots": {"type": "integer"},
|
||
"rows": {
|
||
"type": "array",
|
||
"items": {
|
||
"type": "object",
|
||
"properties": {
|
||
"point": {"type": "string"},
|
||
"hits": {"type": "array", "items": {"type": "integer"}},
|
||
},
|
||
"required": ["point", "hits"],
|
||
},
|
||
},
|
||
},
|
||
},
|
||
"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指令:秒级分镜(每镜画面/动作/机位/光线)、口播原文、"
|
||
"风格锚点、一致性要求。已 @ 的素材会自动作为参考图附上,"
|
||
"不要在这里重复描述它们的长相。**不要写字幕相关要求。**"
|
||
),
|
||
},
|
||
},
|
||
"required": ["usp", "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 [])[:3]:
|
||
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 video_model_name(params: dict) -> str:
|
||
"""会话参数里的模型 label → 火山模型名。认不出就回落 2.5(最长 30 秒那档)。"""
|
||
return VIDEO_MODEL_BY_LABEL.get(str(params.get("model") or ""), 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_BY_LABEL.get(str(params.get("model") or ""), params.get("model") or None),
|
||
reference_image_ids=reference_image_ids or None,
|
||
)
|
||
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 [])
|
||
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 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, ""
|
||
|
||
|
||
# ---------------------------------------------------------------- 提示词
|
||
|
||
|
||
def build_system_prompt(context: AgentContext) -> 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,像个有经验的同事,不要写成客服话术或需求确认清单。",
|
||
"- 不要复述用户刚说过的话,不要说「好的,我明白了」这种空句。",
|
||
"- 一次只推进一步。",
|
||
"",
|
||
"【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。",
|
||
"- 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 出点选卡,禁止只在气泡里问「换成哪个、填个名字」。用户不必 @。",
|
||
"- 用户说改时长/模型/比例/分辨率:立刻 ask_user,type=single 给出选项;选完后旧方案作废,必须按新参数重新 write_plan。不要让用户去点底部菜单。",
|
||
"- 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 not context.is_video:
|
||
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 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;用户要改就出选项卡,选完必须重写方案)")
|
||
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自动附给生成模型锁人锁物,你在 prompt 里不需要重复描述它们的外观。"
|
||
)
|
||
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 build_messages(context: AgentContext) -> 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)}]
|
||
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:
|
||
answers = (message.payload or {}).get("answers") or {}
|
||
labels = {f["key"]: f["label"] for f in (message.payload or {}).get("fields", [])}
|
||
if answers:
|
||
joined = ";".join(f"{labels.get(k, k)} → {v}" for k, v in answers.items())
|
||
messages.append({"role": "assistant", "content": f"(我问了用户几个问题)"})
|
||
messages.append({"role": "user", "content": f"(用户回答){joined}"})
|
||
else:
|
||
messages.append({"role": "assistant", "content": "(我向用户提了问题,还没收到回答)"})
|
||
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})"})
|
||
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:
|
||
try:
|
||
parsed = json.loads(raw or "{}")
|
||
except ValueError:
|
||
return {}
|
||
return parsed if isinstance(parsed, dict) else {}
|
||
|
||
|
||
def stream_creation_agent(
|
||
*,
|
||
conversation: CreationConversation,
|
||
user,
|
||
text: str,
|
||
refs: list[dict] | None = None,
|
||
model_config: ModelConfig | None = None,
|
||
) -> Iterator[str]:
|
||
"""一条用户消息 → SSE 流。生成器,由 StreamingHttpResponse 逐帧下发。"""
|
||
refs = refs or []
|
||
model_config = model_config or get_default_model(ModelConfig.Capability.TEXT)
|
||
if model_config is None:
|
||
yield _sse({"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:
|
||
with transaction.atomic():
|
||
pin_refs(conversation, refs)
|
||
user_message = append_message(conversation, role="user", text=text, refs=refs)
|
||
yield _sse({"type": "message", "message": _message_payload(user_message)})
|
||
|
||
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 _sse({"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)
|
||
|
||
provider = build_provider(model_config)
|
||
messages = build_messages(context)
|
||
tools = tool_schemas(context)
|
||
|
||
for _round in range(MAX_TOOL_ROUNDS):
|
||
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 _sse({"type": "reasoning", "text": chunk.get("text", "")})
|
||
elif kind == "delta":
|
||
piece = chunk.get("text", "")
|
||
text_buffer.append(piece)
|
||
yield _sse({"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")]
|
||
|
||
if said:
|
||
bubble = append_message(conversation, role="assistant", text=said)
|
||
yield _sse({"type": "message", "message": _message_payload(bubble)})
|
||
|
||
if not calls:
|
||
pick = wanted_asset_pick(text, refs)
|
||
param_keys = wanted_param_keys(text, is_video=context.is_video)
|
||
fields = None
|
||
if pick:
|
||
fields = [{
|
||
"key": pick,
|
||
"label": _ASSET_PICK_LABEL[pick],
|
||
"type": "asset",
|
||
"required": True,
|
||
"asset_types": [pick],
|
||
}]
|
||
elif param_keys:
|
||
fields = session_param_fields(param_keys, context.is_video)
|
||
if fields:
|
||
result, _stop = _dispatch_tool(context, "ask_user", {"fields": fields})
|
||
for event in result.get("_events", []):
|
||
yield _sse(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):
|
||
name = call["name"]
|
||
args = _parse_arguments(call["arguments"])
|
||
yield _sse({"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 _sse({"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 _sse({"type": "message", "message": _message_payload(failure)})
|
||
yield _sse({"type": "done"})
|
||
return
|
||
yield _sse({"type": "tool", "id": name, "label": _TOOL_LABELS.get(name, name), "status": "done"})
|
||
for event in result.get("_events", []):
|
||
yield _sse(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 _sse({"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 _sse({"type": "error", "detail": "生成过程出错了,请再试一次"})
|
||
|
||
|
||
_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",
|
||
}
|
||
|
||
|
||
def _dispatch_tool(context: AgentContext, name: str, args: dict) -> 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
|
||
message = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.ELICIT,
|
||
payload={"fields": fields, "submitted": False, "answers": {}},
|
||
)
|
||
# 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":
|
||
message = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.STRATEGY,
|
||
payload={key: str(args.get(key) or "") for key in ("target", "trust", "belief", "direction")},
|
||
)
|
||
# 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
|
||
plan_payload = {
|
||
"usp": str(args.get("usp") or ""),
|
||
"points": [str(p) for p in (args.get("points") or [])][:3],
|
||
"timeline": args.get("timeline") or [],
|
||
"matrix": args.get("matrix") or {},
|
||
"voice_chars": args.get("voice_chars") or [],
|
||
"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={"label": "开始生成", "estimated_credits": credits,
|
||
"video_prompt": video_prompt, "submitted": False},
|
||
)
|
||
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
|
||
payload, tasks = _run_generate_image(context, args)
|
||
events = []
|
||
for task in tasks:
|
||
message = append_message(
|
||
context.conversation, role="assistant",
|
||
kind=CreationMessage.Kind.GENERATING,
|
||
payload={"task_id": str(task.id), "kind": "image", "prompt": args.get("prompt", "")},
|
||
task=task,
|
||
)
|
||
events.append({"type": "message", "message": _message_payload(message)})
|
||
events.append({"type": "task", "task_id": str(task.id), "kind": "image"})
|
||
_remember_artifact(context.conversation, args.get("prompt", ""), "image")
|
||
return {"payload": payload, "_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
|