极速成片页面和提炼提示词

core/backend/apps/ai/services.py

@@ -571,6 +571,7 @@ def _collect_extract_text(
     temperature: float = 0.3,
     timeout: float = 300,
     on_event=None,
+    extra_body=None,
 ) -> tuple[str, dict]:
     """走与「脚本生成」同一条已在生产验证稳定的流式通道把模型输出收全。
 
@@ -587,6 +588,7 @@ def _collect_extract_text(
         messages=messages,
         temperature=temperature,  # 结构化抽取要稳:低温降低 JSON 漂移
         timeout=timeout,
+        extra_body=extra_body,
     ):
         if on_event is not None:
             on_event(ev)
@@ -624,6 +626,7 @@ def execute_routed_text_request(
     abort_check=None,
     allow_retry: bool = True,
     allow_fallback: bool = True,
+    extra_body=None,
 ):
     """文本入口共用的模型调用薄层：能力声明、Provider 调用、输出校验和尝试成本审计。
 
@@ -658,6 +661,7 @@ def execute_routed_text_request(
                 messages,
                 temperature=temperature,
                 timeout=timeout,
+                extra_body=extra_body,
                 on_event=(
                     (lambda event: stream_event_callback(event, stream_call_number))
                     if stream_event_callback is not None

core/backend/apps/ai/test_video_digest.py

@@ -23,9 +23,11 @@ from apps.ai.video_digest import (
     MAX_UPLOAD_BYTES,
     MIN_FRAMES,
     SECONDS_PER_FRAME,
+    DigestVideo,
     VideoDigestError,
     VideoFrame,
     build_digest_messages,
+    digest_input_from_upload,
     frames_from_upload,
     load_digest_skill,
     plan_frame_times,
@@ -36,11 +38,11 @@ from apps.ai.video_digest import (
 
 class FramePlanTests(SimpleTestCase):
     def test_frame_count_scales_with_duration_within_bounds(self):
-        for duration, expected in [(3, MIN_FRAMES), (10, MIN_FRAMES), (30, 6), (60, 12), (180, MAX_FRAMES)]:
+        for duration, expected in [(3, MIN_FRAMES), (10, MIN_FRAMES), (30, 15), (60, 30), (180, MAX_FRAMES)]:
             self.assertEqual(len(plan_frame_times(duration)), expected, duration)
 
     def test_short_clip_still_gets_min_frames(self):
-        """5 秒的片子按每 5 秒一帧只有 1 帧,判不出分镜,必须兜到 MIN_FRAMES。"""
+        """2 秒的片子按每 2 秒一帧只有 1 帧,判不出分镜,必须兜到 MIN_FRAMES。"""
         self.assertEqual(len(plan_frame_times(SECONDS_PER_FRAME)), MIN_FRAMES)
 
     def test_times_are_inside_the_clip_and_ordered(self):
@@ -90,10 +92,18 @@ class UploadGuardTests(SimpleTestCase):
 class MessageBuildTests(SimpleTestCase):
     frames = [VideoFrame(at_seconds=2, jpeg=b"\xff\xd8fake"), VideoFrame(at_seconds=7, jpeg=b"\xff\xd8fake2")]
 
+    def setUp(self):
+        load_digest_skill.cache_clear()
+
     def test_system_prompt_is_the_digest_skill(self):
         messages = build_digest_messages(self.frames, 10)
         self.assertEqual(messages[0]["role"], "system")
-        self.assertIn("七要素", messages[0]["content"])
+        self.assertIn("解说词", messages[0]["content"])
+        self.assertIn("覆盖全片", messages[0]["content"])
+
+    def test_user_prompt_asks_for_full_timeline(self):
+        content = build_digest_messages(self.frames, 60)[1]["content"]
+        self.assertIn("全片", content[0]["text"])
 
     def test_every_frame_is_inlined_with_its_timestamp(self):
         content = build_digest_messages(self.frames, 10)[1]["content"]
@@ -111,7 +121,25 @@ class MessageBuildTests(SimpleTestCase):
         self.assertIn("蓝牙耳机 · 数码3C", content[0]["text"])
 
     def test_skill_loads_from_disk(self):
-        self.assertIn("画面七要素", load_digest_skill())
+        self.assertIn("解说词", load_digest_skill())
+
+
+class NativeVideoInputTests(SimpleTestCase):
+    def test_short_clip_is_sent_as_whole_video(self):
+        video, frames, duration = digest_input_from_upload(_synth_clip(seconds=6))
+        self.assertIsNotNone(video)
+        self.assertEqual(frames, [])
+        self.assertTrue(video.mime.startswith("video/"))
+        self.assertGreater(len(video.data), 100)
+        self.assertAlmostEqual(duration, 6, delta=1)
+
+    def test_native_video_message_inlines_video_data_uri(self):
+        video = DigestVideo(mime="video/mp4", data=b"\x00\x00fake")
+        content = build_digest_messages(duration=20, video=video)[1]["content"]
+        media = [c for c in content if c["type"] == "image_url"]
+        self.assertEqual(len(media), 1)
+        self.assertTrue(media[0]["image_url"]["url"].startswith("data:video/mp4;base64,"))
+        self.assertIn("口播", content[0]["text"])
 
 
 class DigestValidationTests(SimpleTestCase):
@@ -128,6 +156,17 @@ class DigestValidationTests(SimpleTestCase):
         good = "【整体结构】\n形式:口播\n" + "【第 1 镜】0-4 秒 · 钩子\n主体:一位女生\n" * 3
         self.assertEqual(validate_digest_text(f"  {good}  "), good.strip())
 
+    def test_rejects_too_few_shots_for_a_long_clip(self):
+        stub = "【整体结构】\n形式:口播\n" + "【第 1 镜】0-4 秒 · 钩子\n主体:一位女生\n" * 3
+        with self.assertRaises(ValueError):
+            validate_digest_text(stub, duration=60, frame_count=30)
+
+    def test_accepts_dense_shots_for_a_long_clip(self):
+        body = "【整体结构】\n形式:口播\n时长:约 60 秒\n" + "".join(
+            f"【第 {i} 镜】{i * 3}-{(i + 1) * 3} 秒 · 卖点\n主体:一位女生\n" for i in range(1, 11)
+        )
+        self.assertTrue(validate_digest_text(body, duration=60, frame_count=30).startswith("【整体结构】"))
+
 
 class DigestModelPinTests(TestCase):
     """视频提炼必须钉会看图的 Gemini,不能跟默认语言模型走。"""
@@ -226,7 +265,7 @@ def _digest_with_duration(clip, duration: float):
 
 
 class VideoDigestApiTests(TestCase):
-    """视频复刻页独立入口:不绑项目,缺文件直接 400。"""
+    """视频提炼页独立入口:不绑项目,缺文件直接 400。"""
 
     def setUp(self):
         self.user = User.objects.create_user(username="remix-api", password="p")
@@ -284,3 +323,97 @@ class VideoDigestApiTests(TestCase):
         self.assertIsInstance(response.data["detail"], str)
         self.assertTrue(response.data["detail"])
         self.assertEqual(response.data["error"]["operation"], "video_digest")
+
+
+class VideoDigestBillingTests(TestCase):
+    """提炼提示词按功能价 30 积分结算,不跟 Gemini 文本单价。"""
+
+    def setUp(self):
+        from decimal import Decimal
+
+        from apps.billing.models import CreditAccount
+
+        self.user = User.objects.create_user(username="digest-bill", password="p")
+        self.team = Team.objects.create(name="DigestBill", owner=self.user)
+        TeamMember.objects.create(team=self.team, user=self.user, role=TeamMember.Role.OWNER)
+        self.account = CreditAccount.objects.create(team=self.team, balance=Decimal("100"))
+        provider, _ = ModelProvider.objects.get_or_create(
+            name="digest-bill-relay",
+            defaults={"display_name": "Gemini 官转", "status": ModelProvider.Status.ACTIVE},
+        )
+        provider.status = ModelProvider.Status.ACTIVE
+        provider.save(update_fields=["status"])
+        self.model = ModelConfig.objects.create(
+            provider=provider,
+            name=DIGEST_VISION_MODEL_NAME,
+            display_name="Gemini 3.1 Pro 官转",
+            capability=ModelConfig.Capability.TEXT,
+            status=ModelConfig.Status.ACTIVE,
+            unit_price=Decimal("10"),
+        )
+
+    def _run_digest(self):
+        from types import SimpleNamespace
+
+        from apps.ai.video_digest import digest_team_video
+
+        digest = "【整体结构】\n形式:口播\n" + "【第 1 镜】0-4 秒 · 钩子\n主体:一位女生\n" * 3
+        upload = SimpleUploadedFile("ref.mp4", b"x", content_type="video/mp4")
+        with (
+            patch(
+                "apps.ai.video_digest.digest_input_from_upload",
+                return_value=(None, [VideoFrame(at_seconds=2, jpeg=b"\xff\xd8x")], 15.0),
+            ),
+            patch(
+                "apps.ai.services.execute_routed_text_request",
+                return_value=SimpleNamespace(value=(digest, {}, digest)),
+            ),
+        ):
+            return digest_team_video(
+                team=self.team,
+                user=self.user,
+                upload=upload,
+                model_config_id=str(self.model.id),
+            )
+
+    def test_charges_thirty_points_not_model_unit_price(self):
+        from decimal import Decimal
+
+        from apps.ai.models import AITask
+
+        result = self._run_digest()
+        self.account.refresh_from_db()
+        self.assertEqual(Decimal(result["estimated_cost"]), Decimal("30"))
+        self.assertEqual(self.account.balance, Decimal("70"))
+        self.assertEqual(self.account.reserved_balance, Decimal("0"))
+        task = AITask.objects.get(id=result["task_id"])
+        self.assertEqual(task.estimated_cost, Decimal("30"))
+        self.assertEqual(task.actual_cost, Decimal("30"))
+
+    def test_insufficient_credit_skips_model_and_keeps_balance(self):
+        from decimal import Decimal
+
+        from apps.ai.video_digest import digest_team_video
+
+        self.account.balance = Decimal("20")
+        self.account.save(update_fields=["balance"])
+        upload = SimpleUploadedFile("ref.mp4", b"x", content_type="video/mp4")
+        with (
+            patch(
+                "apps.ai.video_digest.digest_input_from_upload",
+                return_value=(None, [VideoFrame(at_seconds=2, jpeg=b"\xff\xd8x")], 15.0),
+            ),
+            patch("apps.ai.services.execute_routed_text_request") as mocked,
+        ):
+            with self.assertRaises(VideoDigestError) as ctx:
+                digest_team_video(
+                    team=self.team,
+                    user=self.user,
+                    upload=upload,
+                    model_config_id=str(self.model.id),
+                )
+        self.assertIn("余额不足", str(ctx.exception))
+        mocked.assert_not_called()
+        self.account.refresh_from_db()
+        self.assertEqual(self.account.balance, Decimal("20"))
+        self.assertEqual(self.account.reserved_balance, Decimal("0"))

core/backend/apps/ai/video_digest.py

@@ -1,16 +1,10 @@
 """上传视频提炼 —— 参考视频 → 可人工逐镜编辑的中文分镜稿。
 
-链路:ffmpeg 抽帧(均匀采样) → 帧内联进多模态 messages → 走现有文本模型路由 → 纯文本分镜稿。
+优先把**完整视频(含音轨)**内联给 Gemini 3.1 Pro,跟官网「直接上传视频」同一条能力:
+模型能看全片、听口播,不会只拿到稀疏静帧。文件太大塞不进请求时,才退回 ffmpeg 抽帧。
 
-两个「本来以为要新建、其实已经有」的前提:
-  1. **读图能力**:拆视频是多模态(帧图 + 文本)。默认语言模型现在可能是纯文本豆包,
-     **不能再用 get_default_model(TEXT)**。固定钉 Gemini 3.1 Pro 官转
-     (``gemini-3.1-pro-preview``,展示名带「官转」优先)。
-  2. **ffmpeg**:第 5 阶段导出早就依赖它,已装进后端镜像(见 Dockerfile)。
-
-**没有音轨**:帧里读不到口播,只能读画面上的字幕。这是刻意取舍——接语音转写要另开火山 ASR 服务、
-另加一套凭证与计价,而带货参考片绝大多数带硬字幕,且口播词下游本来就要按用户自己的商品重写。
-skill 里已要求模型「无字幕就如实写缺失,不许编口播词」。
+拆视频必须会看视频/图。默认语言模型现在可能是纯文本豆包,不能用 get_default_model(TEXT)。
+固定钉 Gemini 3.1 Pro 官转(``gemini-3.1-pro-preview``,展示名带「官转」优先)。
 """
 
 from __future__ import annotations
@@ -19,6 +13,7 @@ import base64
 import json
 import logging
 import math
+import re
 import shutil
 import subprocess
 import tempfile
@@ -36,12 +31,23 @@ ALLOWED_SUFFIXES = (".mp4", ".mov", ".m4v", ".webm")
 MAX_UPLOAD_BYTES = 200 * 1024 * 1024  # 200 MB
 MAX_DURATION_SECONDS = 180  # 3 分钟。带货参考片远短于此;更长的帧采样密度不够,拆出来也是错的
 
-# 抽帧:每 5 秒一帧,夹在 4~12 帧之间。12 帧 × 512px JPEG ≈ 0.5 MB base64,单次请求扛得住
-SECONDS_PER_FRAME = 5
-MIN_FRAMES = 4
-MAX_FRAMES = 12
-FRAME_WIDTH = 512  # 帧宽上限;分镜拆解看的是构图与景别,不需要原分辨率
-FRAME_QUALITY = 5  # ffmpeg -q:v,2(最好)~31(最差)
+# 官网直接传视频的上限大约是请求 20MB;base64 会胀到 4/3,所以原文件卡在 15MB。
+INLINE_VIDEO_MAX_BYTES = 15 * 1024 * 1024
+_SUFFIX_MIME = {
+    ".mp4": "video/mp4",
+    ".m4v": "video/mp4",
+    ".mov": "video/quicktime",
+    ".webm": "video/webm",
+}
+
+# 抽帧只在整段视频塞不进请求时启用。
+SECONDS_PER_FRAME = 2
+MIN_FRAMES = 8
+MAX_FRAMES = 36
+FRAME_WIDTH = 768
+FRAME_QUALITY = 3
+DIGEST_MAX_TOKENS = 8192
+_SHOT_MARK = re.compile(r"【第\s*\d+\s*镜】")
 
 _FFMPEG_TIMEOUT = 60
 
@@ -59,6 +65,15 @@ class VideoFrame:
         return "data:image/jpeg;base64," + base64.b64encode(self.jpeg).decode("ascii")
 
 
+@dataclass(frozen=True)
+class DigestVideo:
+    mime: str
+    data: bytes
+
+    def as_data_url(self) -> str:
+        return f"data:{self.mime};base64," + base64.b64encode(self.data).decode("ascii")
+
+
 # --------------------------------------------------------------------------- #
 # skill 加载
 # --------------------------------------------------------------------------- #
@@ -78,9 +93,9 @@ def load_digest_skill() -> str:
         return main.read_text(encoding="utf-8")
     # 兜底:skill 丢了也别整条链路挂掉,退化成一句话提示词(产出会明显变差,交接文档已注明须带 skills 目录)
     return (
-        "你是分镜拆解 agent。输入是一条电商短视频按时间均匀抽出的截帧。"
-        "逐镜还原分镜,每镜写清主体/动作/场景/景别/运镜/光线氛围/商品露出七要素,"
-        "台词只抄画面上的字幕,看不见的不要编。输出中文纯文本。"
+        "你是分镜拆解 agent。输入是一条电商短视频的完整文件,含画面和口播。"
+        "必须覆盖全片,从 0 秒写到片尾,有几镜写几镜,不要概括成几大段。"
+        "每镜写画面和解说词,解说词按听到的口播逐字写。输出中文纯文本。"
     )
 
 
@@ -145,61 +160,169 @@ def extract_frames(path: str | Path, times: list[int]) -> list[VideoFrame]:
     return frames
 
 
-def frames_from_upload(upload) -> tuple[list[VideoFrame], float]:
-    """校验上传文件 → 落临时盘 → 探时长 → 抽帧。临时文件退出即删。"""
+def _write_upload(upload) -> tuple[str, str, int]:
+    """校验后缀和体积,把上传落到临时文件。返回 (path, suffix, size)。调用方负责删除。"""
     name = (getattr(upload, "name", "") or "").lower()
     if not name.endswith(ALLOWED_SUFFIXES):
         raise VideoDigestError("只支持 mp4 / mov / m4v / webm 四种视频格式")
     size = getattr(upload, "size", 0) or 0
     if size > MAX_UPLOAD_BYTES:
         raise VideoDigestError(f"视频不能超过 {MAX_UPLOAD_BYTES // 1024 // 1024} MB,请压缩后再传")
-
     suffix = Path(name).suffix or ".mp4"
-    with tempfile.NamedTemporaryFile(suffix=suffix) as tmp:
+    tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
+    try:
         for chunk in upload.chunks():
             tmp.write(chunk)
         tmp.flush()
-        duration = probe_duration(tmp.name)
-        if duration > MAX_DURATION_SECONDS:
-            raise VideoDigestError(
-                f"视频不能超过 {MAX_DURATION_SECONDS // 60} 分钟,请剪出要参考的那一段再传"
-            )
-        return extract_frames(tmp.name, plan_frame_times(duration)), duration
+    finally:
+        tmp.close()
+    return tmp.name, suffix, Path(tmp.name).stat().st_size
+
+
+def _materialize_upload(upload) -> tuple[str, str, int, float]:
+    """校验 → 落盘 → 探时长。返回 (path, suffix, size, duration),调用方负责删文件。"""
+    path, suffix, size = _write_upload(upload)
+    try:
+        duration = probe_duration(path)
+    except Exception:
+        Path(path).unlink(missing_ok=True)
+        raise
+    if duration > MAX_DURATION_SECONDS:
+        Path(path).unlink(missing_ok=True)
+        raise VideoDigestError(
+            f"视频不能超过 {MAX_DURATION_SECONDS // 60} 分钟,请剪出要参考的那一段再传"
+        )
+    return path, suffix, size, duration
+
+
+def _compress_video(path: str) -> bytes | None:
+    """压到能内联的体积。失败返回 None,由调用方改抽帧。"""
+    ffmpeg = _binary("ffmpeg")
+    out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    out.close()
+    try:
+        done = subprocess.run(
+            [
+                ffmpeg, "-v", "error", "-y", "-i", path,
+                "-c:v", "libx264", "-preset", "veryfast", "-crf", "28",
+                "-vf", "scale='min(1280,iw)':-2",
+                "-c:a", "aac", "-b:a", "64k",
+                "-movflags", "+faststart", out.name,
+            ],
+            capture_output=True, timeout=_FFMPEG_TIMEOUT * 3,
+        )
+        if done.returncode != 0:
+            return None
+        data = Path(out.name).read_bytes()
+        if not data or len(data) > INLINE_VIDEO_MAX_BYTES:
+            return None
+        return data
+    except Exception:  # noqa: BLE001 — 压缩失败就抽帧,别挡住提炼
+        return None
+    finally:
+        Path(out.name).unlink(missing_ok=True)
+
+
+def _native_video(path: str, size: int, suffix: str) -> DigestVideo | None:
+    mime = _SUFFIX_MIME.get(suffix.lower(), "video/mp4")
+    if size <= INLINE_VIDEO_MAX_BYTES:
+        return DigestVideo(mime=mime, data=Path(path).read_bytes())
+    compressed = _compress_video(path)
+    if compressed:
+        return DigestVideo(mime="video/mp4", data=compressed)
+    return None
+
+
+def digest_input_from_upload(upload) -> tuple[DigestVideo | None, list[VideoFrame], float]:
+    """优先整段视频(含音轨);塞不进请求才抽帧。"""
+    path = ""
+    try:
+        path, suffix, size, duration = _materialize_upload(upload)
+        video = _native_video(path, size, suffix)
+        if video is not None:
+            return video, [], duration
+        return None, extract_frames(path, plan_frame_times(duration)), duration
+    finally:
+        if path:
+            Path(path).unlink(missing_ok=True)
+
+
+def frames_from_upload(upload) -> tuple[list[VideoFrame], float]:
+    """校验上传文件 → 落临时盘 → 探时长 → 抽帧。单测与抽帧兜底用。"""
+    path = ""
+    try:
+        path, _suffix, _size, duration = _materialize_upload(upload)
+        return extract_frames(path, plan_frame_times(duration)), duration
+    finally:
+        if path:
+            Path(path).unlink(missing_ok=True)
 
 
 # --------------------------------------------------------------------------- #
 # 组装多模态消息
 # --------------------------------------------------------------------------- #
 def build_digest_messages(
-    frames: list[VideoFrame],
-    duration: float,
+    frames: list[VideoFrame] | None = None,
+    duration: float = 0,
     *,
     product_hint: str = "",
+    video: DigestVideo | None = None,
 ) -> list[dict]:
-    """system = 拆解 skill;user = 时间戳 + 帧图交替,让模型知道每张图在原片的第几秒。"""
-    head = [
-        f"这是一条时长约 {round(duration)} 秒的电商带货短视频,",
-        f"按时间顺序均匀抽了 {len(frames)} 帧。每帧图前面标了它在原片中的时间点。",
-    ]
+    """system = 拆解 skill;user = 完整视频(优先)或抽帧。"""
+    frames = frames or []
+    if video is not None:
+        head = [
+            f"这是一条时长约 {round(duration)} 秒的电商带货短视频的**完整文件**(含画面和口播音轨)。",
+            "请按技能还原全片分镜:从 0 秒写到片尾,有几镜写几镜。",
+            "解说词按听到的口播逐字写;画面上的花字一并写进画面。",
+        ]
+    else:
+        head = [
+            f"这是一条时长约 {round(duration)} 秒的电商带货短视频,",
+            f"按时间顺序均匀抽了 {len(frames)} 帧。每帧图前面标了它在原片中的时间点。",
+            "请按技能还原**全片**分镜:从 0 秒写到片尾,有几镜写几镜。",
+        ]
     if product_hint:
         head.append(f"用户接下来想用这条片子的结构去拍自己的商品:{product_hint}。")
-    head.append("请按技能里的输出格式还原它的分镜。")
 
     content: list[dict] = [{"type": "text", "text": "".join(head)}]
-    for frame in frames:
-        content.append({"type": "text", "text": f"[第 {frame.at_seconds} 秒]"})
-        content.append({"type": "image_url", "image_url": {"url": frame.as_data_url()}})
+    if video is not None:
+        data_url = video.as_data_url()
+        # 官转(New-API)把 OpenAI image_url 的 data URI 转成 Gemini inline_data,
+        # mime 从 data:video/mp4 头读取,模型按整段视频+音轨理解,等同官网直接上传。
+        content.append({"type": "image_url", "image_url": {"url": data_url}})
+    else:
+        for frame in frames:
+            content.append({"type": "text", "text": f"[第 {frame.at_seconds} 秒]"})
+            content.append({"type": "image_url", "image_url": {"url": frame.as_data_url()}})
     return [
         {"role": "system", "content": load_digest_skill()},
         {"role": "user", "content": content},
     ]
 
 
-def validate_digest_text(text: str) -> str:
-    """模型偶尔吐空 / 吐一句道歉。判空后交给路由层重试或切模型,别把废稿塞给用户。"""
+def min_digest_shots(duration: float, frame_count: int) -> int:
+    """短片至少 3 镜;20 秒以上约每 6 秒一镜,且不超过抽到的帧数。"""
+    if duration < 20 and frame_count < 8:
+        return 3
+    by_time = max(4, int(duration // 6))
+    if frame_count:
+        return min(frame_count, by_time)
+    return by_time
+
+
+def validate_digest_text(text: str, *, duration: float = 0, frame_count: int = 0) -> str:
+    """模型偶尔吐空、只写片头、或概括成几大段。废稿不塞给用户。"""
     cleaned = (text or "").strip()
     if len(cleaned) < 80 or "【" not in cleaned:
         raise ValueError("视频拆解结果不完整")
+    shots = _SHOT_MARK.findall(cleaned)
+    if not shots:
+        raise ValueError("视频拆解结果不完整")
+    if duration >= 20 or frame_count >= 8:
+        needed = min_digest_shots(duration, frame_count)
+        if len(shots) < needed:
+            raise ValueError("视频拆解镜头过少,请重试")
     return cleaned
 
 
@@ -295,7 +418,7 @@ def digest_project_video(*, project, user, upload, model_config_id=None) -> dict
 
 
 def digest_team_video(*, team, user, upload, model_config_id=None) -> dict:
-    """视频复刻页:不绑项目,计费挂当前团队。"""
+    """视频提炼页:不绑项目,计费挂当前团队。"""
     return _digest_video(
         team=team,
         user=user,
@@ -313,31 +436,43 @@ def _digest_video(*, team, user, upload, project=None, product_hint="", model_co
 
     from apps.ai.models import AITask
     from apps.ai.services import create_ai_task, execute_routed_text_request
-    from apps.billing.pricing import quote_flat
+    from apps.billing.pricing import quote_video_digest
     from apps.billing.services.ledger import charge_reserved_credit, reserve_credit
 
-    frames, duration = frames_from_upload(upload)
+    video, frames, duration = digest_input_from_upload(upload)
 
     model_config = resolve_digest_model_config(preferred_id=model_config_id)
     if model_config is None:
         raise VideoDigestError("视频提炼需要 Gemini 3.1 Pro(会看图),当前没有启用,请联系管理员")
     logger.info(
-        "video digest using %s:%s (%s)",
+        "video digest using %s:%s (%s) input=%s duration=%.1fs bytes=%s frames=%s",
         model_config.provider.name,
         model_config.name,
         model_config.display_name,
+        "native_video" if video is not None else "frames",
+        duration,
+        len(video.data) if video is not None else 0,
+        len(frames),
     )
 
-    messages = build_digest_messages(frames, duration, product_hint=product_hint)
+    messages = build_digest_messages(
+        frames, duration, product_hint=product_hint, video=video
+    )
     request_payload = {
         "model": model_config.name,
         "endpoint": model_config.endpoint,
         "feature": "video_remix" if project is None else "video_digest",
         "duration_seconds": round(duration, 2),
+        "input": "native_video" if video is not None else "frames",
         "frame_count": len(frames),
+        "video_bytes": len(video.data) if video is not None else 0,
         "frame_times": [f.at_seconds for f in frames],
     }
 
+    quote = quote_video_digest(team=team, model_config=model_config)
+    if quote.meta.get("rate"):
+        request_payload = {**request_payload, "points_per_yuan_snapshot": quote.meta["rate"]}
+
     if project is not None:
         task = create_ai_task(
             project=project,
@@ -346,11 +481,9 @@ def _digest_video(*, team, user, upload, project=None, product_hint="", model_co
             model_config=model_config,
             # 帧是几百 KB base64,绝不进 request_payload(会把 AITask 表撑爆),只记形状
             request_payload=request_payload,
+            quote=quote,
         )
     else:
-        quote = quote_flat(model_config, team=team)
-        if quote.meta.get("rate"):
-            request_payload = {**request_payload, "points_per_yuan_snapshot": quote.meta["rate"]}
         try:
             with transaction.atomic():
                 task = AITask.objects.create(
@@ -385,8 +518,15 @@ def _digest_video(*, team, user, upload, project=None, product_hint="", model_co
             structured_output=False,
             business_operation="video_digest",
             temperature=0.4,
-            validate_text=validate_digest_text,
-            request_summary={"duration_seconds": round(duration, 2), "frame_count": len(frames)},
+            validate_text=lambda text: validate_digest_text(
+                text, duration=duration, frame_count=len(frames) or (24 if video else 0)
+            ),
+            extra_body={"max_tokens": DIGEST_MAX_TOKENS},
+            request_summary={
+                "duration_seconds": round(duration, 2),
+                "frame_count": len(frames),
+                "input": "native_video" if video is not None else "frames",
+            },
             allow_retry=False,
             allow_fallback=False,
         )
@@ -397,7 +537,7 @@ def _digest_video(*, team, user, upload, project=None, product_hint="", model_co
 
     with transaction.atomic():
         task.status = AITask.Status.SUCCEEDED
-        task.response_payload = {"digest": digest[:8000]}
+        task.response_payload = {"digest": digest[:32000]}
         task.actual_cost = task.estimated_cost
         task.completed_at = timezone.now()
         task.save(update_fields=["status", "response_payload", "actual_cost", "completed_at", "updated_at"])
@@ -407,6 +547,7 @@ def _digest_video(*, team, user, upload, project=None, product_hint="", model_co
         "text": digest,
         "chars": len(digest),
         "frames": len(frames),
+        "input": "native_video" if video is not None else "frames",
         "duration": round(duration, 1),
         "task_id": str(task.id),
         "estimated_cost": str(task.estimated_cost),

core/backend/apps/ai/views.py

@@ -697,9 +697,9 @@ def _set_free_video_generated_assets_deleted(task, deleted):
 
 
 class VideoDigestView(APIView):
-    """视频复刻 · 上传参考视频提炼分镜稿(不绑项目)。
+    """视频提炼 · 上传参考视频提炼分镜稿(不绑项目)。
 
-    POST /api/ai/video-digest/  multipart file → 中文分镜稿。慢(30~60 秒),计一次文本费。
+    POST /api/ai/video-digest/  multipart file → 中文分镜稿。慢(30~60 秒),固定 30 积分/次,失败退还。
     """
 
     parser_classes = [MultiPartParser, FormParser]

core/backend/apps/billing/pricing.py

@@ -33,6 +33,8 @@ _CONFIG_TTL = 60
 # 实扣会比消费页公示价低一半(review 确认的三方不一致)。
 FLAT_FALLBACK_POINTS = Decimal("10")
 FLAT_FALLBACK_POINTS_IMAGE = Decimal("20")
+# 提炼提示词 / 上传视频提炼:功能挂牌价,不跟 Gemini unit_price(那是普通文本 10 积分/次)。
+VIDEO_DIGEST_POINTS = Decimal("30")
 
 
 def get_billing_config() -> BillingConfig:
@@ -114,6 +116,26 @@ def quote_flat(model_config, *, units: int = 1, team=None) -> Quote:
     )
 
 
+def quote_video_digest(*, team=None, model_config=None) -> Quote:
+    """提炼提示词:固定 30 积分/次。失败退还。team 传入则乘团队价格系数。"""
+    multiplier = team_price_multiplier(team)
+    points = apply_team_price(VIDEO_DIGEST_POINTS, multiplier)
+    base_cost = Decimal("0")
+    if model_config is not None:
+        base_cost = Decimal(str(_pricing_meta(model_config).get("base_cost_yuan") or 0))
+    return Quote(
+        points=points,
+        base_cost_yuan=base_cost,
+        meta={
+            "rule": "video_digest_flat",
+            "units": 1,
+            "unit_points": str(VIDEO_DIGEST_POINTS),
+            "price_multiplier": str(multiplier),
+            "rate": str(get_billing_config().points_per_yuan),
+        },
+    )
+
+
 def quote_voiceover(model_config, *, char_count: int, team=None) -> Quote:
     """配音字符阶梯:ceil(chars / chars_per_unit) × points_per_unit,最低 min_units 档。team 传入则乘系数。"""
     pricing = _pricing_meta(model_config)

core/backend/apps/billing/tests.py

@@ -231,6 +231,15 @@ class PricingEngineTests(TestCase):
         # 多单位(单图批量按张)
         self.assertEqual(quote_flat(self.text_model, units=3).points, Decimal("30"))
 
+    def test_video_digest_is_fixed_thirty_not_model_unit_price(self):
+        from apps.billing.pricing import VIDEO_DIGEST_POINTS, quote_video_digest
+
+        self.assertEqual(VIDEO_DIGEST_POINTS, Decimal("30"))
+        quote = quote_video_digest(model_config=self.text_model)
+        self.assertEqual(quote.points, Decimal("30"))
+        self.assertEqual(quote.meta["rule"], "video_digest_flat")
+        self.assertEqual(quote.meta["unit_points"], "30")
+
     def test_flat_fallback_when_unit_price_unset(self):
         from apps.billing.pricing import FLAT_FALLBACK_POINTS, quote_flat
 
@@ -414,6 +423,14 @@ class TeamPriceMultiplierTests(TestCase):
         # meta.rate 是汇率快照契约:create_ai_task 落 payload.points_per_yuan_snapshot,毛利报表用它防汇率漂移
         self.assertEqual(Decimal(quote.meta["rate"]), Decimal("10"))
 
+    def test_video_digest_applies_multiplier_to_feature_price(self):
+        from apps.billing.pricing import quote_video_digest
+
+        # 30 × 0.8 = 24;不跟模型 unit_price=20
+        quote = quote_video_digest(team=self.team, model_config=self.image_model)
+        self.assertEqual(quote.points, Decimal("24"))
+        self.assertEqual(quote_video_digest().points, Decimal("30"))
+
     def test_two_step_rounding(self):
         from apps.billing.pricing import quote_video_from_cost