合并分支
* Merge branch 'main' into cluster-gemini * FIX Gemini推理配置修复langfuse来源 * PERF Gemini推理配置适配langfuse来源 FIX 时间转换适配更多时间格式 * PERF prompt接入langfuse * Merge branch 'main' into cluster-gemini * FIX 修复pydantic导致的问题 PERF 预先使用prompt传输格式要求，如果失败fallback到调用gemini处理格式错误 * Merge branch 'main' into cluster-gemini * FIX 修复上传问题 * PERF 规范化gemini调用方式, 使用response_schema规范化输出 * PERF 修改gemini upload调用为规范调用 * PERF 优化prompt词提高识别准确率 FIX 修复升级httpx导致部署失败的问题 * Merge branch 'main' into cluster-gemini * PERF 优化prompt词提高识别准确率 * FIX 修复prompt时间问题 * Merge branch 'main' into cluster-gemini * PERF 修复时间最大限制问题 * Merge branch 'main' into cluster-gemini * FIX 修复缩放分辨率计算问题 ADD Gemini推理改为二阶段 FIX 修复时间合并计算问题 * Merge branch 'main' into cluster-gemini * ADD gemini数据源使用cloud storage --------- Merge request URL: https://g-ldyi2063.coding.net/p/dev/d/modalDeploy/git/merge/4861 Co-authored-by: 康宇佳
This commit is contained in:
Coding
parent
bffd102622
commit
7666102c27
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@ -1,3 +1,4 @@
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import json
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import uuid
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from datetime import datetime
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from enum import Enum
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@ -652,3 +653,25 @@ class GeminiSecondStageResponseModel(RootModel):
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表示一个包含多个商品及其时间线信息的列表。
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"""
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root: List[ProductTimeline]
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class GeminiFirstStagePromptVariables(BaseModel):
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product_list: List[str] = Field(description="商品名列表")
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@computed_field(description="xml格式排列的商品列表")
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@property
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def product_list_xml(self) -> str:
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xml_items = [f" <product>{product}</product>" for product in self.product_list]
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xml_string = "\n".join(xml_items)
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return f"<products>\n{xml_string}\n </products>"
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class GeminiSecondStagePromptVariables(BaseModel):
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product_json_list: List[dict] = Field(description="识别出的商品JSON列表")
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@computed_field(description="xml格式排列的识别出的商品JSON列表")
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@property
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def product_json_list_xml(self) -> str:
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xml_items = [f" <product>{json.dumps(product,ensure_ascii=False)}</product>" for product in self.product_json_list]
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xml_string = "\n".join(xml_items)
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return f"<products>\n{xml_string}\n </products>"
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@ -26,19 +26,29 @@ def parse_time(time_str):
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if re.match(r"^\d{2}:\d{2}:\d{2}\.\d{3}$",time_str):
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# 先尝试完整格式 HH:MM:SS.fff
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parsed_time = datetime.strptime(time_str, '%H:%M:%S.%f')
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elif re.match(r"^\d{2}:\d{2}:\d{2}:\d{3}$",time_str):
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# 如果失败，尝试 HH:MM:SS:fff 格式
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parsed_time = datetime.strptime(time_str, '%H:%M:%S:%f')
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elif re.match(r"^\d{2}:\d{2}\.\d{3}$",time_str):
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# 如果失败，尝试 MM:SS.fff 格式
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dt = datetime.strptime(time_str, '%M:%S.%f')
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# 将小时设为0，只保留分钟和秒
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parsed_time = datetime.combine(dt.date(), dt.time().replace(hour=0))
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elif re.match(r"^\d{2}\.\d{2}:\d{3}$",time_str):
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# 如果失败，尝试 MM.SS:fff 格式
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dt = datetime.strptime(time_str, '%M.%S:%f')
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# 将小时设为0，只保留分钟和秒
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parsed_time = datetime.combine(dt.date(), dt.time().replace(hour=0))
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elif re.match(r"^\d{2}\.\d{2}\.\d{3}$",time_str):
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# 如果失败，尝试 MM.SS.fff 格式
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dt = datetime.strptime(time_str, '%M.%S.%f')
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# 将小时设为0，只保留分钟和秒
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parsed_time = datetime.combine(dt.date(), dt.time().replace(hour=0))
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elif re.match(r"^\d{2}:\d{2}:\d{3}$",time_str):
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# 如果失败，尝试 MM:SS:fff 格式
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dt = datetime.strptime(time_str, '%M:%S:%f')
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# 将小时设为0，只保留分钟和秒
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parsed_time = datetime.combine(dt.date(), dt.time().replace(hour=0))
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elif re.match(r"^\d{2}:\d{2}:\d{2}:\d{3}$",time_str):
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# 如果失败，尝试 HH:MM:SS:fff 格式
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parsed_time = datetime.strptime(time_str, '%H:%M:%S:%f')
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else:
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logger.error(f"转换时间格式失败 {time_str}")
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return parsed_time
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@ -3,6 +3,9 @@ import uuid
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import modal
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from google.genai import types
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from jinja2 import Template
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from langfuse._client.client import Langfuse
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from langfuse.model import TextPromptClient
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from BowongModalFunctions.utils.HTTPUtils import GoogleAuthUtils, FlatJsonSchemaGenerator
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from ..video import downloader_image, app, config
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@ -16,8 +19,8 @@ with downloader_image.imports():
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from starlette import status
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from BowongModalFunctions.utils.SentryUtils import SentryUtils
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from BowongModalFunctions.models.media_model import MediaSource
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from BowongModalFunctions.models.web_model import SentryTransactionInfo, WebhookNotify, \
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GeminiFirstStageResponseModel, GeminiSecondStageResponseModel
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from BowongModalFunctions.models.web_model import SentryTransactionInfo, WebhookNotify, GeminiFirstStagePromptVariables, \
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GeminiSecondStagePromptVariables, GeminiFirstStageResponseModel, GeminiSecondStageResponseModel
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from BowongModalFunctions.models.ffmpeg_worker_model import FFMpegSliceSegment
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from BowongModalFunctions.utils.TimeUtils import TimeDelta, merge_product_data
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from BowongModalFunctions.utils.VideoUtils import FFMPEGSliceOptions, VideoUtils
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@ -25,7 +28,6 @@ with downloader_image.imports():
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@app.function(cpu=(0.5, 64), timeout=1800,
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max_containers=config.video_downloader_concurrency,
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secrets=[modal.Secret.from_name("gemini-prompt")],
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volumes={
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config.S3_mount_dir: modal.CloudBucketMount(
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bucket_name=config.S3_bucket_name,
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@ -65,6 +67,11 @@ with downloader_image.imports():
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"europe-west8",
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]
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langfuse = Langfuse(host="https://us.cloud.langfuse.com",
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secret_key="sk-lf-dd20cb0b-ef2e-49f6-80f0-b2d9cff1bb11",
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public_key="pk-lf-15f9d809-0bf6-4a84-ae1c-18f7a7d927c7",
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tracing_enabled=False)
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logger.info(f"\nmedia || {media.urn},"
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f"\nstart_time || {start_time}, "
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f"\nend_time || {end_time}, "
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@ -80,10 +87,20 @@ with downloader_image.imports():
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regions=gemini_region, access_token=google_api_key,
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)
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# 动态Prompt
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if "IMAGE_PRODUCT_IDENTIFICATION_PROMPT" in os.environ:
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IMAGE_PRODUCT_IDENTIFICATION_PROMPT = os.environ["IMAGE_PRODUCT_IDENTIFICATION_PROMPT"]
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def split_prompt_config(c:dict):
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return {
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"temperature": c["temperature"],
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"top_p": c["top_p"],
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"safety_settings": c["safety_settings"]
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},c
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# 动态Prompt, langfuse获取失败使用默认值
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IMAGE_PRODUCT_IDENTIFICATION_PROMPT = langfuse.get_prompt("Gemini自动切条/商品识别", type="text", label="latest")
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if IMAGE_PRODUCT_IDENTIFICATION_PROMPT:
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prompt_config = IMAGE_PRODUCT_IDENTIFICATION_PROMPT.config
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first_stage_generate_config, first_stage_correct_config = split_prompt_config(prompt_config)
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else:
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logger.warning("🥺获取一阶段Prompt失败，使用内置Prompt")
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IMAGE_PRODUCT_IDENTIFICATION_PROMPT = """<prompt>
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<instruction>
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你是专业的商品识别专家。我上传了商品图片网格，需要你识别图片中的商品并与商品列表进行匹配。
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- ✅ 输出商品数量不得超过图片中的黑色边框区域数量
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**商品列表-标准商品名称**：
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<items>
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{PRODUCT_LIST}
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</items>
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{{PRODUCT_LIST}}
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</instruction>
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<output_format>
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请按以下JSON格式输出：
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[
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{{
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{
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"image_order": 1,
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"image_name": "图片上显示的原始文字",
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"matched_product": "匹配到的标准商品名称(必须与商品列表-标准商品名称一致)或null",
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"match_confidence": 95,
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"visual_features": {{
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"visual_features": {
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"color": "商品详细配色等有辨识度的色彩特征",
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"pattern": "商品详细图案纹理布料等有辨识度的材质特征，材质可根据商品名以及图片综合进行判断",
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"style": "商品详细款式风格等有辨识度的款式特征"
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}}
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}}
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}
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}
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]
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</output_format>
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</prompt>"""
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first_stage_correct_config = {
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"temperature": 0.01,
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"top_p": 0.7,
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"safety_settings": [
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{
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"category": "HARM_CATEGORY_HARASSMENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_CIVIC_INTEGRITY",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_HATE_SPEECH",
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"threshold": "BLOCK_NONE"
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}
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],
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"response_mime_type": "application/json",
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"response_schema": GeminiFirstStageResponseModel.model_json_schema(schema_generator=FlatJsonSchemaGenerator)
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}
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first_stage_generate_config = {
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"temperature": first_stage_correct_config["temperature"],
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"top_p": first_stage_correct_config["top_p"],
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"safety_settings": first_stage_correct_config["safety_settings"]
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}
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if "VIDEO_TIMELINE_ANALYSIS_PROMPT" in os.environ:
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VIDEO_TIMELINE_ANALYSIS_PROMPT = os.environ["VIDEO_TIMELINE_ANALYSIS_PROMPT"]
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VIDEO_TIMELINE_ANALYSIS_PROMPT = langfuse.get_prompt("Gemini自动切条/视频时间点识别", type="text", label="latest")
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if VIDEO_TIMELINE_ANALYSIS_PROMPT:
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prompt_config = VIDEO_TIMELINE_ANALYSIS_PROMPT.config
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second_stage_generate_config, second_stage_correct_config = split_prompt_config(prompt_config)
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else:
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logger.warning("🥺获取二阶段Prompt失败，使用内置Prompt")
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VIDEO_TIMELINE_ANALYSIS_PROMPT = """<prompt>
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<instruction>
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基于已识别的商品清单，分析视频中每个商品的出现时间段。
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- 常见错误：将05:23.500解释为5分钟23.5秒
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**已识别商品清单(product-商品名 feature-商品特征 feature.color-商品详细颜色配色等色彩特征 feature.pattern-商品详细图案纹理布料等材质特征 feature.style-商品详细款式风格等有辨识度的款式特征)**：
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<items>
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{IDENTIFIED_PRODUCTS}
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</items>
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{{IDENTIFIED_PRODUCTS}}
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</instruction>
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<output_format>
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请按以下JSON格式输出，确保输出格式复核JSON格式要求：
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[
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{{
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{
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"product": "标准商品名称",
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"timeline": [
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"01:15.200 - 02:30.800 (穿着本品+介绍本品)",
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"05:10.100 - 06:25.600 (穿着本品+他品)"
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]
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}}
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}
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]
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</output_format>
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</prompt>"""
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second_stage_correct_config = {
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"temperature": 0.01,
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"top_p": 0.7,
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"safety_settings": [
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{
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"category": "HARM_CATEGORY_HARASSMENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_CIVIC_INTEGRITY",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_HATE_SPEECH",
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"threshold": "BLOCK_NONE"
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}
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],
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"response_mime_type": "application/json",
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"response_schema": GeminiSecondStageResponseModel.model_json_schema(schema_generator=FlatJsonSchemaGenerator)
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}
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second_stage_generate_config = {
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"temperature": second_stage_correct_config["temperature"],
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"top_p": second_stage_correct_config["top_p"],
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"safety_settings": second_stage_correct_config["safety_settings"]
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}
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async def upload(file_path):
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@ -215,7 +297,7 @@ with downloader_image.imports():
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return upload_response.urn
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def convert_json(json_like_str:str, json_model):
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def convert_json(json_like_str:str, correct_config):
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try:
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resp, resp_code = client.generate_content(model_id="gemini-2.5-flash",
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contents=[types.Content(role='user',
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@ -232,35 +314,7 @@ with downloader_image.imports():
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]
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)
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],
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config=types.GenerateContentConfig(
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temperature=0.01,
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top_p=0.7,
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safety_settings=[
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types.SafetySetting(
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category=types.HarmCategory.HARM_CATEGORY_HARASSMENT,
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threshold=types.HarmBlockThreshold.BLOCK_NONE,
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),
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types.SafetySetting(
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category=types.HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY,
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threshold=types.HarmBlockThreshold.BLOCK_NONE,
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),
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types.SafetySetting(
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category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
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threshold=types.HarmBlockThreshold.BLOCK_NONE,
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),
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types.SafetySetting(
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category=types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
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threshold=types.HarmBlockThreshold.BLOCK_NONE,
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),
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types.SafetySetting(
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category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH,
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threshold=types.HarmBlockThreshold.BLOCK_NONE,
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)
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],
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response_mime_type="application/json",
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response_schema=json_model.model_json_schema(
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schema_generator=FlatJsonSchemaGenerator)
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), timeout=900)
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config=types.GenerateContentConfig.model_validate(correct_config), timeout=900)
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except Exception as e:
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logger.exception(f"😭格式化json请求失败 {e}")
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return None
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@ -282,9 +336,7 @@ with downloader_image.imports():
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logger.warning("🥵请求负载过高, 随机更换地区重试")
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return None
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def parse_stage1_result(result_text):
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def parse_stage1_result(result_text, correct_config):
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"""解析第一阶段结果，提取已识别的商品列表"""
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try:
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# 清理结果文本，提取JSON部分
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|
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@ -298,7 +350,7 @@ with downloader_image.imports():
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try:
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stage1_data = json.loads(clean_result)
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except json.decoder.JSONDecodeError:
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stage1_data = convert_json(clean_result, GeminiFirstStageResponseModel)
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stage1_data = convert_json(clean_result, correct_config)
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if not stage1_data:
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raise Exception("json为空")
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identified_products = []
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@ -321,7 +373,6 @@ with downloader_image.imports():
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@SentryUtils.sentry_tracker(sentry_trace.x_trace_id, sentry_trace.x_baggage, op="inference_gemini",
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name="Gemini推理", fn_id=current_function_call_id())
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async def _handler(media: MediaSource,
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google_api_key: str,
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product_grid_list: List[str],
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product_list: List[any],
|
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start_time: str,
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|
|
@ -372,9 +423,6 @@ with downloader_image.imports():
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async def inference_api_first_stage():
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try:
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logger.info("🎈开始一阶段推理")
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product_list_str = ""
|
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for i, product in enumerate(product_title_list):
|
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product_list_str += f"<item>{i}. {product}</item>\n"
|
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image_parts = []
|
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for i in product_grid_list:
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image_parts.append(
|
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|
|
@ -387,7 +435,10 @@ with downloader_image.imports():
|
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)
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image_parts.append(
|
||||
types.Part.from_text(
|
||||
text=IMAGE_PRODUCT_IDENTIFICATION_PROMPT.format(PRODUCT_LIST=product_list_str)
|
||||
text=Template(IMAGE_PRODUCT_IDENTIFICATION_PROMPT.prompt
|
||||
if isinstance(IMAGE_PRODUCT_IDENTIFICATION_PROMPT, TextPromptClient)
|
||||
else IMAGE_PRODUCT_IDENTIFICATION_PROMPT).render(
|
||||
PRODUCT_LIST=GeminiFirstStagePromptVariables(product_list=product_title_list).product_list_xml)
|
||||
)
|
||||
)
|
||||
resp, resp_code = client.generate_content(model_id="gemini-2.5-flash",
|
||||
|
|
@ -395,32 +446,7 @@ with downloader_image.imports():
|
|||
parts=image_parts
|
||||
)
|
||||
],
|
||||
config=types.GenerateContentConfig(
|
||||
temperature=0.01,
|
||||
top_p=0.7,
|
||||
safety_settings=[
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_HARASSMENT,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
)
|
||||
],
|
||||
), timeout=900)
|
||||
config=types.GenerateContentConfig.model_validate(first_stage_generate_config), timeout=900)
|
||||
except Exception as e:
|
||||
logger.exception(f"😭Gemini一阶段推理请求失败: {e}")
|
||||
raise e
|
||||
|
|
@ -430,7 +456,7 @@ with downloader_image.imports():
|
|||
if reason == "STOP":
|
||||
result_text: str = resp.candidates[0].content.parts[0].text
|
||||
# 解析识别结果
|
||||
identified_products = parse_stage1_result(result_text)
|
||||
identified_products = parse_stage1_result(result_text, first_stage_correct_config)
|
||||
return identified_products
|
||||
else:
|
||||
logger.error(f"😭Gemini一阶段推理失败, Reason {reason}")
|
||||
|
|
@ -444,11 +470,6 @@ with downloader_image.imports():
|
|||
async def inference_api_second_stage(identified_products):
|
||||
try:
|
||||
logger.info("🎈开始二阶段推理")
|
||||
# 构建已识别商品清单字符串
|
||||
identified_products_str = ""
|
||||
for i, product in enumerate(identified_products):
|
||||
product_info_json = json.dumps(product,ensure_ascii=False)
|
||||
identified_products_str += f"<item>{i}. {product_info_json}<item>\n"
|
||||
video_parts = []
|
||||
video_parts.append(
|
||||
types.Part(
|
||||
|
|
@ -460,7 +481,10 @@ with downloader_image.imports():
|
|||
)
|
||||
video_parts.append(
|
||||
types.Part.from_text(
|
||||
text=VIDEO_TIMELINE_ANALYSIS_PROMPT.format(IDENTIFIED_PRODUCTS=identified_products_str)
|
||||
text=Template(VIDEO_TIMELINE_ANALYSIS_PROMPT.prompt
|
||||
if isinstance(VIDEO_TIMELINE_ANALYSIS_PROMPT, TextPromptClient)
|
||||
else VIDEO_TIMELINE_ANALYSIS_PROMPT).render(
|
||||
IDENTIFIED_PRODUCTS=GeminiSecondStagePromptVariables(product_json_list=identified_products).product_json_list_xml)
|
||||
)
|
||||
)
|
||||
resp, resp_code = client.generate_content(model_id="gemini-2.5-flash",
|
||||
|
|
@ -468,32 +492,7 @@ with downloader_image.imports():
|
|||
parts=video_parts
|
||||
)
|
||||
],
|
||||
config=types.GenerateContentConfig(
|
||||
temperature=0.01,
|
||||
top_p=0.7,
|
||||
safety_settings=[
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_HARASSMENT,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
),
|
||||
types.SafetySetting(
|
||||
category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH,
|
||||
threshold=types.HarmBlockThreshold.BLOCK_NONE,
|
||||
)
|
||||
],
|
||||
), timeout=900)
|
||||
config=types.GenerateContentConfig.model_validate(second_stage_generate_config), timeout=900)
|
||||
except Exception as e:
|
||||
logger.exception(f"😭Gemini二阶段推理请求失败: {e}")
|
||||
raise e
|
||||
|
|
@ -512,7 +511,7 @@ with downloader_image.imports():
|
|||
try:
|
||||
parts = json.loads(parts_text)
|
||||
except json.decoder.JSONDecodeError:
|
||||
parts = convert_json(parts_text, GeminiSecondStageResponseModel)
|
||||
parts = convert_json(parts_text, second_stage_correct_config)
|
||||
if not parts:
|
||||
raise Exception("json为空")
|
||||
logger.info(f"👌合并前 {json.dumps(parts, indent=4, ensure_ascii=False)}")
|
||||
|
|
@ -562,5 +561,5 @@ with downloader_image.imports():
|
|||
logger.exception(f"🥲推理失败, {e}")
|
||||
raise Exception(f"🥲推理失败, {e}")
|
||||
|
||||
return await _handler(media, google_api_key, product_grid_list, product_list, start_time, end_time, options,
|
||||
return await _handler(media, product_grid_list, product_list, start_time, end_time, options,
|
||||
sentry_trace, retry_time, scale)
|
||||
|
|
|
|||
Loading…
Reference in New Issue