引入Langfuse作为prompt词管理
* - 引入了Langfuse作为prompt词管理 - 处理嵌套型pydantic model输出非嵌套json schema的报错 --------- Merge request URL: https://g-ldyi2063.coding.net/p/dev/d/modalDeploy/git/merge/4836?initial=true Co-authored-by: shuohigh@gmail.com
This commit is contained in:
Coding
parent
2389b4b77f
commit
5e02dc0bb3
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@ -68,6 +68,8 @@ dependencies = [
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"pyfiglet>=1.0.3",
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"google-api-python-client>=2.172.0",
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"google-genai==1.21.0",
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"langfuse>=3.0.3",
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"jinja2>=3.1.6",
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]
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classifiers = [
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"Programming Language :: Python :: 3",
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@ -1,5 +1,5 @@
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from typing import Optional, Any
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from pydantic import Field
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from pydantic import Field, HttpUrl
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from pydantic_settings import BaseSettings, SettingsConfigDict
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@ -12,7 +12,8 @@ class WorkerConfig(BaseSettings):
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S3_region: str = Field(default='ap-northeast-2', description="S3挂载桶的地域")
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S3_bucket_name: str = Field(default='modal-media-cache', description="集群挂载的S3存储桶")
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S3_mount_dir: str = Field(default='/mntS3', description="集群S3存储桶挂载在本地的根目录")
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Cloudfront_cdn_endpoint: str = Field(default='https://d2nj71io21vkj2.cloudfront.net', description="集群挂载S3存储桶的对应AWS Cloudfront CDN")
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Cloudfront_cdn_endpoint: str = Field(default='https://d2nj71io21vkj2.cloudfront.net',
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description="集群挂载S3存储桶的对应AWS Cloudfront CDN")
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S3_cdn_endpoint: str = Field(default="https://cdn.roasmax.cn",
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description="优先使用的集群挂载S3存储桶的对应CDN")
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modal_kv_name: str = Field(default='media-cache', description="Modal视频缓存KV库")
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@ -22,6 +23,12 @@ class WorkerConfig(BaseSettings):
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comfyui_s3_input: Optional[str] = Field(default="comfyui-input", description="ComfyUI input S3文件夹名")
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comfyui_s3_output: Optional[str] = Field(default="comfyui-output", description="ComfyUI output S3文件夹名")
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langfuse_host: HttpUrl = Field(default="https://us.cloud.langfuse.com", description="Langfuse host endpoint")
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langfuse_public_key: str = Field(default="pk-lf-15f9d809-0bf6-4a84-ae1c-18f7a7d927c7",
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description="Langfuse public key")
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langfuse_secret_key: str = Field(default="sk-lf-dd20cb0b-ef2e-49f6-80f0-b2d9cff1bb11",
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description="Langfuse secret key")
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api_version: str = Field(default="0.1.7", description="API接口版本")
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modal_config: Any = SettingsConfigDict(json_schema_extra={
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@ -105,6 +105,7 @@ class HTTPDownloadUtils:
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return await asyncio.gather(*tasks, return_exceptions=True)
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import jsonref
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from google.oauth2 import service_account
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from google.genai import types
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@ -118,7 +119,8 @@ class GoogleAuthUtils:
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class VertexAIRequestModel(types._common.BaseModel):
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contents: Union[types.ContentListUnion, types.ContentListUnionDict] = Field()
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generation_config: Optional[types.GenerateContentConfig] = Field(default=None, alias="generationConfig",
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serialization_alias="generationConfig")
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serialization_alias="generationConfig",
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description="如果在JSON序列化时报MetadataClass")
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@field_validator("generation_config", mode="before")
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def validate_generation_config(cls, v):
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@ -126,7 +128,8 @@ class GoogleAuthUtils:
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return None
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elif isinstance(v, types.GenerateContentConfig):
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if issubclass(v.response_schema, BaseModel):
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v.response_schema = v.response_schema.model_json_schema()
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flat_schema = v.response_schema.model_json_schema(schema_generator=FlatJsonSchemaGenerator)
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v.response_schema = flat_schema
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return v
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elif isinstance(v.response_schema, Dict):
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return v
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@ -163,8 +166,8 @@ class GoogleAuthUtils:
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headers={"Content-Type": "application/json",
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"Authorization": f"Bearer {self.access_token}"},
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content=json_body)
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response.raise_for_status()
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logger.info(response.text)
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response.raise_for_status()
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return types.GenerateContentResponse.model_validate_json(response.text)
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@staticmethod
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@ -199,3 +202,34 @@ class GoogleAuthUtils:
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# upload_url = f"gs://dy-media-storage/video/{filename}"
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return GoogleUploadResponse.model_validate_json(upload_response.text)
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from pydantic.json_schema import GenerateJsonSchema, JsonSchemaMode
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from pydantic import BaseModel, Field
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class FlatJsonSchemaGenerator(GenerateJsonSchema):
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def generate(self, schema, mode: JsonSchemaMode = 'validation'):
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# 先用原始生成器生成 schema
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schema_dict = super().generate(schema, mode=mode)
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# 展开 $ref
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return self._flatten_refs(schema_dict)
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def _flatten_refs(self, schema, defs=None):
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if defs is None:
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defs = schema.get('$defs', {})
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if isinstance(schema, dict):
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if '$ref' in schema:
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ref_path = schema['$ref']
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# 只支持本地 $defs 引用
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if ref_path.startswith('#/$defs/'):
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def_name = ref_path.split('/')[-1]
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return self._flatten_refs(defs[def_name], defs)
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else:
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raise ValueError(f'Unsupported $ref: {ref_path}')
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else:
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return {k: self._flatten_refs(v, defs) for k, v in schema.items() if k != '$defs'}
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elif isinstance(schema, list):
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return [self._flatten_refs(i, defs) for i in schema]
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else:
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return schema
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@ -1,15 +1,47 @@
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import json
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import os
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import unittest
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from typing import Optional, List
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import jsonref
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from loguru import logger
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from google.genai import types
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from pydantic import BaseModel
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from BowongModalFunctions.utils.HTTPUtils import GoogleAuthUtils
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from pydantic import BaseModel, Field, computed_field
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from langfuse import Langfuse
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from jinja2 import Template
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from BowongModalFunctions.utils.HTTPUtils import GoogleAuthUtils, FlatJsonSchemaGenerator
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class BaseResponse(BaseModel):
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message: str
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class VisualFeatureColor(BaseModel):
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pattern: str = Field(description="商品详细图案纹理等有辨识度的材质特征")
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style: str = Field(description="商品详细款式版型等有辨识度的风格特征")
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class VisualFeature(BaseModel):
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color: VisualFeatureColor = Field(description="商品详细颜色配色等有辨识度的色彩特征")
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class VisualRecognizeResult(BaseModel):
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image_order: int = Field(description="图片的顺序, 从0开始")
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image_name: str = Field(description="图片上显示的原始文字")
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matched_product: Optional[str] = Field(description="匹配到的标准商品名称或null")
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match_confidence: int = Field(description="0到100的可信度评分")
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visual_features: List[VisualFeature] = Field(description="所有识别到的商品详细颜色配色等有辨识度的色彩特征")
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class VisualRecognizeResults(BaseModel):
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results: VisualRecognizeResult = Field(description="每一个图片识别的结果")
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image_count: int = Field(description="输入待识别图片的总数")
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product_count: int = Field(description="输入待识别商品的总数")
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class PromptVariables(BaseModel):
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product_list: List[str] = Field(description="商品列表")
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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 GoogleTestCase(unittest.IsolatedAsyncioTestCase):
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@ -64,18 +96,7 @@ class GoogleTestCase(unittest.IsolatedAsyncioTestCase):
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google_project_id=self.service_account_info.get('project_id'),
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regions=['us-central1'], access_token=cred.access_token,
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)
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result = client.generate_content(model_id="gemini-2.5-flash",
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contents=[types.Content(role='user',
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parts=[
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types.Part(file_data=types.FileData(
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mime_type="video/mp4",
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file_uri="gs://dy-media-storage/videos/035b3053-73f8-45b7-9bf8-428df9025608.mp4"
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)),
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types.Part.from_text(
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text="帮我总结一下这个视频里有什么"),
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])],
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config=types.GenerateContentConfig(
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temperature=0.1,
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config = types.GenerateContentConfig(temperature=0.1,
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top_p=0.7,
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safety_settings=[
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types.SafetySetting(
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@ -100,11 +121,140 @@ class GoogleTestCase(unittest.IsolatedAsyncioTestCase):
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)
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],
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response_mime_type="application/json",
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response_schema=BaseResponse
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))
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response_schema=VisualRecognizeResult)
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result = client.generate_content(model_id="gemini-2.5-flash",
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contents=[types.Content(role='user',
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parts=[
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types.Part(file_data=types.FileData(
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mime_type="video/mp4",
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file_uri="gs://dy-media-storage/videos/035b3053-73f8-45b7-9bf8-428df9025608.mp4"
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)),
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types.Part.from_text(
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text="帮我总结一下这个视频里有什么"),
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])],
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config=config)
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logger.info(result.model_dump_json(indent=2, exclude_none=True))
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self.assertIsNotNone(result)
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async def test_google_save_prompt(self):
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config = types.GenerateContentConfig(temperature=0.1, 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=VisualRecognizeResults.model_json_schema(
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schema_generator=FlatJsonSchemaGenerator)
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)
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logger.info(config.model_dump_json(indent=2))
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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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prompt = """
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<prompt><instruction>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。 **输入材料**： - 🖼️ **商品图片网格**：包含多个黑色边框区域，每个区域内有商品图片+商品名称文字 - 📋 **商品列表**：标准商品名称参考清单 **核心任务**： 1. **扫描黑色边框区域**：从左上角开始，按行扫描每个黑色边框区域 2. **提取文字信息**：精确提取每个区域内的所有文字信息 3. **与商品列表匹配**：将图片文字与商品列表进行高相似度匹配 4. **提取商品图片特征**：从商品图片提取详细可识别特征，包括颜色、图案、纹理、材质、版型、款式等 **严格约束**： - 🚫 只识别有黑色边框包围的商品区域 - 🚫 每个商品必须有清晰可见的文字标注 - 🚫 不得推测或添加图片中不存在的商品 - ✅ 输出商品数量不得超过图片中的黑色边框区域数量 **商品列表**： {{PRODUCT_LIST}}
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</instruction></prompt>
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"""
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prompt = langfuse.create_prompt(name="Gemini自动切条", prompt=prompt, type="text", labels=["production"],
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config=config.model_dump(exclude_none=True))
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logger.info(prompt)
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async def test_google_get_prompt(self):
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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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product_title_list = [
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"A美洋MEIYANG【商场同款】碧螺春墨镜 醋酸纤维素防晒太阳眼镜-周四",
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"A美洋MEIYANG【欧若风】微风背心 慵懒百搭圆领无袖上衣-周二",
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"A美洋MEIYANG【商场同款】幸运T恤 复古做旧印花圆领短袖上衣-周四",
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"A美洋MEIYANG 黑武士厚底老爹鞋 赛博末日风~厚底增高运动休闲鞋",
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"合金项链 A美洋MEIYANG 香水瓶毛衣链 个性吊坠麻花链项链-周四",
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"A美洋MEIYANG【商场同款】纱暮半裙 抗起球镂空蕾丝半身中长裙-周四",
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]
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variables = PromptVariables(product_list=product_title_list, )
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latest_prompt = langfuse.get_prompt("Gemini自动切条", type="text", label="latest")
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logger.info(f"variables={latest_prompt.variables}")
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runtime_prompt = Template(latest_prompt.prompt).render(PRODUCT_LIST=variables.product_list_xml)
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cred = await GoogleAuthUtils.get_google_auth_jwt(service_account_info=self.service_account_info,
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scopes=[
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'https://www.googleapis.com/auth/cloud-platform'])
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logger.info(cred.model_dump_json(indent=2))
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client = GoogleAuthUtils.GoogleGenaiClient(
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cloudflare_project_id=self.cloudflare_project_id,
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cloudflare_gateway_id=self.cloudflare_gateway_id,
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google_project_id=self.service_account_info.get('project_id'),
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regions=['us-central1'], access_token=cred.access_token,
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)
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config = types.GenerateContentConfig.model_validate(latest_prompt.config)
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result = client.generate_content(model_id="gemini-2.5-flash",
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contents=[types.Content(role='user',
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parts=[
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_56fe257b-f81f-4ad0-8958-530ad557b876.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_f74b4c4f-a305-4a96-9045-f09e0eb90a30.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_e4c10111-e9ec-4e76-88db-abd57b3bb92e.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_52b73a7d-f5e0-4a17-b7c2-194ef3852856.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_f8d8c35e-1c35-48c4-b9ce-df6365b7fc55.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_b9132a81-f4b6-45f7-8b37-c0caaee06705.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_d7ef44fc-8767-405d-a9dc-f16e4a918efd.jpg"
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)),
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types.Part(file_data=types.FileData(
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mime_type="image/jpeg",
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file_uri="gs://dy-media-storage/images/grid_564b9ff9-fa3a-4d34-9592-038d454f0834.jpg"
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)),
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types.Part.from_text(text=runtime_prompt, ),
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])],
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config=config)
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logger.info(result.model_dump_json(indent=2, exclude_none=True))
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json_result = result.candidates[0].content.parts[0].text
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result_model = VisualRecognizeResults.model_validate_json(json_result)
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logger.info(result_model.model_dump_json(indent=2, exclude_none=True))
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async def test_flat_json_schema(self):
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json_schema = VisualRecognizeResults.model_json_schema(schema_generator=FlatJsonSchemaGenerator)
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logger.info(json.dumps(json_schema, indent=2, ensure_ascii=False))
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if __name__ == '__main__':
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unittest.main()
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254
uv.lock
254
uv.lock
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@ -204,6 +204,8 @@ dependencies = [
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{ name = "httpx" },
|
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[[package]]
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||||
name = "xmltodict"
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version = "0.14.2"
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|
|
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|||
Loading…
Reference in New Issue