feat（新功能）: 项目信息提取/生成接口

fix（修复bug）:
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/api/api_extraction_project_info.py

@@ -4,7 +4,7 @@ from fastapi import APIRouter, HTTPException
 
 from app.schemas.project_info_extraction import ProjectInfoExtractionModel
 from app.schemas.response_template import ResponseModel
-from app.service.project_info_extraction.service_generate_brand_info import ProjectInfoExtraction
+from app.service.project_info_extraction.service import ProjectInfoExtraction
 
 router = APIRouter()
 logger = logging.getLogger()

app/service/project_info_extraction/service.py

@@ -0,0 +1,61 @@
+from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+from langchain_community.chat_models import ChatTongyi
+from langchain_core.prompts import PromptTemplate
+
+from app.schemas.project_info_extraction import ProjectInfoExtractionModel
+
+style = ['NEW_CHINESE', 'COUNTRY_STYLE', 'FUTURISM', 'MINIMALISM', 'LOLITA', 'Y2K', 'BUSINESS', 'MERLAD',
+         'OUTDOOR_FUNCTIONAL', 'ROCK', 'DOPAMINE', 'GOTHIC', 'POST_APOCALYPTIC', 'ROMANTIC', 'WABI_SABI']
+position = ['Overall', 'Tops', 'Bottoms', 'Outwear', 'Blouse', 'Dress', 'Trousers', 'Skirt']
+gender = ['Female', 'Male']
+age_group = ['Adult', 'Child']
+process = ['SERIES_DESIGN', 'SINGLE_DESIGN']
+
+
+class ProjectInfoExtraction:
+    def __init__(self, request_data):
+        # llm generate brand info init
+        self.model = ChatTongyi(model="qwen2.5-14b-instruct", api_key="sk-7658298c6b99443c98184a5e634fe6ab")
+
+        self.response_schemas = [
+            ResponseSchema(name="project_name", description="项目的名称."),
+            ResponseSchema(name="process", description="项目的类型，单品还是系列."),
+            ResponseSchema(name="ageGroup", description="项目设计服装的受众对象."),
+            ResponseSchema(name="gender", description="项目设计服装的受众性别."),
+            ResponseSchema(name="position", description="项目单品设计的部位."),
+            ResponseSchema(name="style", description="项目的设计风格.")
+        ]
+        self.output_parser = StructuredOutputParser.from_response_schemas(self.response_schemas)
+        self.format_instructions = self.output_parser.get_format_instructions()
+        self.prompt = PromptTemplate(
+            template="你是一个时装品牌的设计师助理。根据用户输入提取出"
+                     "[project_name] : 项目的名称,"
+                     f"[process] : 项目的类型，从{process}选择."
+                     f"[ageGroup] : 服装的受众，从{age_group}选择."
+                     f"[gender] : 服装的适用性别，从{gender}选择"
+                     f"[position] : single_design的部位，如果[process]是SINGLE_DESIGN,从{position}中选择，如果[process]是SERIES_DESIGN，这项为空"
+                     f"[style] : 设计的风格，从{style}中选择"
+                     ".\n{format_instructions}\n{question}",
+            input_variables=["question"],
+            partial_variables={"format_instructions": self.format_instructions}
+        )
+        self._input = self.prompt.format_prompt(question=request_data.prompt)
+
+        self.result_data = {}
+
+    def get_result(self):
+        self.llm_extraction_project_info()
+        return self.result_data
+
+    def llm_extraction_project_info(self):
+        output = self.model(self._input.to_messages())
+        project_info = self.output_parser.parse(output.content)
+        self.result_data = project_info
+
+
+if __name__ == '__main__':
+    request_data = ProjectInfoExtractionModel(
+        prompt="海边派对主题的衬衫设计"
+    )
+    service = ProjectInfoExtraction(request_data)
+    print(service.get_result())

app/service/project_info_extraction/service_generate_brand_info.py

@@ -1,47 +0,0 @@
-from langchain.output_parsers import ResponseSchema, StructuredOutputParser
-from langchain_community.chat_models import ChatTongyi
-from langchain_core.prompts import PromptTemplate
-
-from app.schemas.project_info_extraction import ProjectInfoExtractionModel
-
-
-class ProjectInfoExtraction:
-    def __init__(self, request_data):
-        # llm generate brand info init
-        self.model = ChatTongyi(model="qwen2.5-14b-instruct", api_key="sk-7658298c6b99443c98184a5e634fe6ab")
-
-        self.response_schemas = [
-            ResponseSchema(name="project_name", description="project name."),
-            ResponseSchema(name="role", description="The target role of the project."),
-            ResponseSchema(name="gender", description="The gender targeted by the project."),
-            ResponseSchema(name="style", description="Project style.")
-        ]
-        self.output_parser = StructuredOutputParser.from_response_schemas(self.response_schemas)
-        self.format_instructions = self.output_parser.get_format_instructions()
-        self.prompt = PromptTemplate(
-            template="你是一个时装品牌的设计师助理。根据用户输入提取出project_name,role ,gender ,style ."
-                     "gender部分请用以下:menswear,womenswear,childrenwear,如果全部都适用即all."
-                     "如果没有以上内容，需要你根据用户输入随意发挥.\n{format_instructions}\n{question}",
-            input_variables=["question"],
-            partial_variables={"format_instructions": self.format_instructions}
-        )
-        self._input = self.prompt.format_prompt(question=request_data.prompt)
-
-        self.result_data = {}
-
-    def get_result(self):
-        self.llm_extraction_project_info()
-        return self.result_data
-
-    def llm_extraction_project_info(self):
-        output = self.model(self._input.to_messages())
-        project_info = self.output_parser.parse(output.content)
-        self.result_data = project_info
-
-
-if __name__ == '__main__':
-    request_data = ProjectInfoExtractionModel(
-        prompt="海边派对主题的系列设计"
-    )
-    service = ProjectInfoExtraction(request_data)
-    print(service.get_result())