1

app/core/config.py

@@ -31,7 +31,7 @@ class Settings(BaseSettings):
     STYLIST_GUIDE_DIR: str = Field(default="/workspace/lc_stylist_agent/app/core/data/stylist_guide", description="风格指南文本目录")
 
     # 向量数据库配置参数
-    VECTOR_DB_DIR: str = Field(default="./app/core/data/db", description="向量数据库目录")
+    VECTOR_DB_DIR: str = Field(default="./app/db", description="向量数据库目录")
     COLLECTION_NAME: str = Field(default="lc_clothing_embedding", description="向量数据库集合名称")
     EMBEDDING_MODEL_NAME: str = Field(default="openai/clip-vit-base-patch32", description="CLIP嵌入模型名称")
 

app/main.py

@@ -23,4 +23,4 @@ if __name__ == "__main__":
     chat_boot_api = LCChatBot(enable_async=True, stream=True, api_path='/api/v1/chatbot')
     agent_api = LCAgent(enable_async=True, api_path='/api/v1/agent')
     server = ls.LitServer([chat_boot_api, agent_api])
-    server.run(port=8001)
+    server.run(port=8000)

app/request.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "aida-461108",
+  "private_key_id": "b4afaabebb84da24502b318a5fa175f1dc5c096a",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nqlXBUbmHHzamo5DPYL8S/w==\n-----END PRIVATE KEY-----\n",
+  "client_email": "aida-239@aida-461108.iam.gserviceaccount.com",
+  "client_id": "103102077955178349079",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/aida-239%40aida-461108.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

app/server/ChatbotAgent/agent_server.py

@@ -1,6 +1,7 @@
 import asyncio
 import json
 import logging
+import os
 import uuid
 from typing import List, Dict
 
@@ -33,7 +34,7 @@ class LCAgent(ls.LitAPI):
             key_prefix=settings.REDIS_HISTORY_KEY_PREFIX
         )
         self.vector_db = VectorDatabase(
-            vector_db_dir=settings.VECTOR_DB_DIR,
+            vector_db_dir=os.getenv('VECTOR_DB_DIR', '/app/app/db'),
             collection_name=settings.COLLECTION_NAME,
             embedding_model_name=settings.EMBEDDING_MODEL_NAME
         )
@@ -44,6 +45,16 @@ class LCAgent(ls.LitAPI):
         }
 
     async def decode_request(self, request: AgentRequestModel):
+        """
+        [自动生成文档]
+        该方法将 HTTP 请求体解码为模型所需的输入格式。
+
+        Args:
+            request: 包含 prompt, history, temperature 的 ChatBotRequest 对象。
+
+        Returns:
+            prompt, history, temperature
+        """
         logger.info(f"request: {request.model_dump()}")
         return request
 
@@ -81,10 +92,12 @@ class LCAgent(ls.LitAPI):
         history_messages = self.redis.get_history(user_id)
         input_message = "\n".join([f"{msg.role.value}: {msg.content}" for msg in history_messages])
         # 临时调用 LLM 或使用本地逻辑生成总结
-        summary = await self.llm.generate_response(history=[Message(role=Role.USER, content=input_message)], system_prompt=SUMMARY_PROMPT)
+        summary = await self.llm.generate_response(history=[Message(role=Role.USER, content=input_message)],
+                                                   system_prompt=SUMMARY_PROMPT)
         return summary
 
-    async def recommend_outfit(self, request_summary: str, stylist_name: str, start_outfit=None, num_outfits: int = 1, user_id: str = "test"):
+    async def recommend_outfit(self, request_summary: str, stylist_name: str, start_outfit=None, num_outfits: int = 1,
+                               user_id: str = "test"):
         """
         基于用户的对话历史和需求，推荐一套搭配。