feat：update chatbot sys prompt

.gitea/workflows/prod_build_manual.yaml

@@ -37,4 +37,4 @@ jobs:
             docker-compose down 2>&1
             docker-compose up -d --build --remove-orphans 2>&1
 
-            docker image prune -f 2>&1
+#            docker image prune -f 2>&1

app/server/ChatbotAgent/chatbot_server.py

@@ -1,9 +1,13 @@
 import logging
+import sys
+
 import litserve as ls
 
 from typing import AsyncGenerator
 from google import genai
 from pydantic import BaseModel
+from sympy.core.evalf import rnd
+
 from app.config import settings
 from google.genai import types
 
@@ -95,42 +99,61 @@ class LCChatBot(ls.LitAPI):
 
 
 if __name__ == "__main__":
+    sys.stdout = open('permanent.log', 'w', encoding='utf-8')
+
     import asyncio
-    async def run_simple_test():
+
+
+    async def run_simple_test(text):
         """
         一个简单的异步测试用例，用于测试 LCChatBot 的流式输出。
         """
         print("\n" + "=" * 50)
-        print("--- 🔬 开始 LCChatBot 简单流式测试 ---")
+        # print("--- 🔬 开始 LCChatBot 简单流式测试 ---")
 
         # 1. 初始化 LitAPI 和其依赖
         chatbot_api = LCChatBot()
         chatbot_api.setup(device="cpu")
-        print("✅ Setup complete. Mock services initialized.")
+        # print("✅ Setup complete. Mock services initialized.")
 
         # 2. 构造请求数据
         request_data = PredictRequest(
             user_id="simple_user",
             session_id="simple_session",
-            user_message="I want an outfit. I am going to a evening party with friends. Suggest something stylish yet comfortable.",
+            user_message=text,
             gender="female"
         )
         chatbot_api.redis.clear_history(request_data.session_id)
 
-        print(f"-> 正在发送查询: {request_data.user_message}")
+        print(f"user: \n  {request_data.user_message}")
 
         # 3. 调用 predict 方法并处理流
         response_generator = chatbot_api.predict(request_data)
 
-        print("\n<- 接收流式响应:")
+        print("agent:")
 
         # 4. 异步迭代生成器，实时打印输出
         async for chunk in response_generator:
             print(chunk, end="", flush=True)
 
-    print("\n" + "=" * 50)
+
-    # 启动异步事件循环
+    text_list = [
-    try:
+        '我要去参加好朋友的婚礼，你能帮我挑一套衣服吗？',
-        asyncio.run(run_simple_test())
+        'I need something to wear for a big presentation at work tomorrow. I want to look powerful but still approachable.',
-    except Exception as e:
+        'Who do you think is the best world leader right now?',
-        print(f"\n发生致命错误: {e}")
+        'I’m going on a trip to Paris next week and need some outfits.',
+        'Help me find a cool outfit for a rock concert. I hate wearing dresses.',
+        'I want to look very cool, 或者是那种很有个性的风格 for a gallery opening.',
+        "I'm going to a gala. Please list 5 different dress styles for me and use bold text for the names.",
+        "I'm feeling really sad today and just want an outfit that matches my mood."
+    ]
+    for text in text_list:
+        asyncio.run(run_simple_test(text))
+    # print("\n" + "=" * 50)
+    # # 启动异步事件循环
+    # try:
+    #     asyncio.run(run_simple_test())
+    # except Exception as e:
+    #     print(f"\n发生致命错误: {e}")
+    #
+    sys.stdout.close()

app/server/ChatbotAgent/core/prompt.py

@@ -1,4 +1,4 @@
-BASIC_PROMPT = """You are a professional, friendly, and insightful AI {gender}'s styling assistant.
+BASIC_PROMPT_OLD = """You are a professional, friendly, and insightful AI {gender}'s styling assistant.
 
 Your primary mission is to engage in a multi-turn conversation with the user to fully understand their dressing intent. You must adopt a professional yet approachable tone.
 
@@ -21,6 +21,39 @@ Example Follow-up (mimicking a conversational flow):
 User: I want a chic outfit for dinner.
 Your Response: Hey there! A chic dinner outfit, I love that! To give you the perfect recommendations, tell me: is this a romantic date, business dinner, or celebration with friends? And what's your go-to style vibe: classic elegance or something with more edge?"""
 
+BASIC_PROMPT = """
+You are a professional, friendly, and insightful AI {gender}'s styling assistant. You are smart, young, and enthusiastic, turning styling into an exciting experience. Your tone is warm, confident, composed, and genuinely curious about the user's context.
+
+CONVERSATION GOALS:
+    1.  **Occasion:**  Determine the specific event (e.g., romantic dinner, summer wedding).
+    2.  **Style:** Pinpoint the desired aesthetic (e.g., minimalist, bohemian).
+    3.  **Vibe/Details:** Gather mood or constraints (e.g., comfort, specific colors).
+    4.  **Item Preference:** Identify preferences for silhouettes or specific items.
+
+PERSONALITY AND COMMUNICATION RULES:
+    -   Always reply in English only. If the user speaks another language, state in English that you cannot speak that language and steer back to styling.
+    -   Keep responses extremely SHORT (maximum 2 sentences).
+    -   Use clear, modern, and vivid language. Avoid clichés or corporate jargon.
+    -   Be positive and encouraging, never patronizing.
+    -   When uncertain, state assumptions and invite correction.
+    -   Gather sufficient intent before providing recommendations. Ask only 1 to 2 essential questions per turn.
+
+IMPORTANT GUARDRAILS:
+    -   ONLY help with styling ideas and outfit generation.
+    -   If asked about politics, controversial topics, or anything inappropriate, respond with a witty deflection and steer back to styling.
+
+OUTPUT FORMAT INSTRUCTION:
+-   **DO NOT** use any Markdown formatting whatsoever (no asterisks, bold text, lists, or code blocks).
+-   **ONLY** output the plain text response spoken by the AI Assistant.
+
+EXAMPLE DIALOGUES:
+    User: 我想找件衣服参加婚礼。 Response: I am so sorry, but I can only speak English! I would love to help you find the perfect wedding guest look if you can tell me the dress code or the venue vibe.
+
+    User: I need a chic outfit for dinner. Response: That sounds like a fabulous evening! Is this a romantic date or a casual night out with friends, and do you prefer a sleek dress or tailored separates?
+
+    User: What do you think about the current election? Response: I prefer to stick to the polls of fashion rather than politics! Tell me, what kind of style aesthetic are we aiming for today?
+"""
+
 SUMMARY_PROMPT = """
 You are an expert fashion request analyzer. Analyze the conversation history provided by the user.
 Your task is to:
@@ -31,8 +64,8 @@ Your task is to:
 Extract this information accurately from the chat history.
 """
 
-
 from app.taxonomy import FASHION_TAXONOMY, IGNORE_SUBCATEGORY, ALL_SUBCATEGORY_LIST
+
 core_outfit_template = f"""
 You are a professional fashion stylist Agent, specialized in creating complete, tailored outfits for {{gender}}. Your current task is to recommend items for the **{{current_category}}** stage, strictly **mimicking the style and preference** specified in the following Stylist Guide.
 
@@ -96,7 +129,6 @@ You must strictly use the **JSON format** for your output, as follows:
 Now, please start building an outfit (with strictly unique categories for all items) and output the JSON for the first item.
 """
 
-
 accessories_template = f"""
 You are a professional fashion stylist Agent, specialized in creating complete, tailored outfits for {{gender}}. Your current task is to finalize the look by recommending accessories for the **{{current_category}}** stage, strictly **mimicking the style and preference** specified in the following Accessories Guide.
 
@@ -209,8 +241,8 @@ def build_iterative_schema(current_category):
     return schema
 
 
-def build_batch_schema(specified_category: str=""):
+def build_batch_schema(specified_category: str = ""):
-    assert(specified_category in FASHION_TAXONOMY.keys() or specified_category == "")
+    assert (specified_category in FASHION_TAXONOMY.keys() or specified_category == "")
     if not specified_category:
         category_range_desc = "the complete final outfit (including all categories)"
         subcategory_list = ALL_SUBCATEGORY_LIST