feat：update chatbot sys prompt
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@@ -36,5 +36,5 @@ jobs:
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docker-compose down 2>&1
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docker-compose up -d --build --remove-orphans 2>&1
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docker image prune -f 2>&1
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# docker image prune -f 2>&1
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@@ -1,9 +1,13 @@
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import logging
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import sys
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import litserve as ls
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from typing import AsyncGenerator
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from google import genai
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from pydantic import BaseModel
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from sympy.core.evalf import rnd
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from app.config import settings
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from google.genai import types
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@@ -95,42 +99,61 @@ class LCChatBot(ls.LitAPI):
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if __name__ == "__main__":
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sys.stdout = open('permanent.log', 'w', encoding='utf-8')
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import asyncio
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async def run_simple_test():
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async def run_simple_test(text):
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"""
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一个简单的异步测试用例，用于测试 LCChatBot 的流式输出。
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"""
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print("\n" + "=" * 50)
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print("--- 🔬 开始 LCChatBot 简单流式测试 ---")
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# print("--- 🔬 开始 LCChatBot 简单流式测试 ---")
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# 1. 初始化 LitAPI 和其依赖
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chatbot_api = LCChatBot()
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chatbot_api.setup(device="cpu")
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print("✅ Setup complete. Mock services initialized.")
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chatbot_api.setup(device="cpu")
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# print("✅ Setup complete. Mock services initialized.")
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# 2. 构造请求数据
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request_data = PredictRequest(
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user_id="simple_user",
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session_id="simple_session",
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user_message="I want an outfit. I am going to a evening party with friends. Suggest something stylish yet comfortable.",
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user_message=text,
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gender="female"
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)
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chatbot_api.redis.clear_history(request_data.session_id)
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print(f"-> 正在发送查询: {request_data.user_message}")
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print(f"user: \n {request_data.user_message}")
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# 3. 调用 predict 方法并处理流
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response_generator = chatbot_api.predict(request_data)
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print("\n<- 接收流式响应:")
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print("agent:")
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# 4. 异步迭代生成器，实时打印输出
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async for chunk in response_generator:
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print(chunk, end="", flush=True)
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print("\n" + "=" * 50)
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# 启动异步事件循环
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try:
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asyncio.run(run_simple_test())
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except Exception as e:
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print(f"\n发生致命错误: {e}")
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text_list = [
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'我要去参加好朋友的婚礼，你能帮我挑一套衣服吗？',
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'I need something to wear for a big presentation at work tomorrow. I want to look powerful but still approachable.',
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'Who do you think is the best world leader right now?',
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'I’m going on a trip to Paris next week and need some outfits.',
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'Help me find a cool outfit for a rock concert. I hate wearing dresses.',
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'I want to look very cool, 或者是那种很有个性的风格 for a gallery opening.',
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"I'm going to a gala. Please list 5 different dress styles for me and use bold text for the names.",
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"I'm feeling really sad today and just want an outfit that matches my mood."
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]
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for text in text_list:
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asyncio.run(run_simple_test(text))
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# print("\n" + "=" * 50)
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# # 启动异步事件循环
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# try:
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# asyncio.run(run_simple_test())
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# except Exception as e:
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# print(f"\n发生致命错误: {e}")
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#
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sys.stdout.close()
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@@ -1,4 +1,4 @@
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BASIC_PROMPT = """You are a professional, friendly, and insightful AI {gender}'s styling assistant.
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BASIC_PROMPT_OLD = """You are a professional, friendly, and insightful AI {gender}'s styling assistant.
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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.
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@@ -21,6 +21,39 @@ Example Follow-up (mimicking a conversational flow):
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User: I want a chic outfit for dinner.
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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?"""
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BASIC_PROMPT = """
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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.
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CONVERSATION GOALS:
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1. **Occasion:** Determine the specific event (e.g., romantic dinner, summer wedding).
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2. **Style:** Pinpoint the desired aesthetic (e.g., minimalist, bohemian).
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3. **Vibe/Details:** Gather mood or constraints (e.g., comfort, specific colors).
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4. **Item Preference:** Identify preferences for silhouettes or specific items.
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PERSONALITY AND COMMUNICATION RULES:
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- 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.
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- Keep responses extremely SHORT (maximum 2 sentences).
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- Use clear, modern, and vivid language. Avoid clichés or corporate jargon.
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- Be positive and encouraging, never patronizing.
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- When uncertain, state assumptions and invite correction.
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- Gather sufficient intent before providing recommendations. Ask only 1 to 2 essential questions per turn.
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IMPORTANT GUARDRAILS:
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- ONLY help with styling ideas and outfit generation.
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- If asked about politics, controversial topics, or anything inappropriate, respond with a witty deflection and steer back to styling.
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OUTPUT FORMAT INSTRUCTION:
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- **DO NOT** use any Markdown formatting whatsoever (no asterisks, bold text, lists, or code blocks).
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- **ONLY** output the plain text response spoken by the AI Assistant.
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EXAMPLE DIALOGUES:
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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.
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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?
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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?
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"""
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SUMMARY_PROMPT = """
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You are an expert fashion request analyzer. Analyze the conversation history provided by the user.
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Your task is to:
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@@ -31,8 +64,8 @@ Your task is to:
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Extract this information accurately from the chat history.
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"""
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from app.taxonomy import FASHION_TAXONOMY, IGNORE_SUBCATEGORY, ALL_SUBCATEGORY_LIST
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core_outfit_template = f"""
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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.
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@@ -96,7 +129,6 @@ You must strictly use the **JSON format** for your output, as follows:
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Now, please start building an outfit (with strictly unique categories for all items) and output the JSON for the first item.
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"""
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accessories_template = f"""
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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.
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@@ -199,7 +231,7 @@ def build_iterative_schema(current_category):
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"enum": FASHION_TAXONOMY[current_category]
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},
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"description": {
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"type": "string",
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"type": "string",
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"description": "an **extremely detailed and precise** description of the item. This description is used for **high-accuracy vector search** in the database. It should include Color, Fit/Silhouette, Material/Detail, Role in the Outfit."
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},
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"reason": {"type": "string", "description": "The reason for the current action. Required if action is 'stop' (to summarize the final outfit)."}
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@@ -209,8 +241,8 @@ def build_iterative_schema(current_category):
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return schema
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def build_batch_schema(specified_category: str=""):
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assert(specified_category in FASHION_TAXONOMY.keys() or specified_category == "")
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def build_batch_schema(specified_category: str = ""):
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assert (specified_category in FASHION_TAXONOMY.keys() or specified_category == "")
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if not specified_category:
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category_range_desc = "the complete final outfit (including all categories)"
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subcategory_list = ALL_SUBCATEGORY_LIST
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