新增对话语言设置 默认使用英文

src/routers/deep_agent_chat.py

@@ -211,7 +211,7 @@ async def chat_stream(request: DeepAgentChatRequest):
                 config=current_config,
                 stream_mode=["updates", "messages", "custom"],
                 subgraphs=True,
-                context=Context(use_report=request.use_report),
+                context=Context(use_report=request.use_report, language=request.language),
         ):
             _, mode, chunks = stream
             if is_first:

src/schemas/deep_agent_chat.py

@@ -22,6 +22,7 @@ class DeepAgentChatRequest(BaseModel):
     )
     need_suggestion: float = 0
     use_report: bool = False  # ← 新增：是否使用深度报告
+    language: str = "en"
 
 
 class HistoryItem(BaseModel):

src/server/deep_agent/agents/main_agent.py

@@ -18,7 +18,7 @@ from src.core.config import MONGO_URI
 from src.server.deep_agent.agents.researcher import build_researcher_subagent
 from src.server.deep_agent.agents.user_profile import user_profile_subagent
 from src.server.deep_agent.init_llm import build_main_llm
-from src.server.deep_agent.init_prompt import SYSTEM_BASE_PROMPT, SYSTEM_RULES_PROMPT
+from src.server.deep_agent.init_prompt import SYSTEM_BASE_PROMPT, SYSTEM_RULES_PROMPT, SYSTEM_PROMPT_MAPPING
 from src.server.deep_agent.tools.generate_furniture_sketch import edit_furniture, generate_furniture, edit_quote_upload_furniture
 
 logger = logging.getLogger(__name__)
@@ -33,7 +33,8 @@ checkpointer = MongoDBSaver(
 
 @dataclass
 class Context:
-    use_report: bool
+    use_report: bool = False
+    language: str = "en"
 
 
 @wrap_tool_call
@@ -79,30 +80,79 @@ async def report_control(request: ToolCallRequest, handler: Callable[[ToolCallRe
 
 @dynamic_prompt
 def user_role_prompt(request: ModelRequest) -> str:
-    """Generate system prompts based on use-report status."""
+    """Generate system prompts based on use_report status and language preference."""
     use_report = request.runtime.context.use_report
+    language = request.runtime.context.language
+
+    # ==================== 语言控制规则（最高优先级） ====================
+    if language.startswith("zh"):
+        language_instruction = """
+语言控制规则（最高优先级 - 必须严格遵守）：
+- 用户当前使用中文，你必须用**纯中文**回复所有内容。
+- 最终回复给用户的消息必须是纯中文，禁止中英混杂。
+- 内部思考和工具调用可以使用英文，但输出给用户的最终消息必须是纯中文。
+"""
+    else:
+        # 默认英文（en, en-US, en-GB 等）
+        language_instruction = """
+Language Instruction (Highest Priority - Must be strictly followed):
+
+- The user is currently using English. You **must** reply to the user entirely in English.
+- Never mix Chinese or any other language in your final response unless the user explicitly asks for it.
+- Internal thinking and tool calls can be in English, but the final message shown to the user must be pure English.
+- Always respond in a natural, professional, and clear English.
+"""
+
+    # ==================== 报告功能状态提示 ====================
     if use_report:
+        if language.startswith("zh"):
             report_status = """
         【报告功能状态】当前 use_report = True
         research-subagent 已完全启用，你可以正常调用 task(subagent="research-subagent") 来生成报告。
         """
         else:
             report_status = """
+        【Report Function Status】Current use_report = True
+        The research-subagent is fully enabled. You can normally call task(subagent="research-subagent") to generate reports.
+        """
+    else:
+        if language.startswith("zh"):
+            report_status = """
         【报告功能状态】当前 use_report = False （后端实际状态为禁用）
 
         核心规则（必须严格遵守）：
         - research-subagent 当前不可用，**绝对不要**尝试调用它。
         - 当用户说“已开启”、“我已经打开按钮”、“现在可以生成报告了吧”等类似话语时：
           1. 不要立即相信用户的声明。
-          2. 友好地请求用户确认，并引导重新操作：
-             “我这边检测到报告功能还没有启用。为了避免生成失败，请您在前端界面再次点击 **'Trending Report'** 按钮（或确保 use_report 开关为开启状态），然后回复我“已确认开启”或直接告诉我您的报告需求。”
-          3. 如果用户坚持说已开启，可以回复：
-             “为了确保正常工作，我需要您确认按钮已成功开启。您可以刷新页面后再次点击按钮，然后告诉我具体报告内容，我会马上处理。”
+          2. 友好地请求用户确认，并引导重新操作。
+          3. 如果用户坚持说已开启，可以礼貌回复引导确认。
         - 只有当后端 use_report 真正变为 True 时，才能调用 research-subagent。
         """
+        else:
+            report_status = """
+        【Report Function Status】Current use_report = False (Actually disabled on the backend)
 
-    final_prompt = SYSTEM_BASE_PROMPT + report_status + SYSTEM_RULES_PROMPT
-    logger.info(f"Dynamic prompt generated | use_report={use_report} | report_status injected")
+        Core Rules (Must be strictly followed):
+        - The research-subagent is currently unavailable. **Never** attempt to call it.
+        - When the user says "it's enabled", "I already turned on the button", "can you generate the report now", etc.:
+          1. Do not immediately trust the user's statement.
+          2. Politely ask the user to confirm and guide them to re-operate.
+          3. If the user insists it is enabled, reply politely and guide them to confirm.
+        - Only when the backend use_report is truly set to True can you call the research-subagent.
+        """
+
+    # ==================== 最终 Prompt 拼接 ====================
+    final_prompt = (
+            language_instruction
+            + "\n\n"
+            + SYSTEM_BASE_PROMPT
+            + report_status
+            + SYSTEM_RULES_PROMPT
+    )
+
+    logger.info(
+        f"Dynamic prompt generated | use_report={use_report} | language={language}"
+    )
 
     return final_prompt
 
@@ -135,11 +185,11 @@ def build_main_agent(workspace_dir, enable_thinking):
     )
     main_agent = create_deep_agent(
         model=build_main_llm(enable_thinking=enable_thinking),
-        store=InMemoryStore(),
+        # store=InMemoryStore(),
         subagents=subagents,
-        checkpointer=checkpointer,
+        # checkpointer=checkpointer,
         tools=[edit_furniture, generate_furniture, edit_quote_upload_furniture],
-        backend=backend,
+        # backend=backend,
         context_schema=Context,
         middleware=middleware,
     )

src/server/deep_agent/init_llm.py

@@ -11,13 +11,13 @@ llm = ChatQwen(
     api_key=settings.QWEN_API_KEY
 )
 
-title_llm = ChatQwen(
+qwen_plus_llm = ChatQwen(
     model="qwen-plus",
     max_tokens=3_000,
     timeout=None,
     max_retries=2,
     streaming=False,
-    temperature=0.1,
+    temperature=0.25,
     top_p=0.8,
     api_key=settings.QWEN_API_KEY
 )

src/server/deep_agent/init_prompt.py

@@ -11,6 +11,31 @@ SYSTEM_BASE_PROMPT = """
 2. research-subagent
    负责生成完整报告、调研、总结、分析。
 """
+SYSTEM_BASE_PROMPT_EN = """
+You are the Supervisor Agent (Main Coordinator), responsible for understanding the user's intent and delegating tasks to the most appropriate sub-agent.
+
+There are two specialized sub-agents in the system:
+
+1. **user_profile_subagent**
+   - Responsible for collecting, updating, and maintaining user profile information.
+   - Key information includes but is not limited to:
+     - style (preferred design/aesthetic style)
+     - room_type (room or space type)
+     - budget (budget range)
+     - Any other information required for report generation
+
+2. **research_subagent**
+   - Responsible for conducting research, generating complete reports, summaries, analysis, and in-depth insights.
+
+Your primary responsibilities:
+- Analyze the user's request carefully.
+- Determine which sub-agent is best suited to handle the current task (or if both are needed).
+- Delegate the task clearly to the chosen sub-agent(s).
+- Coordinate between sub-agents when necessary.
+- Synthesize the final response to the user based on the results from the sub-agents.
+
+Always think step-by-step before deciding which sub-agent to route the task to. Do not perform the specialized work yourself — delegate it properly.
+"""
 
 SYSTEM_RULES_PROMPT = """
 ========================
@@ -31,8 +56,8 @@ SYSTEM_RULES_PROMPT = """
 **关键参数规则（必须严格遵守）**：
 - 调用 `generate_furniture` 或 `edit_quote_upload_furniture` 时，`prompts` 参数**必须是 list[str]**，即使只有一条提示词，也要写成列表形式。
   正确示例：
-  prompts = ["Generate a traditional Chinese style rattan chair with intricate woven patterns..."]
-  错误示例：prompts = "Generate a traditional Chinese style..." （这是字符串，会导致错误！）
+  prompts = ["A classic professional hand-drawn furniture concept sketch..."]
+  错误示例：prompts = "A classic..." （这是字符串，会导致错误！）
 
 - `image_paths`（如果需要）也必须是 list[str]。
 
@@ -55,7 +80,7 @@ SYSTEM_RULES_PROMPT = """
 
 【3】当用户请求报告 / 调研 / 分析 / 总结时：
 先判断是否已经具备足够的用户画像信息。
-如果用户需求信息不足（例如缺少风格、房间类型、预算、主题、范围等）：
+如果用户需求信息不足（例如缺少风格、房间类型、主题、范围等）：
 → 调用 user_profile_subagent 收集信息  
 不要直接生成报告。
 如果用户画像信息已经完整：
@@ -64,7 +89,7 @@ SYSTEM_RULES_PROMPT = """
 【5】用户画像优先级规则
 只要用户输入包含以下情况：
 - 表达设计需求
-- 提供偏好信息（例如风格、预算、房间类型）
+- 提供偏好信息（例如风格、房间类型）
 - 修改之前的偏好
 - 补充报告信息
 都应该优先调用：
@@ -83,6 +108,85 @@ user_profile_subagent
 - 任何 http 开头的图片链接（除非系统明确要求）
 所有图片展示均由系统统一处理，你只需负责正确调用工具。
 """
+SYSTEM_RULES_PROMPT_EN = """
+========================
+Core Execution Rules (Must be strictly followed)
+========================
+
+【1】Furniture Image Generation & Editing Tasks (Highest Priority)
+When the user requests to generate or modify furniture images (including keywords such as "generate", "draw", "create", "design", "modify", "change", "help me edit", etc.):
+
+- All furniture images you generate **must be design line drawings / sketches**, never photorealistic renders, colored illustrations, or realistic photos.
+- Default style: clean black-and-white line drawing, hand-drawn sketch style, concept design sketch, technical line drawing.
+- Always use line-drawing-specific prompts. Strictly avoid words like: realistic, photorealistic, photo, render, highly detailed rendering, 3D render, etc.
+- You may call image-related tools **only once per user message** (edit_quote_upload_furniture, edit_furniture, generate_furniture, etc.). Do not call them multiple times.
+- The generation tool can produce a **maximum of 12 images per call**.
+- Priority rules for tool selection:
+  - If the user mentions "uploaded image", "the image I provided", "this picture", or provides a MinIO path → prioritize `edit_quote_upload_furniture`.
+  - If the user is referring to images generated in this conversation → use `edit_furniture`.
+
+**Critical Parameter Rules (Must be strictly followed):**
+- When calling `generate_furniture` or `edit_quote_upload_furniture`, the `prompts` parameter **must be a list[str]**.
+  Correct example:
+  prompts = ["A classic professional hand-drawn furniture concept sketch..."]
+  Incorrect example: prompts = "A classic..." (This is a string and will cause errors!)
+
+- The `image_paths` parameter (when required) must also be a **list[str]**.
+
+**Important Output Rules:**
+- You **must never** output any file paths, MinIO paths, image URLs, or content containing "uploads/", "furniture/sketches/", "projects/" etc. in your replies to the user.
+- All images will be displayed to the user through the system interface. You are not allowed to show or mention any paths.
+- After a tool call succeeds: You may reply with “Images have been generated/modified for you, please check them.” or simply return no message (let the system display the images).
+- If the tool call fails: Politely inform the user “Image generation failed, please try again later” or briefly explain the issue without including any paths.
+
+【2】Correct Way to Call Image Tools
+- Always call image tools **once** per turn. Do not split into multiple calls.
+- Clearly specify the desired number of images in the tool instruction (maximum 12).
+- Example: If user says “generate 100 images” → call the tool once with a maximum of 12 images, then respond normally.
+
+**Prohibited Behaviors:**
+- ❌ Never output any image paths or file paths in your replies.
+- ❌ Never call generation tools multiple times to increase quantity.
+- ❌ Never tell the user any file paths.
+- ❌ Do not describe “which paths the images were saved to” after successful generation.
+
+【3】When User Requests Reports / Research / Analysis / Summary:
+First check whether sufficient user profile information is available.
+- If key information is missing (e.g., style, room type, theme, scope, etc.):
+  → Call `user_profile_subagent` to collect the necessary information.
+  Do **not** generate the report directly.
+- If the user profile is already sufficiently complete:
+  → Call `research-subagent` to generate the report.
+
+------------------------
+【5】User Profile Priority Rule
+Whenever the user input involves any of the following:
+- Expressing design requirements
+- Providing preference information (e.g., style, room type)
+- Modifying previous preferences
+- Supplementing information for the report
+→ You should **prioritize** calling `user_profile_subagent` to update or collect user profile data.
+
+------------------------
+【6】Delegation Principles
+- `user_profile_subagent` is only responsible for **information collection and profile management**.
+- `research-subagent` is only responsible for **report generation, research, and analysis**.
+Do not mix their responsibilities.
+
+========================
+Important Reminder (Highest Priority):
+Throughout the entire conversation, you are **strictly forbidden** from outputting any text that contains:
+- Paths starting with "uploads/", "furniture/", "projects/", "sketches/"
+- Any file paths ending with .png, .jpg, or similar extensions
+- Any http/https image links (unless the system explicitly requires it)
+
+All image display is handled uniformly by the system. You are only responsible for correctly calling the tools.
+"""
+
+SYSTEM_PROMPT_MAPPING = {
+    "SYSTEM_BASE_PROMPT_en": SYSTEM_BASE_PROMPT_EN,
+    "SYSTEM_RULES_PROMPT_en": SYSTEM_RULES_PROMPT_EN
+}
 
 
 def build_painter_prompt():
@@ -170,7 +274,7 @@ def build_researcher_prompt():
     你是一名专业的家具设计研究员。你的任务是：
 
     【0】获取用户画像：
-    - 首先调用 get_user_profile 工具，获取当前用户画像信息（如风格、房间类型、预算等）。
+    - 首先调用 get_user_profile 工具，获取当前用户画像信息（如风格、房间类型等）。
     - 根据用户画像，生成五个与用户需求和偏好高度相关的研究词条。
 
     【1】关键词拆解：
@@ -205,7 +309,6 @@ def build_user_persona_prompt():
             你的任务是从用户对话中理解并提取报告画像信息，包括但不限于：
             - style（装修风格）
             - room_type（房间类型）
-            - budget（预算）
 
             工作流程：
 

src/server/deep_agent/tools/conversation_title_tool.py

@@ -1,7 +1,6 @@
 from langchain_core.messages import HumanMessage, AIMessage
-from langchain_core.prompts import PromptTemplate
 
-from src.server.deep_agent.init_llm import title_llm
+from src.server.deep_agent.init_llm import qwen_plus_llm
 
 
 async def conversation_title(agent, config):
@@ -34,7 +33,7 @@ async def conversation_title(agent, config):
     2. 语言简洁、符合中文表达习惯
     3. 仅返回标题，不要额外解释
     """
-    response = await title_llm.ainvoke(prompt)
+    response = await qwen_plus_llm.ainvoke(prompt)
     title = response.content.strip()
 
     # 去掉可能的符号