diff --git a/pyproject.toml b/pyproject.toml index be67773..9861e88 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,4 +66,5 @@ dependencies = [ "python-magic>=0.4.27", "ddgs>=9.14.1", "aiofiles>=24.1.0", + "fast-langdetect>=1.0.0", ] diff --git a/src/server/deep_agent/agents/main_agent.py b/src/server/deep_agent/agents/main_agent.py index 9fbad53..e909ecf 100755 --- a/src/server/deep_agent/agents/main_agent.py +++ b/src/server/deep_agent/agents/main_agent.py @@ -1,15 +1,17 @@ import logging -from typing import Callable +from typing import Callable, Any, Optional, Dict from dataclasses import dataclass from deepagents import create_deep_agent from deepagents.backends import FilesystemBackend, CompositeBackend, StateBackend -from langchain.agents.middleware import SummarizationMiddleware, ToolRetryMiddleware, wrap_model_call, ModelRequest, ModelResponse, wrap_tool_call, dynamic_prompt -from langchain_core.messages import ToolMessage, SystemMessage +from fast_langdetect import detect +from langchain.agents.middleware import SummarizationMiddleware, ToolRetryMiddleware, wrap_model_call, ModelRequest, ModelResponse, wrap_tool_call, dynamic_prompt, before_model, AgentMiddleware, hook_config +from langchain_core.messages import ToolMessage, SystemMessage, AIMessage, HumanMessage from langgraph.checkpoint.mongodb import MongoDBSaver from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer from langgraph.constants import END from langgraph.prebuilt.tool_node import ToolCallRequest +from langgraph.runtime import Runtime from langgraph.store.memory import InMemoryStore from langgraph.types import Command from pymongo import MongoClient @@ -96,7 +98,7 @@ async def report_control(request: ToolCallRequest, handler: Callable[[ToolCallRe def user_role_prompt(request: ModelRequest) -> str: """Generate system prompts based on use_report status and language preference.""" use_report = request.runtime.context.use_report - language = request.runtime.context.language # 默认简体中文 + language = request.runtime.context.language # ==================== 报告功能状态提示（支持中英文） ==================== if use_report: @@ -132,7 +134,13 @@ def user_role_prompt(request: ModelRequest) -> str: CRITICAL: - Be sure to use the above settings when generating line drawings/images. - Do not refer to these three settings repeatedly when generating reports or text-only answers.""" - + langguage_prompt = f""" + ## Custom Language Rules + - All content of the final report and all reply content MUST be fully written in: {language} + - No mixed languages, no bilingual contrast, no extra English annotations. + - Maintain native, fluent, professional expression conforming to the language habits of {language}. + - All professional terms, captions, notes and reference descriptions must follow the unified {language} specification. + """ final_prompt = backend_prompt + SYSTEM_PROMPT_MAPPING[f'SYSTEM_BASE_PROMPT_en'] + report_status + SYSTEM_PROMPT_MAPPING[f"SYSTEM_RULES_PROMPT_en"] logger.info( @@ -142,6 +150,58 @@ def user_role_prompt(request: ModelRequest) -> str: return final_prompt +from langchain.agents.middleware import AgentState + + +class LanguageDetectionMiddleware(AgentMiddleware): + """使用 fast-langdetect（基于 fastText）自动检测语言""" + + def __init__(self, min_length: int = 8, default_lang: str = "zh"): + self.min_length = min_length + self.default_lang = default_lang + + def before_model(self, state: AgentState, runtime=None) -> Optional[Dict[str, Any]]: + messages = state.get("messages", []) + if not messages: + return None + + last_msg = messages[-1] + if not isinstance(last_msg, HumanMessage): + return None + + content = last_msg.content if hasattr(last_msg, "content") else str(last_msg) + content = content[0].get("text").strip() + + if len(content) < self.min_length: + return None + + try: + detected_lang = "en" + confidence = 0 + # 单语言检测（最常用） + res = detect(text=content, model="auto", k=1) + if res and res[0].get("lang") and res[0].get("score", 0) > 0.5: + detected_lang = res[0]["lang"] + confidence = res[0]["score"] + + print(f"🔍 fast-langdetect 检测到: {detected_lang} (score={confidence:.4f})") + + runtime.context.language = detected_lang + + return { + "language": detected_lang, + "preferred_language": detected_lang, + "language_confidence": float(confidence), + } + + except Exception as e: + print(f"语言检测失败: {e}") + return {"language": self.default_lang} + + async def abefore_model(self, state: AgentState, runtime=None): + return self.before_model(state, runtime) + + def build_main_agent(workspace_dir, enable_thinking): research_subagent = build_researcher_subagent(workspace_dir) # painter_subagent = build_painter_subagent(workspace_dir) @@ -151,6 +211,7 @@ def build_main_agent(workspace_dir, enable_thinking): user_profile_subagent ] middleware = [ + LanguageDetectionMiddleware(min_length=8, default_lang="en"), user_role_prompt, report_control, SummarizationMiddleware( diff --git a/src/server/deep_agent/agents/researcher.py b/src/server/deep_agent/agents/researcher.py index 6cae7bd..ccf00df 100755 --- a/src/server/deep_agent/agents/researcher.py +++ b/src/server/deep_agent/agents/researcher.py @@ -1,3 +1,5 @@ +from langchain.agents.middleware import dynamic_prompt, ModelRequest + from src.server.deep_agent.init_prompt import build_researcher_prompt from src.server.deep_agent.tools.crawl_tool import create_crawl4ai_batch_tool from src.server.deep_agent.tools.report_generator_tool import create_report_generator_tool @@ -6,6 +8,52 @@ from src.server.deep_agent.tools.structured_retrieval_tool import create_structu from src.server.deep_agent.tools.user_persona_tool import query_report_profile +@dynamic_prompt +def language_control(request: ModelRequest) -> str: + """Generate system prompts based on use_report status and language preference.""" + language = request.runtime.context.language # 默认简体中文 + + final_prompt = f""" +You are a professional furniture design researcher. + +Your primary goal: +- Generate a high-quality, structured furniture design research report based on the user's request and user profile. +- The report should be clear, insightful, and written in well-structured Markdown format. +- It should include design trends, materials, color directions, representative cases, and relevant references. + +You are allowed to: +- Retrieve user profile information (e.g., style, room type, preferences) +- Generate research keywords +- Search for relevant topics and sources +- Crawl and read web content +- Extract structured insights +- Generate the final report + +Tool usage guidelines: +- If necessary, first retrieve the user profile to better understand preferences. +- Use meaningful and relevant keywords for research. +- When crawling web content, try to process multiple sources efficiently (avoid repeated calls). +- Focus on extracting key insights such as trends, materials, colors, and case studies. +- Use the report_generator tool to produce the final report. + +Important rules: +- Your objective is to complete a high-quality report, not to strictly follow a fixed sequence of steps. +- You may adapt your approach depending on the situation. +- Avoid calling the same tool repeatedly (especially crawl tools). +- If some data is missing, proceed with available information and clearly mention any limitations. +- Once the report is generated, consider the task complete and stop further actions. + +## Custom Language Rules +- All content of the final report and all reply content MUST be fully written in: {language} +- No mixed languages, no bilingual contrast, no extra English annotations. +- Maintain native, fluent, professional expression conforming to the language habits of {language}. +- All professional terms, captions, notes and reference descriptions must follow the unified {language} specification. + +""" + + return final_prompt + + def build_researcher_subagent(workspace_dir): crawl4ai_batch = create_crawl4ai_batch_tool(workspace_dir) structured_retrieval = create_structured_retrieval_tool(workspace_dir) @@ -28,10 +76,11 @@ This sub-agent will: - Produce a complete research report Do NOT use this sub-agent for: -- User profile collection (handled by user_profile_subagent) +- User profile collection - Image generation or editing tasks """, "system_prompt": build_researcher_prompt(), + "middleware": [language_control], "tools": [ query_report_profile, topic_research, diff --git a/src/server/deep_agent/agents/user_profile.py b/src/server/deep_agent/agents/user_profile.py index ba3b577..99cab30 100755 --- a/src/server/deep_agent/agents/user_profile.py +++ b/src/server/deep_agent/agents/user_profile.py @@ -1,6 +1,101 @@ +from langchain.agents.middleware import dynamic_prompt, ModelRequest + from src.server.deep_agent.init_prompt import build_user_persona_prompt from src.server.deep_agent.tools.user_persona_tool import query_report_profile, update_report_profile, check_profile_complete + +@dynamic_prompt +def language_control(request: ModelRequest) -> str: + """Generate system prompts based on use_report status and language preference.""" + language = request.runtime.context.language # 默认简体中文 + + final_prompt = f""" + You are a user profile collection assistant. + + Your goal: + - Extract and maintain structured user profile information from the conversation. + - The profile is used for generating furniture design reports. + + Profile fields may include: + - style (design style or aesthetic preference) + - room_type (type of room or space) + - budget (optional) + - other relevant design preferences + + What you should do: + - Understand the user's input and identify any profile-related information. + - If new information is found, update the profile accordingly. + - If no new information is provided, keep the existing profile unchanged. + - Ensure previously stored information is preserved unless the user explicitly modifies it. + + Tool usage guidelines: + - Use query_report_profile when you need to know the current profile. + - Use update_report_profile only when new or updated information is detected. + - Use check_profile_complete to determine if required fields are sufficient for report generation. + + Behavior rules: + - Do NOT generate reports. + - Do NOT guess or fabricate missing information. + - Only extract information that is clearly stated or strongly implied by the user. + - Be concise and structured in your output. + + When profile is incomplete: + - Ask the user for the missing information in a natural way. + + When profile is complete: + - Respond with a clear signal that profile collection is done, for example: + "Profile is complete. Ready for report generation." + + Language rules: + - Always respond in the same language as the user. + - Do not mix languages. + - Keep the output consistent and natural. + + Strict Language Enforcement: + - You MUST use only one language in the entire response. + - The language must match the user's input. + - Mixing multiple languages is strictly prohibited. + """ + + final_prompt = f""" +You are a professional furniture design researcher. + +## Core Objectives +- Generate high-quality, in-depth & structured furniture design research reports in standard Markdown format. +- Strictly combine user requirements and complete user profile information for customized analysis. +- The report must cover: design trend analysis, mainstream material selection, color palette orientation, classic representative cases and industry reference information. + +## Permitted Capabilities +- Retrieve and parse user profile data (design style preference, room type, usage scenario, aesthetic tendency, etc.). +- Extract core research keywords for industry investigation. +- Search, crawl and summarize multi-source industry information. +- Refine structured, actionable design insights. +- Call the report_generator tool to output the final standardized report. + +## Tool Usage Specifications +- Prioritize obtaining complete user profile before research to improve report relevance. +- Use precise, industry-oriented search keywords. +- Crawl and integrate multiple sources at one time to avoid redundant and repeated tool calls. +- Focus on screening effective information: trend characteristics, material performance, color matching logic, typical brand cases. +- Do not over-rely on tool processes; flexibly adjust research ideas according to information integrity. + +## Critical Rules +- Task priority: deliver a complete, high-quality research report. +- No rigid step-by-step execution; adjust research logic adaptively based on actual conditions. +- Prohibit frequent repeated calls to crawl and search tools. +- If partial industry data is missing, continue writing with existing valid information and mark data limitations clearly in the report. +- Stop all tool calls and work immediately after the final report is generated. + +## Custom Language Rules +- All content of the final report and all reply content MUST be fully written in: {language} +- No mixed languages, no bilingual contrast, no extra English annotations. +- Maintain native, fluent, professional expression conforming to the language habits of {language}. +- All professional terms, captions, notes and reference descriptions must follow the unified {language} specification. +""" + + return final_prompt + + user_profile_subagent = { "name": "user_profile_subagent", "description": """ @@ -22,6 +117,7 @@ Do NOT use this sub-agent for: - Image generation or editing """, "system_prompt": build_user_persona_prompt(), + "middleware": [language_control], "tools": [ query_report_profile, update_report_profile, diff --git a/src/server/deep_agent/tools/research_tool.py b/src/server/deep_agent/tools/research_tool.py index 0e63eea..46f70aa 100755 --- a/src/server/deep_agent/tools/research_tool.py +++ b/src/server/deep_agent/tools/research_tool.py @@ -79,8 +79,8 @@ from langchain.tools import tool @tool async def topic_research(topic: List[str], max_urls: int = 5) -> str: """ - 深度调研工具（DuckDuckGo版本）。 - 根据多个主题关键词进行搜索，返回去重后的高质量 URL 列表（JSON字符串）。 + In-depth research tool (DuckDuckGo version). + Search based on multiple topic keywords and return a high-quality URL list (JSON string) after deduplication. 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