feat(i18n): inject language instruction into LLM system prompts

backend/app/services/ontology_generator.py

@@ -6,6 +6,7 @@
 import json
 from typing import Dict, Any, List, Optional
 from ..utils.llm_client import LLMClient
+from ..utils.locale import get_language_instruction
 
 
 # 本体生成的系统提示词
@@ -188,8 +189,9 @@ class OntologyGenerator:
             additional_context
         )
         
+        system_prompt = f"{ONTOLOGY_SYSTEM_PROMPT}\n\n{get_language_instruction()}"
         messages = [
-            {"role": "system", "content": ONTOLOGY_SYSTEM_PROMPT},
+            {"role": "system", "content": system_prompt},
             {"role": "user", "content": user_message}
         ]
         

backend/app/services/report_agent.py

@@ -21,6 +21,7 @@ from enum import Enum
 from ..config import Config
 from ..utils.llm_client import LLMClient
 from ..utils.logger import get_logger
+from ..utils.locale import get_language_instruction
 from .zep_tools import (
     ZepToolsService, 
     SearchResult, 
@@ -1162,7 +1163,7 @@ class ReportAgent:
         if progress_callback:
             progress_callback("planning", 30, "正在生成报告大纲...")
         
-        system_prompt = PLAN_SYSTEM_PROMPT
+        system_prompt = f"{PLAN_SYSTEM_PROMPT}\n\n{get_language_instruction()}"
         user_prompt = PLAN_USER_PROMPT_TEMPLATE.format(
             simulation_requirement=self.simulation_requirement,
             total_nodes=context.get('graph_statistics', {}).get('total_nodes', 0),
@@ -1258,6 +1259,7 @@ class ReportAgent:
             section_title=section.title,
             tools_description=self._get_tools_description(),
         )
+        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
 
         # 构建用户prompt - 每个已完成章节各传入最大4000字
         if previous_sections:
@@ -1805,6 +1807,7 @@ class ReportAgent:
             report_content=report_content if report_content else "（暂无报告）",
             tools_description=self._get_tools_description(),
         )
+        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
 
         # 构建消息
         messages = [{"role": "system", "content": system_prompt}]

backend/app/services/simulation_config_generator.py

@@ -20,6 +20,7 @@ from openai import OpenAI
 
 from ..config import Config
 from ..utils.logger import get_logger
+from ..utils.locale import get_language_instruction
 from .zep_entity_reader import EntityNode, ZepEntityReader
 
 logger = get_logger('mirofish.simulation_config')
@@ -585,7 +586,8 @@ class SimulationConfigGenerator:
 - reasoning (string): 简要说明为什么这样配置"""
 
         system_prompt = "你是社交媒体模拟专家。返回纯JSON格式，时间配置需符合中国人作息习惯。"
-        
+        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
+
         try:
             return self._call_llm_with_retry(prompt, system_prompt)
         except Exception as e:
@@ -701,7 +703,8 @@ class SimulationConfigGenerator:
 }}"""
 
         system_prompt = "你是舆论分析专家。返回纯JSON格式。注意 poster_type 必须精确匹配可用实体类型。"
-        
+        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
+
         try:
             return self._call_llm_with_retry(prompt, system_prompt)
         except Exception as e:
@@ -864,7 +867,8 @@ class SimulationConfigGenerator:
 }}"""
 
         system_prompt = "你是社交媒体行为分析专家。返回纯JSON，配置需符合中国人作息习惯。"
-        
+        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
+
         try:
             result = self._call_llm_with_retry(prompt, system_prompt)
             llm_configs = {cfg["agent_id"]: cfg for cfg in result.get("agent_configs", [])}