420 lines
20 KiB
Python
420 lines
20 KiB
Python
import asyncio
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import json
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Set
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try:
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from config import (
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OUTPUT_FORMATS, DATA_DIR, PROMPTS_DIR, NEED_CONFIRMATION,
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MAX_TERMINALS, TERMINAL_BUFFER_SIZE, TERMINAL_DISPLAY_SIZE,
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MAX_READ_FILE_CHARS, READ_TOOL_DEFAULT_MAX_CHARS,
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READ_TOOL_DEFAULT_CONTEXT_BEFORE, READ_TOOL_DEFAULT_CONTEXT_AFTER,
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READ_TOOL_MAX_CONTEXT_BEFORE, READ_TOOL_MAX_CONTEXT_AFTER,
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READ_TOOL_DEFAULT_MAX_MATCHES, READ_TOOL_MAX_MATCHES,
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READ_TOOL_MAX_FILE_SIZE,
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TERMINAL_SANDBOX_MOUNT_PATH,
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TERMINAL_SANDBOX_MODE,
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TERMINAL_SANDBOX_CPUS,
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TERMINAL_SANDBOX_MEMORY,
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PROJECT_MAX_STORAGE_MB,
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CUSTOM_TOOLS_ENABLED,
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)
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except ImportError:
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import sys
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project_root = Path(__file__).resolve().parents[2]
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if str(project_root) not in sys.path:
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sys.path.insert(0, str(project_root))
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from config import (
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OUTPUT_FORMATS, DATA_DIR, PROMPTS_DIR, NEED_CONFIRMATION,
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MAX_TERMINALS, TERMINAL_BUFFER_SIZE, TERMINAL_DISPLAY_SIZE,
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MAX_READ_FILE_CHARS, READ_TOOL_DEFAULT_MAX_CHARS,
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READ_TOOL_DEFAULT_CONTEXT_BEFORE, READ_TOOL_DEFAULT_CONTEXT_AFTER,
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READ_TOOL_MAX_CONTEXT_BEFORE, READ_TOOL_MAX_CONTEXT_AFTER,
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READ_TOOL_DEFAULT_MAX_MATCHES, READ_TOOL_MAX_MATCHES,
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READ_TOOL_MAX_FILE_SIZE,
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TERMINAL_SANDBOX_MOUNT_PATH,
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TERMINAL_SANDBOX_MODE,
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TERMINAL_SANDBOX_CPUS,
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TERMINAL_SANDBOX_MEMORY,
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PROJECT_MAX_STORAGE_MB,
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CUSTOM_TOOLS_ENABLED,
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)
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from modules.file_manager import FileManager
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from modules.search_engine import SearchEngine
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from modules.terminal_ops import TerminalOperator
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from modules.memory_manager import MemoryManager
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from modules.terminal_manager import TerminalManager
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from modules.todo_manager import TodoManager
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from modules.sub_agent import SubAgentManager
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from modules.webpage_extractor import extract_webpage_content, tavily_extract
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from modules.multi_agent.prompts import build_available_agents_prompt
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from modules.ocr_client import OCRClient
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from modules.easter_egg_manager import EasterEggManager
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from modules.personalization_manager import (
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load_personalization_config,
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build_personalization_prompt,
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RECENT_CONVERSATIONS_PROMPT_LIMIT_MIN,
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RECENT_CONVERSATIONS_PROMPT_LIMIT_MAX,
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RECENT_CONVERSATIONS_PROMPT_LIMIT_DEFAULT,
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)
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from modules.skills_manager import (
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get_skills_catalog,
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build_skills_list,
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merge_enabled_skills,
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build_skills_prompt,
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infer_private_skills_dir,
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)
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from modules.custom_tool_registry import CustomToolRegistry, build_default_tool_category
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from modules.custom_tool_executor import CustomToolExecutor
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try:
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from config.limits import THINKING_FAST_INTERVAL
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except ImportError:
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THINKING_FAST_INTERVAL = 10
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from modules.container_monitor import collect_stats, inspect_state
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from core.tool_config import TOOL_CATEGORIES
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from utils.api_client import DeepSeekClient
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from utils.context_manager import ContextManager, AUTO_SHALLOW_PLACEHOLDER
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from utils.host_workspace_debug import write_host_workspace_debug
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from utils.tool_result_formatter import format_tool_result_for_context
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from utils.logger import setup_logger
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from config.model_profiles import (
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get_model_profile,
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get_model_prompt_replacements,
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get_model_context_window,
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model_supports_image,
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model_supports_video,
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)
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logger = setup_logger(__name__)
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DISABLE_LENGTH_CHECK = True
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class MessagesMixin:
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"""MainTerminalContextMixin messages 能力 mixin。"""
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def build_messages(self, context: Dict, user_input: str) -> List[Dict]:
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"""构建消息列表（添加终端内容注入）"""
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try:
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file_tree_preview = (context.get("project_info", {}).get("file_tree") or "").splitlines()
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write_host_workspace_debug(
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"main_terminal.build_messages.context_snapshot",
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terminal_id=id(self),
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terminal_project_path=str(getattr(self, "project_path", "")),
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context_project_path=str(getattr(getattr(self, "context_manager", None), "project_path", "")),
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project_info_path=str(context.get("project_info", {}).get("path", "")),
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file_tree_first_line=file_tree_preview[0] if file_tree_preview else "",
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current_conversation_id=getattr(getattr(self, "context_manager", None), "current_conversation_id", None),
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)
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except Exception:
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pass
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# 根据当前模型多模态能力选择系统提示。system prompt 按对话冻结；
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# 工具 schema 保持动态，由 define_tools 每轮根据真实状态生成。
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current_model = getattr(self, "model_key", None)
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prompt_name = "main_system_vl" if (model_supports_image(current_model) or model_supports_video(current_model)) else "main_system"
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model_key = getattr(self, "model_key", None)
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prompt_replacements = get_model_prompt_replacements(model_key)
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is_multi_agent_mode = bool(getattr(self, "multi_agent_mode", False))
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def _build_main_system_prompt() -> str:
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if is_multi_agent_mode:
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# 多智能体模式下主 prompt 使用 Team Leader 专属模板
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try:
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from modules.multi_agent.prompts import _load_template
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return _load_template("master")
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except Exception as exc:
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logger.warning(f"[messages] 加载多智能体主 prompt 失败: {exc}")
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system_prompt_template = self.load_prompt(prompt_name)
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# main_system.txt / main_system_vl.txt 仅使用 {model_description}
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return system_prompt_template.format(
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model_description=prompt_replacements.get("model_description", "")
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)
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main_system_frozen_key = (
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"frozen_main_system_prompt_multi_agent"
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if is_multi_agent_mode
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else "frozen_main_system_prompt"
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)
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system_prompt = self._get_or_init_frozen_prompt(
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main_system_frozen_key,
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_build_main_system_prompt,
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)
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messages = [
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{"role": "system", "content": system_prompt}
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]
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personalization_config = getattr(self.context_manager, "custom_personalization_config", None) or load_personalization_config(self.data_dir)
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shallow_replace_enabled = bool(personalization_config.get("auto_shallow_compress_enabled", False)) if isinstance(personalization_config, dict) else False
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# 顺序：主prompt → 权限模式 → 执行环境 → 最近对话 → 个性化配置 → 工作区信息 → AGENTS.md → skills → 记忆 → 自定义 → 禁用提示
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# 权限模式
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permission_mode_message = self._get_or_init_frozen_mode_prompt(
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"frozen_permission_prompt",
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self._build_permission_mode_message,
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)
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if permission_mode_message:
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messages.append({"role": "system", "content": permission_mode_message})
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# 执行环境
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execution_mode_message = self._get_or_init_frozen_mode_prompt(
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"frozen_execution_prompt",
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self._build_execution_mode_message,
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)
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if execution_mode_message:
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messages.append({"role": "system", "content": execution_mode_message})
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# 最近对话
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def _build_recent_conversations_prompt() -> str:
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recent_conversations_enabled = (
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bool(personalization_config.get("recent_conversations_prompt_enabled", False))
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if isinstance(personalization_config, dict)
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else False
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)
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if not recent_conversations_enabled:
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return ""
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try:
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recent_limit = int(
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personalization_config.get(
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"recent_conversations_prompt_limit",
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RECENT_CONVERSATIONS_PROMPT_LIMIT_DEFAULT,
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)
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)
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except Exception:
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recent_limit = RECENT_CONVERSATIONS_PROMPT_LIMIT_DEFAULT
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recent_limit = max(
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RECENT_CONVERSATIONS_PROMPT_LIMIT_MIN,
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min(RECENT_CONVERSATIONS_PROMPT_LIMIT_MAX, recent_limit),
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)
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return self._build_recent_conversations_message(limit=recent_limit) or ""
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recent_conversations_prompt = self._get_or_init_frozen_prompt(
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"frozen_recent_conversations_prompt",
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_build_recent_conversations_prompt,
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)
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if recent_conversations_prompt:
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messages.append({"role": "system", "content": recent_conversations_prompt})
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# 个性化配置
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def _build_personalization_system_prompt() -> str:
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personalization_block = build_personalization_prompt(personalization_config, include_header=False)
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if not personalization_block:
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return ""
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personalization_template = self.load_prompt("personalization").strip()
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if personalization_template and "{personalization_block}" in personalization_template:
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return personalization_template.format(personalization_block=personalization_block)
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if personalization_template:
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return f"{personalization_template}\n{personalization_block}"
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return personalization_block
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personalization_text = self._get_or_init_frozen_prompt(
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"frozen_personalization_prompt",
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_build_personalization_system_prompt,
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)
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if personalization_text:
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messages.append({"role": "system", "content": personalization_text})
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# 工作区信息
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workspace_system = self._get_or_init_frozen_prompt(
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"frozen_workspace_prompt",
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lambda: self.context_manager._build_workspace_system_message(context) or "",
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)
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if workspace_system:
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messages.append({"role": "system", "content": workspace_system})
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# AGENTS.md
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def _build_agents_md_prompt() -> str:
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agents_md_inject_enabled = bool(personalization_config.get("agents_md_auto_inject", False)) if isinstance(personalization_config, dict) else False
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if not agents_md_inject_enabled:
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return ""
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agents_md_content = self._load_agents_md_content()
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if not agents_md_content:
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return ""
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agents_md_template = self.load_prompt("agents_md_inject").strip()
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if agents_md_template and "{{AGENTS_MD_CONTENT}}" in agents_md_template:
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return agents_md_template.replace("{{AGENTS_MD_CONTENT}}", agents_md_content)
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return f"【AGENTS.md 项目规范】\n\n{agents_md_content}\n\n---\n请注意：以上规范来自工作区根目录的 AGENTS.md 文件，若有冲突请以 AGENTS.md 为准。"
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# AGENTS.md 注入开关可能变化，只在开启时生成并缓存，避免关闭时把空字符串冻住
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agents_md_inject_enabled = (
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bool(personalization_config.get("agents_md_auto_inject", False))
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if isinstance(personalization_config, dict)
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else False
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)
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if agents_md_inject_enabled:
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agents_md_text = self._get_or_init_frozen_prompt(
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"frozen_agents_md_prompt",
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_build_agents_md_prompt,
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)
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if agents_md_text:
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messages.append({"role": "system", "content": agents_md_text})
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# skills 列表
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skills_catalog = get_skills_catalog(private_dir=infer_private_skills_dir(self.data_dir))
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enabled_skills = merge_enabled_skills(
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personalization_config.get("enabled_skills") if isinstance(personalization_config, dict) else None,
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skills_catalog,
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personalization_config.get("skills_catalog_snapshot") if isinstance(personalization_config, dict) else None,
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)
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def _build_skills_system_prompt() -> str:
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skills_template = self.load_prompt("skills_system").strip()
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skills_list = build_skills_list(skills_catalog, enabled_skills)
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return build_skills_prompt(skills_template, skills_list) or ""
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skills_prompt = self._get_or_init_frozen_prompt(
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"frozen_skills_prompt",
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_build_skills_system_prompt,
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)
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if skills_prompt:
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messages.append({"role": "system", "content": skills_prompt})
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# 记忆系统（总体长期记忆 + 项目记忆）
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def _build_memory_system_prompt() -> str:
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return self._build_memory_system_content()
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memory_prompt = self._get_or_init_frozen_prompt(
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"frozen_memory_prompt",
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_build_memory_system_prompt,
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)
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if memory_prompt:
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messages.append({"role": "system", "content": memory_prompt})
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# API 自定义 system prompt
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custom_system_prompt = self._get_or_init_frozen_prompt(
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"frozen_custom_system_prompt",
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lambda: (
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getattr(self.context_manager, "custom_system_prompt", "").strip()
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if isinstance(getattr(self.context_manager, "custom_system_prompt", None), str)
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else ""
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),
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)
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if custom_system_prompt:
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messages.append({"role": "system", "content": custom_system_prompt})
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# 禁用工具提示
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disabled_notice = self._get_or_init_frozen_prompt(
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"frozen_disabled_tools_prompt",
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lambda: self._format_disabled_tool_notice() or "",
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)
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# 多智能体模式额外提示词：可用的子智能体角色（动态 prompt，第一个用户消息后冻结）
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if getattr(self, "multi_agent_mode", False):
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try:
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available_agents_prompt = self._get_or_init_frozen_prompt(
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"frozen_available_agents_prompt",
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lambda: build_available_agents_prompt() or "",
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)
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if available_agents_prompt:
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messages.append({"role": "system", "content": available_agents_prompt})
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except Exception as exc:
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logger.warning(f"[messages] 注入可用子智能体 prompt 失败: {exc}")
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if disabled_notice:
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messages.append({
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"role": "system",
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"content": disabled_notice
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})
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# 添加对话历史（保留完整结构，包括tool_calls和tool消息）
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conversation = context["conversation"]
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replaced_tool_count = 0
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deep_compacted_skipped = 0
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for idx, conv in enumerate(conversation):
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metadata = conv.get("metadata") or {}
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# 深压缩：被标记为 deep_compacted 的消息（整段已压缩前缀）原文保留在历史中用于展示，
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# 但构建请求时整体跳过，不纳入上下文。由于按连续前缀整体标记，
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# 不会出现 assistant.tool_calls 与其 tool 响应被拆散导致的配对悬空。
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if metadata.get("deep_compacted"):
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deep_compacted_skipped += 1
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continue
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if conv["role"] == "assistant":
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# Assistant消息可能包含工具调用
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message = {
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"role": conv["role"],
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"content": conv["content"]
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}
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# 对于思考模式（如 DeepSeek thinking），assistant 历史消息中的
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# reasoning_content 即使为空字符串也需要原样回传，否则下一轮可能被
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# API 判定为“未回传 reasoning_content”并返回 400。
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if "reasoning_content" in conv:
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message["reasoning_content"] = conv.get("reasoning_content", "")
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# 如果有工具调用信息，添加到消息中
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tool_calls = conv.get("tool_calls") or []
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if tool_calls and self._tool_calls_followed_by_tools(conversation, idx, tool_calls):
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message["tool_calls"] = tool_calls
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messages.append(message)
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elif conv["role"] == "tool":
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if shallow_replace_enabled and metadata.get("auto_shallow_compacted"):
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messages.append({
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"role": "tool",
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"content": AUTO_SHALLOW_PLACEHOLDER,
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"tool_call_id": conv.get("tool_call_id", ""),
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"name": conv.get("name", "")
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})
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replaced_tool_count += 1
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continue
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# Tool消息需要保留完整结构
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images = conv.get("images") or metadata.get("images") or []
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videos = conv.get("videos") or metadata.get("videos") or []
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media_refs = conv.get("media_refs") or metadata.get("media_refs") or []
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content_value = conv.get("content")
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if isinstance(content_value, list):
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content_payload = content_value
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elif images or videos or media_refs:
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content_payload = self.context_manager._build_content_with_images(
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content_value,
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images,
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videos,
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media_refs=media_refs,
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)
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else:
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content_payload = content_value
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message = {
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"role": "tool",
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"content": content_payload,
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"tool_call_id": conv.get("tool_call_id", ""),
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"name": conv.get("name", "")
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}
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messages.append(message)
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else:
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# User 或普通 System 消息
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images = conv.get("images") or metadata.get("images") or []
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videos = conv.get("videos") or metadata.get("videos") or []
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media_refs = conv.get("media_refs") or metadata.get("media_refs") or []
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content_payload = (
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self.context_manager._build_content_with_images(
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conv["content"],
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images,
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videos,
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media_refs=media_refs,
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)
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if (images or videos or media_refs) else conv["content"]
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)
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# 调试：记录所有 system 消息
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if conv["role"] == "system":
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logger.info(f"[DEBUG build_messages] 添加 system 消息: content前50字={conv['content'][:50]}")
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# 调试：记录多智能体子智能体输出消息是否进入上下文
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if metadata.get("multi_agent_message"):
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logger.info(
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f"[DEBUG build_messages] 添加多智能体子智能体消息到上下文: "
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f"role={conv['role']}, subtype={metadata.get('multi_agent_subtype')}, "
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f"display_name={metadata.get('multi_agent_display_name')}, "
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f"content前80字={str(conv.get('content', ''))[:80]}"
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)
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messages.append({
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"role": conv["role"],
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"content": content_payload
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})
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# 当前用户输入已经在conversation中了，不需要重复添加
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if shallow_replace_enabled:
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print(f"[ContextCompression] build_messages 替换tool占位符: {replaced_tool_count} 条")
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if deep_compacted_skipped:
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print(f"[ContextCompression] build_messages 跳过已深压缩消息: {deep_compacted_skipped} 条")
|
||
return messages
|