293 lines
14 KiB
Python
293 lines
14 KiB
Python
import asyncio
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import json
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from datetime import datetime
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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_manager import SubAgentManager
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from modules.webpage_extractor import extract_webpage_content, tavily_extract
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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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)
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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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)
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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
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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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)
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logger = setup_logger(__name__)
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DISABLE_LENGTH_CHECK = True
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class MainTerminalContextMixin:
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def build_context(self) -> Dict:
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"""构建主终端上下文"""
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# 读取记忆
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memory = self.memory_manager.read_main_memory()
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# 构建上下文
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return self.context_manager.build_main_context(memory)
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def _tool_calls_followed_by_tools(self, conversation: List[Dict], start_idx: int, tool_calls: List[Dict]) -> bool:
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"""判断指定助手消息的工具调用是否拥有后续的工具响应。"""
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if not tool_calls:
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return False
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expected_ids = [tc.get("id") for tc in tool_calls if tc.get("id")]
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if not expected_ids:
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return False
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matched: Set[str] = set()
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idx = start_idx + 1
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total = len(conversation)
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while idx < total and len(matched) < len(expected_ids):
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next_conv = conversation[idx]
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role = next_conv.get("role")
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if role == "tool":
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call_id = next_conv.get("tool_call_id")
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if call_id in expected_ids:
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matched.add(call_id)
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else:
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break
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elif role in ("assistant", "user"):
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break
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idx += 1
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return len(matched) == len(expected_ids)
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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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# 加载系统提示（Qwen3.5 使用专用提示）
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prompt_name = "main_system_qwenvl" if getattr(self, "model_key", "kimi") in {"qwen3-vl-plus", "kimi-k2.5"} else "main_system"
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system_prompt = self.load_prompt(prompt_name)
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# 格式化系统提示
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container_path = self.container_mount_path or "/workspace"
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container_cpus = self.container_cpu_limit
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container_memory = self.container_memory_limit
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project_storage = self.project_storage_limit
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model_key = getattr(self, "model_key", "kimi")
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prompt_replacements = get_model_prompt_replacements(model_key)
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system_prompt = system_prompt.format(
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project_path=container_path,
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container_path=container_path,
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container_cpus=container_cpus,
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container_memory=container_memory,
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project_storage=project_storage,
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file_tree=context["project_info"]["file_tree"],
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memory=context["memory"],
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current_time=datetime.now().strftime("%Y-%m-%d %H"),
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model_description=prompt_replacements.get("model_description", "")
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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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skills_catalog = get_skills_catalog()
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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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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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skills_prompt = build_skills_prompt(skills_template, skills_list)
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if skills_prompt:
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messages.append({"role": "system", "content": skills_prompt})
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workspace_system = self.context_manager._build_workspace_system_message(context)
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if workspace_system:
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messages.append({"role": "system", "content": workspace_system})
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if self.tool_category_states.get("todo", True):
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todo_prompt = self.load_prompt("todo_guidelines").strip()
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if todo_prompt:
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messages.append({"role": "system", "content": todo_prompt})
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if self.tool_category_states.get("sub_agent", True):
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sub_agent_prompt = self.load_prompt("sub_agent_guidelines").strip()
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if sub_agent_prompt:
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messages.append({"role": "system", "content": sub_agent_prompt})
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if self.deep_thinking_mode:
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deep_prompt = self.load_prompt("deep_thinking_mode_guidelines").strip()
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if deep_prompt:
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deep_prompt = deep_prompt.format(
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deep_thinking_line=prompt_replacements.get("deep_thinking_line", "")
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)
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messages.append({"role": "system", "content": deep_prompt})
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elif self.thinking_mode:
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thinking_prompt = self.load_prompt("thinking_mode_guidelines").strip()
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if thinking_prompt:
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thinking_prompt = thinking_prompt.format(
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thinking_model_line=prompt_replacements.get("thinking_model_line", "")
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)
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messages.append({"role": "system", "content": thinking_prompt})
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# 支持按对话覆盖的个性化配置
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personalization_block = build_personalization_prompt(personalization_config, include_header=False)
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if personalization_block:
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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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personalization_text = personalization_template.format(personalization_block=personalization_block)
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elif personalization_template:
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personalization_text = f"{personalization_template}\n{personalization_block}"
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else:
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personalization_text = personalization_block
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messages.append({"role": "system", "content": personalization_text})
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# 支持按对话覆盖的自定义 system prompt（API 用途）。
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# 放在最后一个 system 消息位置，确保优先级最高，便于业务场景强约束。
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custom_system_prompt = getattr(self.context_manager, "custom_system_prompt", None)
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if isinstance(custom_system_prompt, str) and custom_system_prompt.strip():
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messages.append({"role": "system", "content": custom_system_prompt.strip()})
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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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for idx, conv in enumerate(conversation):
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metadata = conv.get("metadata") or {}
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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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reasoning = conv.get("reasoning_content")
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if reasoning:
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message["reasoning_content"] = reasoning
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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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# 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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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:
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content_payload = self.context_manager._build_content_with_images(content_value, images, videos)
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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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elif conv["role"] == "system" and metadata.get("sub_agent_notice"):
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# 转换为用户消息，让模型能及时响应
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messages.append({
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"role": "user",
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"content": conv["content"]
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})
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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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content_payload = (
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self.context_manager._build_content_with_images(conv["content"], images, videos)
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if (images or videos) 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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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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todo_message = self.context_manager.render_todo_system_message()
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if todo_message:
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messages.append({
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"role": "system",
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"content": todo_message
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})
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disabled_notice = self._format_disabled_tool_notice()
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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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return messages
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def load_prompt(self, name: str) -> str:
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"""加载提示模板"""
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prompt_file = Path(PROMPTS_DIR) / f"{name}.txt"
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if prompt_file.exists():
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with open(prompt_file, 'r', encoding='utf-8') as f:
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return f.read()
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return "你是一个AI助手。"
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