218 lines
7.7 KiB
Python
218 lines
7.7 KiB
Python
"""
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统一历史对话管理器
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整合所有分散的 parse_history / _parse_history / build_history_str / filter_image_urls 逻辑
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"""
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import json
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from typing import Any, Dict, List, Optional
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class HistoryManager:
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"""统一的历史对话管理器，提供解析、截断、格式化、过滤等功能"""
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def __init__(
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self,
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max_messages: int = 8,
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max_assistant_chars: int = 300,
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):
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"""
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Args:
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max_messages: 保留的最近消息条数
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max_assistant_chars: assistant 角色内容截断字符数
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"""
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self.max_messages = max_messages
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self.max_assistant_chars = max_assistant_chars
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# ==================== 解析 ====================
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def parse(self, history_message: Any) -> List[Dict[str, Any]]:
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"""
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解析历史消息为列表，保留最近 max_messages 条。
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兼容字符串和列表输入，替代所有 parse_history / _parse_history。
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"""
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if not history_message:
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return []
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try:
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data = json.loads(history_message) if isinstance(history_message, str) else history_message
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return data[-self.max_messages:] if isinstance(data, list) else []
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except Exception:
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return []
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# ==================== 截断 ====================
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def truncate(self, messages: List[Dict[str, Any]], keep_recent: Optional[int] = None) -> List[Dict[str, Any]]:
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"""按数量截断，保留最近 keep_recent 条"""
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if not messages:
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return []
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n = keep_recent if keep_recent is not None else self.max_messages
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return messages[-n:]
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# ==================== 格式化 ====================
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def to_prompt_str(
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self,
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messages: List[Dict[str, Any]],
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recent_count: Optional[int] = None,
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assistant_truncate: Optional[int] = None,
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) -> str:
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"""
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格式化为 "用户: xxx\\n助手: xxx" 纯文本字符串，用于 prompt 注入。
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替代 build_history_str。
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"""
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if not messages:
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return ""
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n = recent_count if recent_count is not None else self.max_messages
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trunc = assistant_truncate if assistant_truncate is not None else self.max_assistant_chars
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recent = messages[-n:]
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parts = []
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for m in recent:
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if isinstance(m, dict):
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role = m.get("role", "")
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content = (m.get("content") or "").strip()
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if role == "user":
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parts.append(f"用户: {content}")
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elif role == "assistant":
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parts.append(f"助手: {content[:trunc]}")
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return "\n".join(parts) if parts else ""
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def to_openai_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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格式化为 OpenAI messages 列表，直接传入 LLM。
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用于百科问答等需要完整消息格式的场景。
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"""
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if not messages:
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return []
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result = []
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for m in messages:
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if isinstance(m, dict) and m.get("role") and m.get("content"):
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result.append({"role": m["role"], "content": m["content"]})
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return result
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# ==================== 请求预处理 ====================
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def preprocess_from_request(self, history_message: Any, exclude_last: bool = True) -> str:
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"""
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处理前端传入的历史消息：解析、移除当前用户消息、序列化。
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对应 app.py 中 history_message 预处理逻辑。
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Args:
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history_message: 前端传入的历史消息，支持 str 或 list
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exclude_last: 是否移除最后一条（当前用户消息），默认 True
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Returns:
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处理后的 JSON 字符串
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"""
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if not history_message:
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return ""
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try:
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data = json.loads(history_message) if isinstance(history_message, str) else history_message
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except (json.JSONDecodeError, TypeError):
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return ""
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if not isinstance(data, list):
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return ""
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if exclude_last and data:
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data = data[:-1]
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return json.dumps(data, ensure_ascii=False)
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# ==================== 状态初始化 ====================
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def init_history(self, history_message: Any) -> Dict[str, Any]:
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"""
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一步完成历史消息解析，返回 history_message(str) + history_messages(list) 双字段。
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用于各工作流入口初始化 state，消除重复的 parse_history 调用。
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Args:
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history_message: 历史消息，支持 str 或 list
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Returns:
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{"history_message": str, "history_messages": list}
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"""
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parsed = self.parse(history_message)
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raw = json.dumps(parsed, ensure_ascii=False) if parsed else ""
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return {"history_message": raw, "history_messages": parsed}
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# ==================== 过滤 ====================
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@staticmethod
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def filter_image_urls(history_message: Any) -> Any:
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"""
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过滤历史消息中的图片URL，将多模态内容转为纯文本。
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替代 main_agent.filter_image_urls。
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"""
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if not history_message:
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return history_message
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try:
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history_messages = json.loads(history_message) if isinstance(history_message, str) else history_message
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except (json.JSONDecodeError, TypeError):
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return history_message
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if not isinstance(history_messages, list):
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return history_messages
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filtered_messages = []
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for msg in history_messages:
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if not isinstance(msg, dict):
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filtered_messages.append(msg)
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continue
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new_msg = msg.copy()
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content = msg.get("content")
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if isinstance(content, list):
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text_parts = []
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for content_item in content:
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if isinstance(content_item, dict):
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if content_item.get("type") == "text":
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text_value = content_item.get("text", "")
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if text_value:
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text_parts.append(text_value)
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elif isinstance(content_item, str):
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text_parts.append(content_item)
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if text_parts:
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new_msg["content"] = "".join(text_parts)
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filtered_messages.append(new_msg)
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else:
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filtered_messages.append(new_msg)
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return filtered_messages
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# ==================== 模块级便捷实例 ====================
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# 提供与旧函数签名兼容的模块级函数，方便渐进式迁移
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_default_manager = HistoryManager()
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def parse_history(history_message: Any) -> List[Dict[str, Any]]:
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"""兼容旧调用的模块级函数"""
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return _default_manager.parse(history_message)
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def build_history_str(
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history_messages: List[Dict[str, Any]],
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recent_count: int = 4,
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assistant_truncate: int = 300,
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) -> str:
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"""兼容旧调用的模块级函数"""
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mgr = HistoryManager(max_messages=recent_count, max_assistant_chars=assistant_truncate)
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return mgr.to_prompt_str(history_messages, recent_count=recent_count, assistant_truncate=assistant_truncate)
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def filter_image_urls(history_message: Any) -> Any:
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"""兼容旧调用的模块级函数"""
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return HistoryManager.filter_image_urls(history_message)
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def preprocess_from_request(history_message: Any, exclude_last: bool = True) -> str:
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"""兼容旧调用的模块级函数"""
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return _default_manager.preprocess_from_request(history_message, exclude_last=exclude_last)
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def init_history(history_message: Any) -> Dict[str, Any]:
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"""兼容旧调用的模块级函数"""
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return _default_manager.init_history(history_message)
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