225 lines
7.4 KiB
Python
225 lines
7.4 KiB
Python
"""Utilities for managing per-user personalization settings."""
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from __future__ import annotations
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import json
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from copy import deepcopy
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from pathlib import Path
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from typing import Any, Dict, Iterable, Optional, Union
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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 core.tool_config import TOOL_CATEGORIES
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PERSONALIZATION_FILENAME = "personalization.json"
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MAX_SHORT_FIELD_LENGTH = 20
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MAX_CONSIDERATION_LENGTH = 50
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MAX_CONSIDERATION_ITEMS = 10
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TONE_PRESETS = ["健谈", "幽默", "直言不讳", "鼓励性", "诗意", "企业商务", "打破常规", "同理心"]
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THINKING_INTERVAL_MIN = 1
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THINKING_INTERVAL_MAX = 50
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DEFAULT_PERSONALIZATION_CONFIG: Dict[str, Any] = {
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"enabled": False,
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"self_identify": "",
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"user_name": "",
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"profession": "",
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"tone": "",
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"considerations": [],
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"thinking_interval": None,
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"disabled_tool_categories": [],
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}
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__all__ = [
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"PERSONALIZATION_FILENAME",
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"DEFAULT_PERSONALIZATION_CONFIG",
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"TONE_PRESETS",
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"MAX_CONSIDERATION_ITEMS",
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"load_personalization_config",
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"save_personalization_config",
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"ensure_personalization_config",
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"build_personalization_prompt",
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"sanitize_personalization_payload",
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]
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PathLike = Union[str, Path]
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def _ensure_parent(path: Path) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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def _to_path(base: PathLike) -> Path:
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base_path = Path(base).expanduser()
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if base_path.is_dir():
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return base_path / PERSONALIZATION_FILENAME
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return base_path
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def ensure_personalization_config(base_dir: PathLike) -> Dict[str, Any]:
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"""Ensure the personalization file exists and return its content."""
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path = _to_path(base_dir)
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_ensure_parent(path)
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if not path.exists():
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with open(path, "w", encoding="utf-8") as f:
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json.dump(DEFAULT_PERSONALIZATION_CONFIG, f, ensure_ascii=False, indent=2)
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return deepcopy(DEFAULT_PERSONALIZATION_CONFIG)
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return load_personalization_config(base_dir)
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def load_personalization_config(base_dir: PathLike) -> Dict[str, Any]:
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"""Load personalization config; fall back to defaults on errors."""
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path = _to_path(base_dir)
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_ensure_parent(path)
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if not path.exists():
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return ensure_personalization_config(base_dir)
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try:
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with open(path, "r", encoding="utf-8") as f:
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data = json.load(f)
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return sanitize_personalization_payload(data)
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except (json.JSONDecodeError, OSError):
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# 重置为默认配置，避免错误阻塞
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with open(path, "w", encoding="utf-8") as f:
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json.dump(DEFAULT_PERSONALIZATION_CONFIG, f, ensure_ascii=False, indent=2)
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return deepcopy(DEFAULT_PERSONALIZATION_CONFIG)
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def sanitize_personalization_payload(
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payload: Optional[Dict[str, Any]],
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fallback: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""Normalize payload structure and clamp field lengths."""
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base = deepcopy(DEFAULT_PERSONALIZATION_CONFIG)
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if fallback:
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base.update(fallback)
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data = payload or {}
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allowed_tool_categories = set(TOOL_CATEGORIES.keys())
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def _resolve_short_field(key: str) -> str:
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if key in data:
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return _sanitize_short_field(data.get(key))
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return _sanitize_short_field(base.get(key))
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base["enabled"] = bool(data.get("enabled", base["enabled"]))
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base["self_identify"] = _resolve_short_field("self_identify")
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base["user_name"] = _resolve_short_field("user_name")
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base["profession"] = _resolve_short_field("profession")
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base["tone"] = _resolve_short_field("tone")
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if "considerations" in data:
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base["considerations"] = _sanitize_considerations(data.get("considerations"))
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else:
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base["considerations"] = _sanitize_considerations(base.get("considerations", []))
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if "thinking_interval" in data:
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base["thinking_interval"] = _sanitize_thinking_interval(data.get("thinking_interval"))
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else:
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base["thinking_interval"] = _sanitize_thinking_interval(base.get("thinking_interval"))
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if "disabled_tool_categories" in data:
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base["disabled_tool_categories"] = _sanitize_tool_categories(data.get("disabled_tool_categories"), allowed_tool_categories)
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else:
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base["disabled_tool_categories"] = _sanitize_tool_categories(base.get("disabled_tool_categories"), allowed_tool_categories)
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return base
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def save_personalization_config(base_dir: PathLike, payload: Dict[str, Any]) -> Dict[str, Any]:
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"""Persist sanitized personalization config and return it."""
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existing = load_personalization_config(base_dir)
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config = sanitize_personalization_payload(payload, fallback=existing)
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path = _to_path(base_dir)
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_ensure_parent(path)
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with open(path, "w", encoding="utf-8") as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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return config
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def build_personalization_prompt(
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config: Optional[Dict[str, Any]],
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include_header: bool = True
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) -> Optional[str]:
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"""Generate the personalization prompt text based on config."""
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if not config or not config.get("enabled"):
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return None
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lines = []
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if include_header:
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lines.append("用户的个性化数据，请回答时务必参照这些信息")
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if config.get("self_identify"):
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lines.append(f"用户希望你自称：{config['self_identify']}")
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if config.get("user_name"):
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lines.append(f"用户希望你称呼为：{config['user_name']}")
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if config.get("profession"):
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lines.append(f"用户的职业是：{config['profession']}")
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if config.get("tone"):
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lines.append(f"用户希望你使用 {config['tone']} 的语气与TA交流")
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considerations: Iterable[str] = config.get("considerations") or []
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considerations = [item for item in considerations if item]
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if considerations:
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lines.append("用户希望你在回答问题时必须考虑的信息是：")
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for idx, item in enumerate(considerations, 1):
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lines.append(f"{idx}. {item}")
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if len(lines) == (1 if include_header else 0):
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# 没有任何有效内容时不注入
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return None
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return "\n".join(lines)
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def _sanitize_short_field(value: Optional[str]) -> str:
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if not value:
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return ""
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text = str(value).strip()
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if not text:
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return ""
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return text[:MAX_SHORT_FIELD_LENGTH]
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def _sanitize_considerations(value: Any) -> list:
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if not isinstance(value, list):
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return []
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cleaned = []
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for item in value:
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if not isinstance(item, str):
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continue
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text = item.strip()
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if not text:
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continue
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cleaned.append(text[:MAX_CONSIDERATION_LENGTH])
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if len(cleaned) >= MAX_CONSIDERATION_ITEMS:
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break
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return cleaned
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def _sanitize_thinking_interval(value: Any) -> Optional[int]:
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if value is None or value == "":
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return None
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try:
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interval = int(value)
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except (TypeError, ValueError):
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return None
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interval = max(THINKING_INTERVAL_MIN, min(THINKING_INTERVAL_MAX, interval))
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if interval == THINKING_FAST_INTERVAL:
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return None
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return interval
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def _sanitize_tool_categories(value: Any, allowed: set) -> list:
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if not isinstance(value, list):
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return []
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result = []
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for item in value:
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if not isinstance(item, str):
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continue
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candidate = item.strip()
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if not candidate or candidate not in allowed:
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continue
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if candidate not in result:
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result.append(candidate)
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return result
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