312 lines
12 KiB
Python
312 lines
12 KiB
Python
import json
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import time
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import random
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import hashlib
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from typing import List, Dict, Tuple, Optional, Any
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from json_repair import repair_json
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config, model_config
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from src.common.logger import get_logger
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from src.common.database.database_model import Expression
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from .expression_learner import get_expression_learner
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logger = get_logger("expression_selector")
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def init_prompt():
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expression_evaluation_prompt = """
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以下是正在进行的聊天内容：
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{chat_observe_info}
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你的名字是{bot_name}{target_message}
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以下是可选的表达情境：
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{all_situations}
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请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的{min_num}-{max_num}个情境。
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考虑因素包括：
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1. 聊天的情绪氛围（轻松、严肃、幽默等）
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2. 话题类型（日常、技术、游戏、情感等）
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3. 情境与当前语境的匹配度
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{target_message_extra_block}
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请以JSON格式输出，只需要输出选中的情境编号：
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例如：
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{{
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"selected_situations": [2, 3, 5, 7, 19, 22, 25, 38, 39, 45, 48 , 64]
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}}
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例如：
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{{
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"selected_situations": [1, 4, 7, 9, 23, 38, 44]
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}}
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请严格按照JSON格式输出，不要包含其他内容：
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"""
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Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
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def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]:
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"""按权重随机抽样"""
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if not population or not weights or k <= 0:
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return []
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if len(population) <= k:
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return population.copy()
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# 使用累积权重的方法进行加权抽样
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selected = []
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population_copy = population.copy()
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weights_copy = weights.copy()
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for _ in range(k):
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if not population_copy:
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break
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# 选择一个元素
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chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0]
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selected.append(population_copy.pop(chosen_idx))
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weights_copy.pop(chosen_idx)
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return selected
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class ExpressionSelector:
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def __init__(self):
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self.expression_learner = get_expression_learner()
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self.llm_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small, request_type="expression.selector"
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)
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@staticmethod
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def _parse_stream_config_to_chat_id(stream_config_str: str) -> Optional[str]:
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"""解析'platform:id:type'为chat_id（与get_stream_id一致）"""
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try:
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parts = stream_config_str.split(":")
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if len(parts) != 3:
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return None
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platform = parts[0]
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id_str = parts[1]
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stream_type = parts[2]
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is_group = stream_type == "group"
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if is_group:
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components = [platform, str(id_str)]
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else:
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components = [platform, str(id_str), "private"]
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key = "_".join(components)
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return hashlib.md5(key.encode()).hexdigest()
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except Exception:
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return None
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def get_related_chat_ids(self, chat_id: str) -> List[str]:
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"""根据expression_groups配置，获取与当前chat_id相关的所有chat_id（包括自身）"""
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groups = global_config.expression.expression_groups
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for group in groups:
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group_chat_ids = []
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for stream_config_str in group:
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if chat_id_candidate := self._parse_stream_config_to_chat_id(stream_config_str):
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group_chat_ids.append(chat_id_candidate)
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if chat_id in group_chat_ids:
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return group_chat_ids
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return [chat_id]
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def get_random_expressions(
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self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
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) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
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# sourcery skip: extract-duplicate-method, move-assign
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# 支持多chat_id合并抽选
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related_chat_ids = self.get_related_chat_ids(chat_id)
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# 优化：一次性查询所有相关chat_id的表达方式
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style_query = Expression.select().where(
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(Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "style")
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)
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grammar_query = Expression.select().where(
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(Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "grammar")
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)
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style_exprs = [
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{
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"situation": expr.situation,
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"style": expr.style,
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"count": expr.count,
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"last_active_time": expr.last_active_time,
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"source_id": expr.chat_id,
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"type": "style",
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"create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
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}
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for expr in style_query
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]
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grammar_exprs = [
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{
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"situation": expr.situation,
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"style": expr.style,
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"count": expr.count,
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"last_active_time": expr.last_active_time,
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"source_id": expr.chat_id,
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"type": "grammar",
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"create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
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}
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for expr in grammar_query
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]
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style_num = int(total_num * style_percentage)
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grammar_num = int(total_num * grammar_percentage)
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# 按权重抽样（使用count作为权重）
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if style_exprs:
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style_weights = [expr.get("count", 1) for expr in style_exprs]
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selected_style = weighted_sample(style_exprs, style_weights, style_num)
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else:
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selected_style = []
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if grammar_exprs:
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grammar_weights = [expr.get("count", 1) for expr in grammar_exprs]
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selected_grammar = weighted_sample(grammar_exprs, grammar_weights, grammar_num)
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else:
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selected_grammar = []
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return selected_style, selected_grammar
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def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, Any]], increment: float = 0.1):
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"""对一批表达方式更新count值，按chat_id+type分组后一次性写入数据库"""
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if not expressions_to_update:
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return
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updates_by_key = {}
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for expr in expressions_to_update:
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source_id: str = expr.get("source_id") # type: ignore
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expr_type: str = expr.get("type", "style")
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situation: str = expr.get("situation") # type: ignore
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style: str = expr.get("style") # type: ignore
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if not source_id or not situation or not style:
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logger.warning(f"表达方式缺少必要字段，无法更新: {expr}")
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continue
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key = (source_id, expr_type, situation, style)
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if key not in updates_by_key:
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updates_by_key[key] = expr
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for chat_id, expr_type, situation, style in updates_by_key:
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query = Expression.select().where(
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(Expression.chat_id == chat_id)
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& (Expression.type == expr_type)
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& (Expression.situation == situation)
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& (Expression.style == style)
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)
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if query.exists():
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expr_obj = query.get()
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current_count = expr_obj.count
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new_count = min(current_count + increment, 5.0)
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expr_obj.count = new_count
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expr_obj.last_active_time = time.time()
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expr_obj.save()
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logger.debug(
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f"表达方式激活: 原count={current_count:.3f}, 增量={increment}, 新count={new_count:.3f} in db"
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)
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async def select_suitable_expressions_llm(
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self,
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chat_id: str,
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chat_info: str,
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max_num: int = 10,
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min_num: int = 5,
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target_message: Optional[str] = None,
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) -> List[Dict[str, Any]]:
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# sourcery skip: inline-variable, list-comprehension
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"""使用LLM选择适合的表达方式"""
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# 1. 获取35个随机表达方式（现在按权重抽取）
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style_exprs, grammar_exprs = self.get_random_expressions(chat_id, 50, 0.5, 0.5)
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# 2. 构建所有表达方式的索引和情境列表
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all_expressions = []
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all_situations = []
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# 添加style表达方式
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for expr in style_exprs:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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expr_with_type = expr.copy()
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expr_with_type["type"] = "style"
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all_expressions.append(expr_with_type)
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all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
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# 添加grammar表达方式
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for expr in grammar_exprs:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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expr_with_type = expr.copy()
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expr_with_type["type"] = "grammar"
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all_expressions.append(expr_with_type)
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all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
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if not all_expressions:
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logger.warning("没有找到可用的表达方式")
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return []
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all_situations_str = "\n".join(all_situations)
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if target_message:
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target_message_str = f"，现在你想要回复消息：{target_message}"
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target_message_extra_block = "4.考虑你要回复的目标消息"
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else:
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target_message_str = ""
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target_message_extra_block = ""
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# 3. 构建prompt（只包含情境，不包含完整的表达方式）
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prompt = (await global_prompt_manager.get_prompt_async("expression_evaluation_prompt")).format(
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bot_name=global_config.bot.nickname,
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chat_observe_info=chat_info,
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all_situations=all_situations_str,
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min_num=min_num,
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max_num=max_num,
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target_message=target_message_str,
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target_message_extra_block=target_message_extra_block,
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)
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# print(prompt)
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# 4. 调用LLM
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try:
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content, _ = await self.llm_model.generate_response_async(prompt=prompt)
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# logger.info(f"{self.log_prefix} LLM返回结果: {content}")
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if not content:
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logger.warning("LLM返回空结果")
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return []
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# 5. 解析结果
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result = repair_json(content)
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if isinstance(result, str):
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result = json.loads(result)
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if not isinstance(result, dict) or "selected_situations" not in result:
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logger.error("LLM返回格式错误")
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logger.info(f"LLM返回结果: \n{content}")
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return []
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selected_indices = result["selected_situations"]
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# 根据索引获取完整的表达方式
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valid_expressions = []
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for idx in selected_indices:
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if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
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expression = all_expressions[idx - 1] # 索引从1开始
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valid_expressions.append(expression)
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# 对选中的所有表达方式，一次性更新count数
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if valid_expressions:
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self.update_expressions_count_batch(valid_expressions, 0.006)
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# logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
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return valid_expressions
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except Exception as e:
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logger.error(f"LLM处理表达方式选择时出错: {e}")
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return []
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init_prompt()
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try:
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expression_selector = ExpressionSelector()
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except Exception as e:
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print(f"ExpressionSelector初始化失败: {e}")
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