257 lines
9.4 KiB
Python
257 lines
9.4 KiB
Python
from .exprssion_learner import get_expression_learner
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import random
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from typing import List, Dict, Tuple
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from json_repair import repair_json
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import json
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import os
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import time
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.common.logger import get_logger
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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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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你的名字是{bot_name}
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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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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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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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# TODO: API-Adapter修改标记
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self.llm_model = LLMRequest(
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model=global_config.model.utils_small,
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request_type="expression.selector",
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)
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def get_random_expressions(
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self, chat_id: str, style_num: int, grammar_num: int, personality_num: int
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) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
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(
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learnt_style_expressions,
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learnt_grammar_expressions,
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personality_expressions,
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) = self.expression_learner.get_expression_by_chat_id(chat_id)
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# 按权重抽样（使用count作为权重）
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if learnt_style_expressions:
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style_weights = [expr.get("count", 1) for expr in learnt_style_expressions]
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selected_style = weighted_sample(learnt_style_expressions, style_weights, style_num)
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else:
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selected_style = []
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if learnt_grammar_expressions:
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grammar_weights = [expr.get("count", 1) for expr in learnt_grammar_expressions]
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selected_grammar = weighted_sample(learnt_grammar_expressions, grammar_weights, grammar_num)
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else:
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selected_grammar = []
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if personality_expressions:
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personality_weights = [expr.get("count", 1) for expr in personality_expressions]
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selected_personality = weighted_sample(personality_expressions, personality_weights, personality_num)
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else:
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selected_personality = []
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return selected_style, selected_grammar, selected_personality
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def update_expression_count(self, chat_id: str, expression: Dict[str, str], increment: float = 0.1):
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"""更新表达方式的count值
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Args:
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chat_id: 聊天ID
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expression: 表达方式字典
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increment: 增量值，默认0.1
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"""
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if expression.get("type") == "style_personality":
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# personality表达方式存储在全局文件中
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file_path = os.path.join("data", "expression", "personality", "expressions.json")
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else:
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# style和grammar表达方式存储在对应chat_id目录中
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expr_type = expression.get("type", "style")
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if expr_type == "style":
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file_path = os.path.join("data", "expression", "learnt_style", str(chat_id), "expressions.json")
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elif expr_type == "grammar":
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file_path = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
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else:
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return
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if not os.path.exists(file_path):
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return
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try:
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with open(file_path, "r", encoding="utf-8") as f:
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expressions = json.load(f)
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# 找到匹配的表达方式并更新count
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for expr in expressions:
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if expr.get("situation") == expression.get("situation") and expr.get("style") == expression.get(
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"style"
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):
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current_count = expr.get("count", 1)
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# 简单加0.1，但限制最高为5
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new_count = min(current_count + increment, 5.0)
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expr["count"] = new_count
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expr["last_active_time"] = time.time()
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logger.info(f"表达方式激活: 原count={current_count:.2f}, 增量={increment}, 新count={new_count:.2f}")
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break
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# 保存更新后的文件
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with open(file_path, "w", encoding="utf-8") as f:
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json.dump(expressions, f, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"更新表达方式count失败: {e}")
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async def select_suitable_expressions_llm(
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self, chat_id: str, chat_info: str, max_num: int = 10, min_num: int = 5
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) -> List[Dict[str, str]]:
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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, personality_exprs = self.get_random_expressions(chat_id, 25, 25, 10)
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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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# 添加personality表达方式
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for expr in personality_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_personality"
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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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# 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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)
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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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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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# 对选中的表达方式count数+0.1
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self.update_expression_count(chat_id, expression, 0.1)
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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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