267 lines
10 KiB
Python
267 lines
10 KiB
Python
from .exprssion_learner import get_expression_learner
|
||
import random
|
||
from typing import List, Dict, Tuple
|
||
from json_repair import repair_json
|
||
import json
|
||
import os
|
||
import time
|
||
from src.llm_models.utils_model import LLMRequest
|
||
from src.config.config import global_config
|
||
from src.common.logger import get_logger
|
||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||
|
||
logger = get_logger("expression_selector")
|
||
|
||
|
||
def init_prompt():
|
||
expression_evaluation_prompt = """
|
||
以下是正在进行的聊天内容：
|
||
{chat_observe_info}
|
||
|
||
你的名字是{bot_name}{target_message}
|
||
|
||
以下是可选的表达情境：
|
||
{all_situations}
|
||
|
||
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。
|
||
考虑因素包括：
|
||
1. 聊天的情绪氛围（轻松、严肃、幽默等）
|
||
2. 话题类型（日常、技术、游戏、情感等）
|
||
3. 情境与当前语境的匹配度
|
||
{target_message_extra_block}
|
||
|
||
请以JSON格式输出，只需要输出选中的情境编号：
|
||
例如：
|
||
{{
|
||
"selected_situations": [2, 3, 5, 7, 19, 22, 25, 38, 39, 45, 48 , 64]
|
||
}}
|
||
例如：
|
||
{{
|
||
"selected_situations": [1, 4, 7, 9, 23, 38, 44]
|
||
}}
|
||
|
||
请严格按照JSON格式输出，不要包含其他内容：
|
||
"""
|
||
Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
|
||
|
||
|
||
def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]:
|
||
"""按权重随机抽样"""
|
||
if not population or not weights or k <= 0:
|
||
return []
|
||
|
||
if len(population) <= k:
|
||
return population.copy()
|
||
|
||
# 使用累积权重的方法进行加权抽样
|
||
selected = []
|
||
population_copy = population.copy()
|
||
weights_copy = weights.copy()
|
||
|
||
for _ in range(k):
|
||
if not population_copy:
|
||
break
|
||
|
||
# 选择一个元素
|
||
chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0]
|
||
selected.append(population_copy.pop(chosen_idx))
|
||
weights_copy.pop(chosen_idx)
|
||
|
||
return selected
|
||
|
||
|
||
class ExpressionSelector:
|
||
def __init__(self):
|
||
self.expression_learner = get_expression_learner()
|
||
# TODO: API-Adapter修改标记
|
||
self.llm_model = LLMRequest(
|
||
model=global_config.model.utils_small,
|
||
request_type="expression.selector",
|
||
)
|
||
|
||
def get_random_expressions(
|
||
self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
|
||
) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
|
||
(
|
||
learnt_style_expressions,
|
||
learnt_grammar_expressions,
|
||
) = self.expression_learner.get_expression_by_chat_id(chat_id)
|
||
|
||
style_num = int(total_num * style_percentage)
|
||
grammar_num = int(total_num * grammar_percentage)
|
||
|
||
# 按权重抽样（使用count作为权重）
|
||
if learnt_style_expressions:
|
||
style_weights = [expr.get("count", 1) for expr in learnt_style_expressions]
|
||
selected_style = weighted_sample(learnt_style_expressions, style_weights, style_num)
|
||
else:
|
||
selected_style = []
|
||
|
||
if learnt_grammar_expressions:
|
||
grammar_weights = [expr.get("count", 1) for expr in learnt_grammar_expressions]
|
||
selected_grammar = weighted_sample(learnt_grammar_expressions, grammar_weights, grammar_num)
|
||
else:
|
||
selected_grammar = []
|
||
|
||
return selected_style, selected_grammar
|
||
|
||
def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, str]], increment: float = 0.1):
|
||
"""对一批表达方式更新count值，按文件分组后一次性写入"""
|
||
if not expressions_to_update:
|
||
return
|
||
|
||
updates_by_file = {}
|
||
for expr in expressions_to_update:
|
||
source_id = expr.get("source_id")
|
||
if not source_id:
|
||
logger.warning(f"表达方式缺少source_id，无法更新: {expr}")
|
||
continue
|
||
|
||
file_path = ""
|
||
if source_id == "personality":
|
||
file_path = os.path.join("data", "expression", "personality", "expressions.json")
|
||
else:
|
||
chat_id = source_id
|
||
expr_type = expr.get("type", "style")
|
||
if expr_type == "style":
|
||
file_path = os.path.join("data", "expression", "learnt_style", str(chat_id), "expressions.json")
|
||
elif expr_type == "grammar":
|
||
file_path = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
|
||
|
||
if file_path:
|
||
if file_path not in updates_by_file:
|
||
updates_by_file[file_path] = []
|
||
updates_by_file[file_path].append(expr)
|
||
|
||
for file_path, updates in updates_by_file.items():
|
||
if not os.path.exists(file_path):
|
||
continue
|
||
|
||
try:
|
||
with open(file_path, "r", encoding="utf-8") as f:
|
||
all_expressions = json.load(f)
|
||
|
||
# Create a dictionary for quick lookup
|
||
expr_map = {(e.get("situation"), e.get("style")): e for e in all_expressions}
|
||
|
||
# Update counts in memory
|
||
for expr_to_update in updates:
|
||
key = (expr_to_update.get("situation"), expr_to_update.get("style"))
|
||
if key in expr_map:
|
||
expr_in_map = expr_map[key]
|
||
current_count = expr_in_map.get("count", 1)
|
||
new_count = min(current_count + increment, 5.0)
|
||
expr_in_map["count"] = new_count
|
||
expr_in_map["last_active_time"] = time.time()
|
||
logger.debug(
|
||
f"表达方式激活: 原count={current_count:.3f}, 增量={increment}, 新count={new_count:.3f} in {file_path}"
|
||
)
|
||
|
||
# Save the updated list once for this file
|
||
with open(file_path, "w", encoding="utf-8") as f:
|
||
json.dump(all_expressions, f, ensure_ascii=False, indent=2)
|
||
|
||
except Exception as e:
|
||
logger.error(f"批量更新表达方式count失败 for {file_path}: {e}")
|
||
|
||
async def select_suitable_expressions_llm(
|
||
self, chat_id: str, chat_info: str, max_num: int = 10, min_num: int = 5, target_message: str = None
|
||
) -> List[Dict[str, str]]:
|
||
"""使用LLM选择适合的表达方式"""
|
||
|
||
# 1. 获取35个随机表达方式（现在按权重抽取）
|
||
style_exprs, grammar_exprs = self.get_random_expressions(chat_id, 50, 0.5, 0.5)
|
||
|
||
# 2. 构建所有表达方式的索引和情境列表
|
||
all_expressions = []
|
||
all_situations = []
|
||
|
||
# 添加style表达方式
|
||
for expr in style_exprs:
|
||
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
|
||
expr_with_type = expr.copy()
|
||
expr_with_type["type"] = "style"
|
||
all_expressions.append(expr_with_type)
|
||
all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
|
||
|
||
# 添加grammar表达方式
|
||
for expr in grammar_exprs:
|
||
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
|
||
expr_with_type = expr.copy()
|
||
expr_with_type["type"] = "grammar"
|
||
all_expressions.append(expr_with_type)
|
||
all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
|
||
|
||
if not all_expressions:
|
||
logger.warning("没有找到可用的表达方式")
|
||
return []
|
||
|
||
all_situations_str = "\n".join(all_situations)
|
||
|
||
if target_message:
|
||
target_message_str = f"，现在你想要回复消息：{target_message}"
|
||
target_message_extra_block = "4.考虑你要回复的目标消息"
|
||
else:
|
||
target_message_str = ""
|
||
target_message_extra_block = ""
|
||
|
||
# 3. 构建prompt（只包含情境，不包含完整的表达方式）
|
||
prompt = (await global_prompt_manager.get_prompt_async("expression_evaluation_prompt")).format(
|
||
bot_name=global_config.bot.nickname,
|
||
chat_observe_info=chat_info,
|
||
all_situations=all_situations_str,
|
||
min_num=min_num,
|
||
max_num=max_num,
|
||
target_message=target_message_str,
|
||
target_message_extra_block=target_message_extra_block,
|
||
)
|
||
|
||
# print(prompt)
|
||
|
||
# 4. 调用LLM
|
||
try:
|
||
content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt)
|
||
|
||
# logger.info(f"{self.log_prefix} LLM返回结果: {content}")
|
||
|
||
if not content:
|
||
logger.warning("LLM返回空结果")
|
||
return []
|
||
|
||
# 5. 解析结果
|
||
result = repair_json(content)
|
||
if isinstance(result, str):
|
||
result = json.loads(result)
|
||
|
||
if not isinstance(result, dict) or "selected_situations" not in result:
|
||
logger.error("LLM返回格式错误")
|
||
return []
|
||
|
||
selected_indices = result["selected_situations"]
|
||
|
||
# 根据索引获取完整的表达方式
|
||
valid_expressions = []
|
||
for idx in selected_indices:
|
||
if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
|
||
expression = all_expressions[idx - 1] # 索引从1开始
|
||
valid_expressions.append(expression)
|
||
|
||
# 对选中的所有表达方式，一次性更新count数
|
||
if valid_expressions:
|
||
self.update_expressions_count_batch(valid_expressions, 0.006)
|
||
|
||
# logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
|
||
return valid_expressions
|
||
|
||
except Exception as e:
|
||
logger.error(f"LLM处理表达方式选择时出错: {e}")
|
||
return []
|
||
|
||
|
||
init_prompt()
|
||
|
||
try:
|
||
expression_selector = ExpressionSelector()
|
||
except Exception as e:
|
||
print(f"ExpressionSelector初始化失败: {e}")
|