Losita/mai-bot
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

全部prompt独立

Browse Source
This commit is contained in:
UnCLAS-Prommer
2026-01-21 22:24:31 +08:00
parent 1a1edde750
commit f44598a331
34 changed files with 690 additions and 1037 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
src/bw_learner/expression_selector.py
View File

@@ -8,7 +8,7 @@ from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
from src.common.logger import get_logger
from src.common.database.database_model import Expression
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.prompt.prompt_manager import prompt_manager
from src.bw_learner.learner_utils import weighted_sample
from src.chat.message_receive.chat_stream import get_chat_manager
from src.chat.utils.common_utils import TempMethodsExpression
@@ -16,33 +16,6 @@ from src.chat.utils.common_utils import TempMethodsExpression
logger = get_logger("expression_selector")
def init_prompt():
expression_evaluation_prompt = """{chat_observe_info}
你的名字是{bot_name}{target_message}
{reply_reason_block}
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，最多{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]
}}
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")
class ExpressionSelector:
def __init__(self):
self.llm_model = LLMRequest(
@@ -125,7 +98,9 @@ class ExpressionSelector:
# 查询所有相关chat_id的表达方式，排除 rejected=1 的，且只选择 count > 1 的
# 如果 expression_checked_only 为 True，则只选择 checked=True 且 rejected=False 的
base_conditions = (Expression.chat_id.in_(related_chat_ids)) & (~Expression.rejected) & (Expression.count > 1)
base_conditions = (
(Expression.chat_id.in_(related_chat_ids)) & (~Expression.rejected) & (Expression.count > 1)
)
if global_config.expression.expression_checked_only:
base_conditions = base_conditions & (Expression.checked)
style_query = Expression.select().where(base_conditions)
@@ -149,9 +124,7 @@ class ExpressionSelector:
if len(style_exprs) < min_required:
# 高 count 样本不足：如果还有候选，就降级为随机选 3 个；如果一个都没有，则直接返回空
if not style_exprs:
logger.info(
f"聊天流 {chat_id} 没有满足 count > 1 且未被拒绝的表达方式，简单模式不进行选择"
)
logger.info(f"聊天流 {chat_id} 没有满足 count > 1 且未被拒绝的表达方式，简单模式不进行选择")
# 完全没有高 count 样本时，退化为全量随机抽样（不进入LLM流程）
fallback_num = min(3, max_num) if max_num > 0 else 3
fallback_selected = self._random_expressions(chat_id, fallback_num)
@@ -405,15 +378,15 @@ class ExpressionSelector:
reply_reason_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_context,
all_situations=all_situations_str,
max_num=max_num,
target_message=target_message_str,
target_message_extra_block=target_message_extra_block,
reply_reason_block=reply_reason_block,
)
prompt_template = prompt_manager.get_prompt("expression_evaluation_prompt")
prompt_template.add_context("bot_name", global_config.bot.nickname)
prompt_template.add_context("chat_observe_info", chat_context)
prompt_template.add_context("all_situations", all_situations_str)
prompt_template.add_context("max_num", str(max_num))
prompt_template.add_context("target_message", target_message_str)
prompt_template.add_context("target_message_extra_block", target_message_extra_block)
prompt_template.add_context("reply_reason_block", reply_reason_block)
prompt = await prompt_manager.render_prompt(prompt_template)
# 4. 调用LLM
content, (reasoning_content, model_name, _) = await self.llm_model.generate_response_async(prompt=prompt)
@@ -482,9 +455,6 @@ class ExpressionSelector:
expr_obj.save()
logger.debug("表达方式激活: 更新last_active_time in db")
init_prompt()
try:
expression_selector = ExpressionSelector()
except Exception as e: