feat：提高工具调用成功率，移除冗余的描述中参数介绍，增加索引列表的描述，修改prompt，移除timing的wait打断
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SengokuCola
@@ -1,5 +1,5 @@
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"""
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表达方式自动检查定时任务
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表达方式自动检查定时任务。
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功能：
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1. 定期随机选取指定数量的表达方式
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@@ -9,52 +9,48 @@
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"""
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import asyncio
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import json
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import random
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from typing import List
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from sqlmodel import select
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from src.learners.expression_review_store import get_review_state, set_review_state
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from src.common.data_models.llm_service_data_models import LLMGenerationOptions
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from src.common.database.database import get_db_session
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from src.common.database.database_model import Expression
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.common.data_models.llm_service_data_models import LLMGenerationOptions
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from src.services.llm_service import LLMServiceClient
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from src.learners.expression_review_store import get_review_state, set_review_state
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from src.learners.expression_utils import parse_evaluation_response
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from src.manager.async_task_manager import AsyncTask
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from src.services.llm_service import LLMServiceClient
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logger = get_logger("expressor")
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def create_evaluation_prompt(situation: str, style: str) -> str:
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"""
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创建评估提示词
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创建评估提示词。
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Args:
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situation: 情境
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situation: 情景
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style: 风格
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Returns:
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评估提示词
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"""
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# 基础评估标准
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base_criteria = [
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"表达方式或言语风格 是否与使用条件或使用情景 匹配",
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"允许部分语法错误或口头化或缺省出现",
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"表达方式或言语风格是否与使用条件或使用情景匹配",
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"允许部分语法错误或口语化或缺省出现",
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"表达方式不能太过特指，需要具有泛用性",
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"一般不涉及具体的人名或名称",
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]
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# 从配置中获取额外的自定义标准
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custom_criteria = global_config.expression.expression_auto_check_custom_criteria
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# 合并所有评估标准
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all_criteria = base_criteria.copy()
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if custom_criteria:
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all_criteria.extend(custom_criteria)
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# 构建评估标准列表字符串
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criteria_list = "\n".join([f"{i + 1}. {criterion}" for i, criterion in enumerate(all_criteria)])
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prompt = f"""请评估以下表达方式或语言风格以及使用条件或使用情景是否合适：
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@@ -64,14 +60,13 @@ def create_evaluation_prompt(situation: str, style: str) -> str:
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请从以下方面进行评估：
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{criteria_list}
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请以JSON格式输出评估结果：
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请以 JSON 格式输出评估结果：
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{{
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"suitable": true/false,
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"reason": "评估理由（如果不合适，请说明原因）"
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}}
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如果合适，suitable设为true；如果不合适，suitable设为false，并在reason中说明原因。
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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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如果合适，suitable 设为 true；如果不合适，suitable 设为 false，并在 reason 中说明原因。
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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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return prompt
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@@ -81,10 +76,10 @@ judge_llm = LLMServiceClient(task_name="utils", request_type="expression_check")
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async def single_expression_check(situation: str, style: str) -> tuple[bool, str, str | None]:
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"""
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执行单次LLM评估
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执行单次 LLM 评估。
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Args:
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situation: 情境
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situation: 情景
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style: 风格
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Returns:
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@@ -101,20 +96,10 @@ async def single_expression_check(situation: str, style: str) -> tuple[bool, str
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response = generation_result.response
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logger.debug(f"LLM响应: {response}")
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# 解析JSON响应
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try:
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evaluation = json.loads(response)
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except json.JSONDecodeError as e:
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import re
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evaluation = parse_evaluation_response(response)
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json_match = re.search(r'\{[^{}]*"suitable"[^{}]*\}', response, re.DOTALL)
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if json_match:
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evaluation = json.loads(json_match.group())
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else:
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raise ValueError("无法从响应中提取JSON格式的评估结果") from e
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suitable = evaluation.get("suitable", False)
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reason = evaluation.get("reason", "未提供理由")
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suitable = bool(evaluation.get("suitable", False))
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reason = str(evaluation.get("reason", "未提供理由"))
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logger.debug(f"评估结果: {'通过' if suitable else '不通过'}")
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return suitable, reason, None
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@@ -125,20 +110,19 @@ async def single_expression_check(situation: str, style: str) -> tuple[bool, str
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class ExpressionAutoCheckTask(AsyncTask):
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"""表达方式自动检查定时任务"""
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"""表达方式自动检查定时任务。"""
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def __init__(self):
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# 从配置中获取检查间隔和一次检查数量
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check_interval = global_config.expression.expression_auto_check_interval
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super().__init__(
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task_name="Expression Auto Check Task",
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wait_before_start=60, # 启动后等待60秒再开始第一次检查
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wait_before_start=60,
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run_interval=check_interval,
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)
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async def _select_expressions(self, count: int) -> List[Expression]:
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"""
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随机选择指定数量的未检查表达方式
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随机选择指定数量的未检查表达方式。
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Args:
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count: 需要选择的数量
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@@ -158,11 +142,12 @@ class ExpressionAutoCheckTask(AsyncTask):
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logger.info("没有未检查的表达方式")
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return []
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# 随机选择指定数量
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selected_count = min(count, len(unevaluated_expressions))
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selected = random.sample(unevaluated_expressions, selected_count)
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logger.info(f"从 {len(unevaluated_expressions)} 条未检查表达方式中随机选择了 {selected_count} 条")
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logger.info(
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f"从 {len(unevaluated_expressions)} 条未检查表达方式中随机选择了 {selected_count} 条"
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)
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return selected
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except Exception as e:
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@@ -171,35 +156,35 @@ class ExpressionAutoCheckTask(AsyncTask):
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async def _evaluate_expression(self, expression: Expression) -> bool:
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"""
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评估单个表达方式
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评估单个表达方式。
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Args:
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expression: 要评估的表达方式
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Returns:
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True表示通过，False表示不通过
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True 表示通过，False 表示不通过
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"""
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suitable, reason, error = await single_expression_check(
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expression.situation,
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expression.style,
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)
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# 更新数据库
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try:
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set_review_state(expression.id, True, not suitable, "ai")
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status = "通过" if suitable else "不通过"
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# 保留这段注释，方便后续需要时恢复更详细的审核日志。
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# logger.info(
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# f"表达方式评估完成 [ID: {expression.id}] - {status} | "
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# f"Situation: {expression.situation}... | "
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# f"Style: {expression.style}... | "
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# f"Reason: {reason[:50]}..."
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# f"表达方式评估完成 [ID: {expression.id}] - {status} | "
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# f"Situation: {expression.situation}... | "
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# f"Style: {expression.style}... | "
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# f"Reason: {reason[:50]}..."
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# )
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if error:
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logger.warning(f"表达方式评估时出现错误 [ID: {expression.id}]: {error}")
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logger.debug(f"表达方式 [ID: {expression.id}] 评估完成: {status}, reason={reason}")
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return suitable
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except Exception as e:
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@@ -207,9 +192,8 @@ class ExpressionAutoCheckTask(AsyncTask):
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return False
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async def run(self):
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"""执行检查任务"""
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"""执行检查任务。"""
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try:
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# 检查是否启用自动检查
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if not global_config.expression.expression_self_reflect:
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logger.debug("表达方式自动检查未启用，跳过本次执行")
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return
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@@ -221,26 +205,22 @@ class ExpressionAutoCheckTask(AsyncTask):
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logger.info(f"开始执行表达方式自动检查，本次将检查 {check_count} 条")
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# 选择要检查的表达方式
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expressions = await self._select_expressions(check_count)
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if not expressions:
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logger.info("没有需要检查的表达方式")
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return
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# 逐个评估
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passed_count = 0
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failed_count = 0
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for i, expression in enumerate(expressions, 1):
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logger.debug(f"正在评估 [{i}/{len(expressions)}]: ID={expression.id}")
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for index, expression in enumerate(expressions, 1):
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logger.debug(f"正在评估 [{index}/{len(expressions)}]: ID={expression.id}")
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if await self._evaluate_expression(expression):
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passed_count += 1
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else:
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failed_count += 1
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# 避免请求过快
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await asyncio.sleep(0.3)
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logger.info(
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