From 6527a43bba440d0bfd5c5928c49de3bd11aa0899 Mon Sep 17 00:00:00 2001 From: minecraft1024a Date: Thu, 21 Aug 2025 19:41:59 +0800 Subject: [PATCH] =?UTF-8?q?refactor(schedule):=20=E9=87=8D=E6=9E=84?= =?UTF-8?q?=E6=9C=88=E5=BA=A6=E8=AE=A1=E5=88=92=E7=94=9F=E6=88=90=E5=99=A8?= =?UTF-8?q?=E4=BB=A5=E5=A2=9E=E5=BC=BA=E7=A8=B3=E5=AE=9A=E6=80=A7=E5=92=8C?= =?UTF-8?q?=E5=8F=AF=E7=BB=B4=E6=8A=A4=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 将计划生成逻辑重构为使用统一的 `LLMRequest` 类，以简化模型调用流程。此更改消除了原有的手动选择模型、构建客户端和处理响应的复杂逻辑。 引入了 Pydantic 模型 `PlanResponse` 对 LLM 的 JSON 输出进行严格的验证和解析，并集成了 `json_repair` 库来自动修复格式错误的 JSON。这些措施显著提高了计划生成功能的健壮性和对 LLM 异常输出的容错能力。 --- src/schedule/plan_generator.py | 80 +++++++++++++++++----------------- 1 file changed, 39 insertions(+), 41 deletions(-) diff --git a/src/schedule/plan_generator.py b/src/schedule/plan_generator.py index a85149a74..3930da82a 100644 --- a/src/schedule/plan_generator.py +++ b/src/schedule/plan_generator.py @@ -1,16 +1,22 @@ # mmc/src/schedule/plan_generator.py import json -import random from typing import List +from pydantic import BaseModel, ValidationError +from json_repair import repair_json + from src.config.config import global_config, model_config -from src.llm_models.model_client.base_client import client_registry -from src.llm_models.payload_content.message import Message, RoleType -from src.llm_models.payload_content.resp_format import RespFormat, RespFormatType +from src.llm_models.utils_model import LLMRequest from src.common.logger import get_logger logger = get_logger("plan_generator") +class PlanResponse(BaseModel): + """ + 用于验证月度计划LLM响应的Pydantic模型。 + """ + plans: List[str] + class PlanGenerator: """ 负责生成月度计划。 @@ -18,6 +24,8 @@ class PlanGenerator: def __init__(self): self.bot_personality = self._get_bot_personality() + task_config = model_config.model_task_config.get_task("monthly_plan_generator") + self.llm_request = LLMRequest(model_set=task_config, request_type="monthly_plan_generator") def _get_bot_personality(self) -> str: """ @@ -61,56 +69,46 @@ class PlanGenerator: :return: 生成的计划文本列表 """ try: - # 1. 获取模型任务配置 - task_config = model_config.model_task_config.get_task("monthly_plan_generator") - - # 2. 随机选择一个模型 - model_name = random.choice(task_config.model_list) - model_info = model_config.get_model_info(model_name) - api_provider = model_config.get_provider(model_info.api_provider) - - # 3. 获取客户端实例 - llm_client = client_registry.get_client_class_instance(api_provider) - - # 4. 构建Prompt和消息体 + # 1. 构建Prompt prompt = self._build_prompt(year, month, count) - message_list = [Message(role=RoleType.User, content=prompt)] + logger.info(f"正在为 {year}-{month} 生成 {count} 个月度计划...") + + # 2. 调用LLM + llm_content, (reasoning, model_name, _) = await self.llm_request.generate_response_async(prompt=prompt) - logger.info(f"正在使用模型 '{model_name}' 为 {year}-{month} 生成 {count} 个月度计划...") - - # 5. 调用LLM - response = await llm_client.get_response( - model_info=model_info, - message_list=message_list, - temperature=task_config.temperature, - max_tokens=task_config.max_tokens, - response_format=RespFormat(format_type=RespFormatType.JSON_OBJ) # 请求JSON输出 - ) - - if not response or not response.content: + logger.info(f"使用模型 '{model_name}' 生成完成。") + if reasoning: + logger.debug(f"模型推理过程: {reasoning}") + + if not llm_content: logger.error("LLM未能返回有效的计划内容。") return [] - # 6. 解析LLM返回的JSON + # 3. 解析并验证LLM返回的JSON try: # 移除可能的Markdown代码块标记 - clean_content = response.content.strip() + clean_content = llm_content.strip() if clean_content.startswith("```json"): clean_content = clean_content[7:] if clean_content.endswith("```"): clean_content = clean_content[:-3] - - data = json.loads(clean_content.strip()) - plans = data.get("plans", []) - if isinstance(plans, list) and all(isinstance(p, str) for p in plans): - logger.info(f"成功生成并解析了 {len(plans)} 个月度计划。") - return plans - else: - logger.error(f"LLM返回的JSON格式不正确或'plans'键不是字符串列表: {response.content}") - return [] + # 修复并解析JSON + repaired_json_str = repair_json(clean_content) + data = json.loads(repaired_json_str) + + # 使用Pydantic进行验证 + validated_response = PlanResponse.model_validate(data) + plans = validated_response.plans + + logger.info(f"成功生成并验证了 {len(plans)} 个月度计划。") + return plans + except json.JSONDecodeError: - logger.error(f"无法解析LLM返回的JSON: {response.content}") + logger.error(f"修复后仍然无法解析LLM返回的JSON: {llm_content}") + return [] + except ValidationError as e: + logger.error(f"LLM返回的JSON格式不符合预期: {e}\n原始响应: {llm_content}") return [] except Exception as e: