#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 内容生成引擎 V2 - 不访问数据库，接收完整数据 - 使用 PromptRegistry 管理 prompt - 统一依赖注入 """ import logging from typing import Dict, Any, Optional from .base import BaseAIGCEngine, EngineResult logger = logging.getLogger(__name__) class ContentGenerateEngineV2(BaseAIGCEngine): """ 内容生成引擎 V2 改进: 1. 不访问数据库，所有数据由调用方传入 2. 使用 PromptRegistry 管理 prompt 3. 接收完整对象而非 ID """ engine_id = "content_generate_v2" engine_name = "内容生成 V2" version = "2.0.0" description = "根据选题信息生成小红书风格的营销文案（新版本，无数据库依赖）" def __init__(self): super().__init__() self._prompt_registry = None def get_param_schema(self) -> Dict[str, Any]: """定义参数结构""" return { # 选题信息 "topic": { "type": "object", "required": True, "desc": "选题信息 {index, date, object, style, targetAudience, ...}", }, # 完整对象 (由 Java 端传入) "scenic_spot": { "type": "object", "required": False, "desc": "景区信息对象 {id, name, description, ...}", }, "product": { "type": "object", "required": False, "desc": "产品信息对象 {id, name, price, description, ...}", }, "style": { "type": "object", "required": False, "desc": "风格信息对象 {id, name, description}", }, "audience": { "type": "object", "required": False, "desc": "受众信息对象 {id, name, description}", }, # 参考内容 "refer_content": { "type": "str", "required": False, "desc": "参考范文内容", }, # 审核选项 "enable_judge": { "type": "bool", "required": False, "default": True, "desc": "是否启用内容审核", }, # Prompt 版本控制 "prompt_version": { "type": "str", "required": False, "default": "latest", "desc": "使用的 prompt 版本", }, } def estimate_duration(self, params: Dict[str, Any]) -> int: """预估执行时间""" enable_judge = params.get('enable_judge', True) return 30 if enable_judge else 20 async def execute(self, params: Dict[str, Any], task_id: str = None) -> EngineResult: """执行内容生成""" try: self.log("开始生成内容 (V2)") self.set_progress(task_id, 10) # 提取参数 topic = params.get('topic', {}) scenic_spot = params.get('scenic_spot') product = params.get('product') style = params.get('style') audience = params.get('audience') refer_content = params.get('refer_content', '') enable_judge = params.get('enable_judge', True) prompt_version = params.get('prompt_version', 'latest') self.set_progress(task_id, 20) # 获取 PromptRegistry prompt_registry = self._get_prompt_registry() # 构建 prompt 上下文 context = { 'style_content': self._format_style(style, topic), 'demand_content': self._format_audience(audience, topic), 'object_content': self._format_scenic_spot(scenic_spot, topic), 'product_content': self._format_product(product), 'refer_content': refer_content, } # 渲染 prompt system_prompt, user_prompt = prompt_registry.render( 'content_generate', context=context, version=prompt_version ) self.set_progress(task_id, 30) # 获取模型参数 prompt_config = prompt_registry.get('content_generate', prompt_version) model_params = prompt_config.get_model_params() # 调用 LLM 生成内容 self.log("调用 LLM 生成内容...") raw_result, input_tokens, output_tokens, time_cost = await self.llm.generate( system_prompt=system_prompt, user_prompt=user_prompt, **model_params ) self.set_progress(task_id, 60) # 解析结果 content = self._parse_content(raw_result) if not content: return EngineResult( success=False, error="内容生成失败，无法解析结果", error_code="PARSE_ERROR" ) # 内容审核 final_content = content judge_result = None if enable_judge: self.set_progress(task_id, 70) self.log("执行内容审核...") judge_result = await self._judge_content( content, scenic_spot, product, prompt_registry, prompt_version ) if judge_result and judge_result.get('success'): final_content = { 'title': judge_result.get('title', content.get('title')), 'content': judge_result.get('content', content.get('content')), 'tag': content.get('tag', ''), } self.set_progress(task_id, 100) return EngineResult( success=True, data={ "content": final_content, "original_content": content, "topic": topic, "judged": enable_judge and judge_result is not None, "judge_analysis": judge_result.get('analysis') if judge_result else None, }, metadata={ "input_tokens": input_tokens, "output_tokens": output_tokens, "time_cost": time_cost, "prompt_version": prompt_version, } ) except Exception as e: self.log(f"内容生成异常: {e}", level='error') return EngineResult( success=False, error=str(e), error_code="EXECUTION_ERROR" ) def _get_prompt_registry(self): """获取 PromptRegistry""" if self._prompt_registry: return self._prompt_registry from domain.prompt import PromptRegistry self._prompt_registry = PromptRegistry('prompts') return self._prompt_registry def _format_style(self, style: Optional[Dict], topic: Dict) -> str: """格式化风格信息""" if style: name = style.get('name', style.get('styleName', '')) desc = style.get('description', '') return f"{name}\n{desc}" if desc else name # 从 topic 中获取 return topic.get('style', '') def _format_audience(self, audience: Optional[Dict], topic: Dict) -> str: """格式化受众信息""" if audience: name = audience.get('name', audience.get('audienceName', '')) desc = audience.get('description', '') return f"{name}\n{desc}" if desc else name return topic.get('targetAudience', '') def _format_scenic_spot(self, scenic_spot: Optional[Dict], topic: Dict) -> str: """格式化景区信息""" if scenic_spot: name = scenic_spot.get('name', '') desc = scenic_spot.get('description', '') location = scenic_spot.get('location', '') parts = [name] if location: parts.append(f"位置: {location}") if desc: parts.append(desc) return "\n".join(parts) return topic.get('object', '') def _format_product(self, product: Optional[Dict]) -> str: """格式化产品信息""" if not product: return "" name = product.get('name', product.get('productName', '')) price = product.get('price', '') desc = product.get('description', product.get('detailedDescription', '')) parts = [name] if price: parts.append(f"价格: {price}") if desc: parts.append(desc) return "\n".join(parts) def _parse_content(self, raw_result: str) -> Optional[Dict[str, Any]]: """解析 LLM 返回的内容""" import json import re # 尝试提取 JSON json_match = re.search(r'\{[\s\S]*\}', raw_result) if json_match: try: return json.loads(json_match.group()) except json.JSONDecodeError: pass # 尝试 json_repair try: import json_repair return json_repair.loads(raw_result) except: pass self.log("无法解析内容结果", level='error') return None async def _judge_content(self, content: Dict, scenic_spot: Optional[Dict], product: Optional[Dict], prompt_registry, prompt_version: str) -> Optional[Dict]: """执行内容审核""" try: # 构建产品资料 product_info_parts = [] if scenic_spot: product_info_parts.append(f"景区: {scenic_spot.get('name', '')}") if scenic_spot.get('description'): product_info_parts.append(scenic_spot['description']) if product: product_info_parts.append(f"产品: {product.get('name', '')}") if product.get('price'): product_info_parts.append(f"价格: {product['price']}") if product.get('description'): product_info_parts.append(product['description']) product_info = "\n".join(product_info_parts) # 渲染审核 prompt context = { 'product_info': product_info, 'title_to_judge': content.get('title', ''), 'content_to_judge': content.get('content', ''), } system_prompt, user_prompt = prompt_registry.render( 'content_judge', context=context, version=prompt_version ) # 获取模型参数 judge_config = prompt_registry.get('content_judge', prompt_version) model_params = judge_config.get_model_params() # 调用 LLM raw_result, _, _, _ = await self.llm.generate( system_prompt=system_prompt, user_prompt=user_prompt, **model_params ) # 解析结果 result = self._parse_content(raw_result) if result: result['success'] = True return result except Exception as e: self.log(f"内容审核失败: {e}", level='warning') return None