#!/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, List 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" engine_name = "内容生成" version = "2.1.0" description = "根据选题信息生成小红书风格的营销文案" def __init__(self): super().__init__() self._prompt_registry = None self._reference_manager = None def get_param_schema(self) -> Dict[str, Any]: """ 定义参数结构 V2.2: 合并 scenic_spot 和 product 为 subject """ return { # 选题信息 "topic": { "type": "object", "required": True, "desc": "选题信息 {index, date, title, subject_name, product_name, style, audience, ...}", }, # 主体信息 (景区+产品合并) "subject": { "type": "object", "required": False, "desc": "主体信息 {id, name, type, description, location, products: [...]}", }, # 兼容旧字段 "scenic_spot": { "type": "object", "required": False, "desc": "[兼容] 景区信息，建议使用 subject", }, "product": { "type": "object", "required": False, "desc": "[兼容] 产品信息，建议使用 subject.products", }, "style": { "type": "object", "required": False, "desc": "风格信息对象 {id, name}", }, "audience": { "type": "object", "required": False, "desc": "受众信息对象 {id, name}", }, # 热点信息 "hot_topics": { "type": "object", "required": False, "desc": "热点信息 {events: [], festivals: [], trending: []}", }, # 参考内容 "reference": { "type": "object", "required": False, "desc": "参考内容 {mode: 'none'/'reference'/'rewrite', title, content}. reference=参考风格原创内容, rewrite=保留框架换主体", }, # 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', {}) # 主体信息 (支持新旧两种格式) subject = params.get('subject') scenic_spot = params.get('scenic_spot') product = params.get('product') # 兼容处理: 如果没有 subject，从 scenic_spot + product 构建 if not subject and scenic_spot: subject = { **scenic_spot, 'type': scenic_spot.get('type', 'scenic_spot'), 'products': [product] if product else [] } style = params.get('style') audience = params.get('audience') hot_topics = params.get('hot_topics') reference = params.get('reference') 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() # 从 subject 提取产品信息 current_product = None if subject and subject.get('products'): current_product = subject['products'][0] # 如果没有用户指定的参考内容，加载内置参考文献库 use_builtin_examples = not reference or reference.get('mode') == 'none' # 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_subject(subject, topic), 'product_content': self._format_product(current_product), 'hot_topics': hot_topics, 'reference': reference, # 内置参考文献 (仅在无用户指定参考时使用) 'title_examples': self._get_title_examples(20) if use_builtin_examples else None, 'content_examples': self._get_content_examples(3) if use_builtin_examples else None, } # 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 _get_reference_manager(self): """获取 ReferenceManager""" if self._reference_manager is None: from domain.prompt.reference_manager import get_reference_manager self._reference_manager = get_reference_manager() return self._reference_manager def _get_title_examples(self, audience_id: str = None, style_id: str = None, count: int = 20) -> List[str]: """获取标题参考格式 (智能匹配 + 随机抽取)""" manager = self._get_reference_manager() return manager.get_titles(audience_id=audience_id, style_id=style_id, count=count) def _get_content_examples(self, audience_id: str = None, style_id: str = None, count: int = 3) -> List[str]: """获取正文范文参考 (智能匹配 + 随机抽取)""" manager = self._get_reference_manager() return manager.get_contents(audience_id=audience_id, style_id=style_id, count=count) 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_subject(self, subject: Optional[Dict], topic: Dict) -> str: """格式化主体信息 (景区/酒店等)""" if subject: name = subject.get('name', '') desc = subject.get('description', '') location = subject.get('location', '') highlights = subject.get('highlights', []) parts = [f"名称: {name}"] if location: parts.append(f"位置: {location}") if highlights: parts.append(f"亮点: {', '.join(highlights)}") if desc: parts.append(f"描述: {desc}") return "\n".join(parts) # 兼容旧字段 return topic.get('object', topic.get('subject_name', '')) def _format_scenic_spot(self, scenic_spot: Optional[Dict], topic: Dict) -> str: """[兼容] 格式化景区信息""" return self._format_subject(scenic_spot, topic) 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