# coding=utf-8 """ AI 分析器模块 调用 AI 大模型对热点新闻进行深度分析 基于 LiteLLM 统一接口，支持 100+ AI 提供商 """ import json from dataclasses import dataclass from pathlib import Path from typing import Any, Callable, Dict, List, Optional from trendradar.ai.client import AIClient @dataclass class AIAnalysisResult: """AI 分析结果""" # 新版 5 核心板块 core_trends: str = "" # 核心热点与舆情态势 sentiment_controversy: str = "" # 舆论风向与争议 signals: str = "" # 异动与弱信号 rss_insights: str = "" # RSS 深度洞察 outlook_strategy: str = "" # 研判与策略建议 # 基础元数据 raw_response: str = "" # 原始响应 success: bool = False # 是否成功 error: str = "" # 错误信息 # 新闻数量统计 total_news: int = 0 # 总新闻数（热榜+RSS） analyzed_news: int = 0 # 实际分析的新闻数 max_news_limit: int = 0 # 分析上限配置值 hotlist_count: int = 0 # 热榜新闻数 rss_count: int = 0 # RSS 新闻数 class AIAnalyzer: """AI 分析器""" def __init__( self, ai_config: Dict[str, Any], analysis_config: Dict[str, Any], get_time_func: Callable, debug: bool = False, ): """ 初始化 AI 分析器 Args: ai_config: AI 模型配置（LiteLLM 格式） analysis_config: AI 分析功能配置（language, prompt_file 等） get_time_func: 获取当前时间的函数 debug: 是否开启调试模式 """ self.ai_config = ai_config self.analysis_config = analysis_config self.get_time_func = get_time_func self.debug = debug # 创建 AI 客户端（基于 LiteLLM） self.client = AIClient(ai_config) # 验证配置 valid, error = self.client.validate_config() if not valid: print(f"[AI] 配置警告: {error}") # 从分析配置获取功能参数 self.max_news = analysis_config.get("MAX_NEWS_FOR_ANALYSIS", 50) self.include_rss = analysis_config.get("INCLUDE_RSS", True) self.include_rank_timeline = analysis_config.get("INCLUDE_RANK_TIMELINE", False) self.language = analysis_config.get("LANGUAGE", "Chinese") # 加载提示词模板 self.system_prompt, self.user_prompt_template = self._load_prompt_template( analysis_config.get("PROMPT_FILE", "ai_analysis_prompt.txt") ) def _load_prompt_template(self, prompt_file: str) -> tuple: """加载提示词模板""" config_dir = Path(__file__).parent.parent.parent / "config" prompt_path = config_dir / prompt_file if not prompt_path.exists(): print(f"[AI] 提示词文件不存在: {prompt_path}") return "", "" content = prompt_path.read_text(encoding="utf-8") # 解析 [system] 和 [user] 部分 system_prompt = "" user_prompt = "" if "[system]" in content and "[user]" in content: parts = content.split("[user]") system_part = parts[0] user_part = parts[1] if len(parts) > 1 else "" # 提取 system 内容 if "[system]" in system_part: system_prompt = system_part.split("[system]")[1].strip() user_prompt = user_part.strip() else: # 整个文件作为 user prompt user_prompt = content return system_prompt, user_prompt def analyze( self, stats: List[Dict], rss_stats: Optional[List[Dict]] = None, report_mode: str = "daily", report_type: str = "当日汇总", platforms: Optional[List[str]] = None, keywords: Optional[List[str]] = None, ) -> AIAnalysisResult: """ 执行 AI 分析 Args: stats: 热榜统计数据 rss_stats: RSS 统计数据 report_mode: 报告模式 report_type: 报告类型 platforms: 平台列表 keywords: 关键词列表 Returns: AIAnalysisResult: 分析结果 """ if not self.client.api_key: return AIAnalysisResult( success=False, error="未配置 AI API Key，请在 config.yaml 或环境变量 AI_API_KEY 中设置" ) # 准备新闻内容并获取统计数据 news_content, rss_content, hotlist_total, rss_total, analyzed_count = self._prepare_news_content(stats, rss_stats) total_news = hotlist_total + rss_total if not news_content and not rss_content: return AIAnalysisResult( success=False, error="没有可分析的新闻内容", total_news=total_news, hotlist_count=hotlist_total, rss_count=rss_total, analyzed_news=0, max_news_limit=self.max_news ) # 构建提示词 current_time = self.get_time_func().strftime("%Y-%m-%d %H:%M:%S") # 提取关键词 if not keywords: keywords = [s.get("word", "") for s in stats if s.get("word")] if stats else [] # 使用安全的字符串替换，避免模板中其他花括号（如 JSON 示例）被误解析 user_prompt = self.user_prompt_template user_prompt = user_prompt.replace("{report_mode}", report_mode) user_prompt = user_prompt.replace("{report_type}", report_type) user_prompt = user_prompt.replace("{current_time}", current_time) user_prompt = user_prompt.replace("{news_count}", str(hotlist_total)) user_prompt = user_prompt.replace("{rss_count}", str(rss_total)) user_prompt = user_prompt.replace("{platforms}", ", ".join(platforms) if platforms else "多平台") user_prompt = user_prompt.replace("{keywords}", ", ".join(keywords[:20]) if keywords else "无") user_prompt = user_prompt.replace("{news_content}", news_content) user_prompt = user_prompt.replace("{rss_content}", rss_content) user_prompt = user_prompt.replace("{language}", self.language) if self.debug: print("\n" + "=" * 80) print("[AI 调试] 发送给 AI 的完整提示词") print("=" * 80) if self.system_prompt: print("\n--- System Prompt ---") print(self.system_prompt) print("\n--- User Prompt ---") print(user_prompt) print("=" * 80 + "\n") # 调用 AI API（使用 LiteLLM） try: response = self._call_ai(user_prompt) result = self._parse_response(response) # 如果配置未启用 RSS 分析，强制清空 AI 返回的 RSS 洞察 if not self.include_rss: result.rss_insights = "" # 填充统计数据 result.total_news = total_news result.hotlist_count = hotlist_total result.rss_count = rss_total result.analyzed_news = analyzed_count result.max_news_limit = self.max_news return result except Exception as e: error_type = type(e).__name__ error_msg = str(e) # 截断过长的错误消息 if len(error_msg) > 200: error_msg = error_msg[:200] + "..." friendly_msg = f"AI 分析失败 ({error_type}): {error_msg}" return AIAnalysisResult( success=False, error=friendly_msg ) def _prepare_news_content( self, stats: List[Dict], rss_stats: Optional[List[Dict]] = None, ) -> tuple: """ 准备新闻内容文本（增强版） 热榜新闻包含：来源、标题、排名范围、时间范围、出现次数 RSS 包含：来源、标题、发布时间 Returns: tuple: (news_content, rss_content, hotlist_total, rss_total, analyzed_count) """ news_lines = [] rss_lines = [] news_count = 0 rss_count = 0 # 计算总新闻数 hotlist_total = sum(len(s.get("titles", [])) for s in stats) if stats else 0 rss_total = sum(len(s.get("titles", [])) for s in rss_stats) if rss_stats else 0 # 热榜内容 if stats: for stat in stats: word = stat.get("word", "") titles = stat.get("titles", []) if word and titles: news_lines.append(f"\n**{word}** ({len(titles)}条)") for t in titles: if not isinstance(t, dict): continue title = t.get("title", "") if not title: continue # 来源 source = t.get("source_name", t.get("source", "")) # 构建行 if source: line = f"- [{source}] {title}" else: line = f"- {title}" # 始终显示简化格式：排名范围 + 时间范围 + 出现次数 ranks = t.get("ranks", []) if ranks: min_rank = min(ranks) max_rank = max(ranks) rank_str = f"{min_rank}" if min_rank == max_rank else f"{min_rank}-{max_rank}" else: rank_str = "-" first_time = t.get("first_time", "") last_time = t.get("last_time", "") time_str = self._format_time_range(first_time, last_time) appear_count = t.get("count", 1) line += f" | 排名:{rank_str} | 时间:{time_str} | 出现:{appear_count}次" # 开启完整时间线时，额外添加轨迹 if self.include_rank_timeline: rank_timeline = t.get("rank_timeline", []) timeline_str = self._format_rank_timeline(rank_timeline) line += f" | 轨迹:{timeline_str}" news_lines.append(line) news_count += 1 if news_count >= self.max_news: break if news_count >= self.max_news: break # RSS 内容（仅在启用时构建） if self.include_rss and rss_stats: remaining = self.max_news - news_count for stat in rss_stats: if rss_count >= remaining: break word = stat.get("word", "") titles = stat.get("titles", []) if word and titles: rss_lines.append(f"\n**{word}** ({len(titles)}条)") for t in titles: if not isinstance(t, dict): continue title = t.get("title", "") if not title: continue # 来源 source = t.get("source_name", t.get("feed_name", "")) # 发布时间 time_display = t.get("time_display", "") # 构建行：[来源] 标题 | 发布时间 if source: line = f"- [{source}] {title}" else: line = f"- {title}" if time_display: line += f" | {time_display}" rss_lines.append(line) rss_count += 1 if rss_count >= remaining: break news_content = "\n".join(news_lines) if news_lines else "" rss_content = "\n".join(rss_lines) if rss_lines else "" total_count = news_count + rss_count return news_content, rss_content, hotlist_total, rss_total, total_count def _call_ai(self, user_prompt: str) -> str: """调用 AI API（使用 LiteLLM）""" messages = [] if self.system_prompt: messages.append({"role": "system", "content": self.system_prompt}) messages.append({"role": "user", "content": user_prompt}) return self.client.chat(messages) def _format_time_range(self, first_time: str, last_time: str) -> str: """格式化时间范围（简化显示，只保留时分）""" def extract_time(time_str: str) -> str: if not time_str: return "-" # 尝试提取 HH:MM 部分 if " " in time_str: parts = time_str.split(" ") if len(parts) >= 2: time_part = parts[1] if ":" in time_part: return time_part[:5] # HH:MM elif ":" in time_str: return time_str[:5] # 处理 HH-MM 格式 result = time_str[:5] if len(time_str) >= 5 else time_str if len(result) == 5 and result[2] == '-': result = result.replace('-', ':') return result first = extract_time(first_time) last = extract_time(last_time) if first == last or last == "-": return first return f"{first}~{last}" def _format_rank_timeline(self, rank_timeline: List[Dict]) -> str: """格式化排名时间线""" if not rank_timeline: return "-" parts = [] for item in rank_timeline: time_str = item.get("time", "") if len(time_str) == 5 and time_str[2] == '-': time_str = time_str.replace('-', ':') rank = item.get("rank") if rank is None: parts.append(f"0({time_str})") else: parts.append(f"{rank}({time_str})") return "→".join(parts) def _parse_response(self, response: str) -> AIAnalysisResult: """解析 AI 响应""" result = AIAnalysisResult(raw_response=response) if not response or not response.strip(): result.error = "AI 返回空响应" return result # 尝试解析 JSON try: # 提取 JSON 部分 json_str = response if "```json" in response: parts = response.split("```json", 1) if len(parts) > 1: code_block = parts[1] end_idx = code_block.find("```") if end_idx != -1: json_str = code_block[:end_idx] else: json_str = code_block elif "```" in response: parts = response.split("```", 2) if len(parts) >= 2: json_str = parts[1] json_str = json_str.strip() if not json_str: raise ValueError("提取的 JSON 内容为空") data = json.loads(json_str) # 新版字段解析 result.core_trends = data.get("core_trends", "") result.sentiment_controversy = data.get("sentiment_controversy", "") result.signals = data.get("signals", "") result.rss_insights = data.get("rss_insights", "") result.outlook_strategy = data.get("outlook_strategy", "") result.success = True except json.JSONDecodeError as e: error_context = json_str[max(0, e.pos - 30):e.pos + 30] if json_str and e.pos else "" result.error = f"JSON 解析错误 (位置 {e.pos}): {e.msg}" if error_context: result.error += f"，上下文: ...{error_context}..." # 使用原始响应填充 core_trends，确保有输出 result.core_trends = response[:500] + "..." if len(response) > 500 else response result.success = True except (IndexError, KeyError, TypeError, ValueError) as e: result.error = f"响应解析错误: {type(e).__name__}: {str(e)}" result.core_trends = response[:500] if len(response) > 500 else response result.success = True except Exception as e: result.error = f"解析时发生未知错误: {type(e).__name__}: {str(e)}" result.core_trends = response[:500] if len(response) > 500 else response result.success = True return result