import os from typing import Dict, List, Union, Optional import json from loguru import logger # IMPORTS REMOVED: agno.agent, get_model # Internal LLM logic has been removed to delegate analysis to the calling Agent. from .database_manager import DatabaseManager # 从环境变量读取默认情绪分析模式 DEFAULT_SENTIMENT_MODE = os.getenv("SENTIMENT_MODE", "auto") # auto, bert, llm class SentimentTools: """ 情绪分析工具 - 支持 LLM 和 BERT 两种模式 模式说明: - "auto": 自动选择，优先使用 BERT（速度快），不可用时回退到 LLM - "bert": 强制使用 BERT 模型（需要 transformers 库） - "llm": 强制使用 LLM（更准确但较慢） 可通过环境变量 SENTIMENT_MODE 设置默认模式。 """ def __init__(self, db: DatabaseManager, mode: Optional[str] = None): """ 初始化情绪分析工具。 Args: db: 数据库管理器实例 mode: 分析模式，可选 "auto", "bert", "llm"。None 则使用环境变量默认值。 model_provider: LLM 提供商，如 "openai", "ust", "deepseek" model_id: 模型标识符 """ self.db = db self.mode = mode or DEFAULT_SENTIMENT_MODE self.bert_pipeline = None # LLM initialization removed. Agent should perform analysis if needed. # Initialize BERT if needed if self.mode in ["bert", "auto"]: try: from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification from transformers.utils import logging as transformers_logging transformers_logging.set_verbosity_error() # 减少冗余日志 bert_model = os.getenv("BERT_SENTIMENT_MODEL", "uer/roberta-base-finetuned-chinanews-chinese") # 优先使用本地缓存 try: tokenizer = AutoTokenizer.from_pretrained(bert_model, local_files_only=True) model = AutoModelForSequenceClassification.from_pretrained(bert_model, local_files_only=True) self.bert_pipeline = pipeline( "sentiment-analysis", model=model, tokenizer=tokenizer, device=-1 ) logger.info(f"✅ BERT pipeline loaded from local cache: {bert_model}") except (OSError, ValueError, ImportError): # 本地没有，则从网络下载 logger.info(f"📡 Downloading BERT model: {bert_model}...") tokenizer = AutoTokenizer.from_pretrained(bert_model) model = AutoModelForSequenceClassification.from_pretrained(bert_model) self.bert_pipeline = pipeline( "sentiment-analysis", model=model, tokenizer=tokenizer, device=-1 ) logger.info(f"✅ BERT Sentiment pipeline ({bert_model}) initialized.") except ImportError: logger.warning("Transformers library not installed. BERT sentiment analysis disabled.") except Exception as e: if self.mode == "bert": logger.error(f"BERT mode requested but failed: {e}") else: logger.warning(f"BERT unavailable, using LLM only. Error: {e}") self.bert_pipeline = None def analyze_sentiment(self, text: str) -> Dict[str, Union[float, str]]: """ 分析文本的情绪极性。仅支持 BERT 模式。 如需 LLM 分析，请 Agent 按照 SKILL.md 中的 Prompt 自行执行。 Args: text: 需要分析的文本内容。 Returns: BERT 分析结果，或错误信息。 """ if self.bert_pipeline: results = self.analyze_sentiment_bert([text]) return results[0] if results else {"score": 0.0, "label": "error"} else: return { "score": 0.0, "label": "error", "reason": "BERT pipeline not initialized. For LLM analysis, please manually execute the prompt in SKILL.md." } def update_single_news_sentiment(self, news_id: Union[str, int], score: float, reason: str = "") -> bool: """ 允许 Agent 将手动分析的结果保存到数据库。 Args: news_id: 新闻 ID score: -1.0 到 1.0 reason: 分析理由 Returns: Success bool """ try: cursor = self.db.conn.cursor() cursor.execute(""" UPDATE daily_news SET sentiment_score = ?, meta_data = json_set(COALESCE(meta_data, '{}'), '$.sentiment_reason', ?) WHERE id = ? """, (score, reason, news_id)) self.db.conn.commit() return True except Exception as e: logger.error(f"Failed to update sentiment for {news_id}: {e}") return False def analyze_sentiment_bert(self, texts: List[str]) -> List[Dict]: """ 使用 BERT 进行批量高速情绪分析。 Args: texts: 需要分析的文本列表。 Returns: 与输入列表等长的分析结果列表。 """ if not self.bert_pipeline: return [{"score": 0.0, "label": "error", "reason": "BERT not available"}] * len(texts) try: results = self.bert_pipeline(texts, truncation=True, max_length=512) processed = [] for r in results: label = r['label'].lower() score = r['score'] # 标准化不同模型的标签格式 if 'negative' in label or 'neg' in label: score = -score elif 'neutral' in label or 'neu' in label: score = 0.0 processed.append({ "score": float(round(score, 3)), "label": "positive" if score > 0.1 else ("negative" if score < -0.1 else "neutral"), "reason": "BERT automated analysis" }) return processed except Exception as e: logger.error(f"BERT analysis failed: {e}") return [{"score": 0.0, "label": "error", "reason": str(e)}] * len(texts) def batch_update_news_sentiment(self, source: Optional[str] = None, limit: int = 50, use_bert: Optional[bool] = None): """ 批量更新数据库中新闻的情绪分数。 Args: source: 筛选特定新闻源，如 "wallstreetcn"。None 则处理所有来源。 limit: 最多处理的新闻数量。 use_bert: 是否使用 BERT。None 则根据初始化模式自动决定。 Returns: 成功更新的新闻数量。 """ news_items = self.db.get_daily_news(source=source, limit=limit) to_analyze = [item for item in news_items if not item.get('sentiment_score')] if not to_analyze: return 0 updated_count = 0 cursor = self.db.conn.cursor() # 决定使用哪种方法 if self.bert_pipeline: logger.info(f"🚀 Using BERT for batch analysis of {len(to_analyze)} items...") titles = [item['title'] for item in to_analyze] results = self.analyze_sentiment_bert(titles) for item, analysis in zip(to_analyze, results): cursor.execute(""" UPDATE daily_news SET sentiment_score = ?, meta_data = json_set(COALESCE(meta_data, '{}'), '$.sentiment_reason', ?) WHERE id = ? """, (analysis['score'], analysis['reason'], item['id'])) updated_count += 1 else: logger.warning("BERT pipeline not available. Batch update skipped. Please use Agentic analysis for high-quality results.") self.db.conn.commit() return updated_count