Import 9 alphaear finance skills
- alphaear-deepear-lite: DeepEar Lite API integration - alphaear-logic-visualizer: Draw.io XML finance diagrams - alphaear-news: Real-time finance news (10+ sources) - alphaear-predictor: Kronos time-series forecasting - alphaear-reporter: Professional financial reports - alphaear-search: Web search + local RAG - alphaear-sentiment: FinBERT/LLM sentiment analysis - alphaear-signal-tracker: Signal evolution tracking - alphaear-stock: A-Share/HK/US stock data Updates: - All scripts updated to use universal .env path - Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example - Updated load_dotenv() to use ~/.config/opencode/.env
This commit is contained in:
Kunthawat Greethong
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skills/alphaear-sentiment/scripts/sentiment_tools.py
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skills/alphaear-sentiment/scripts/sentiment_tools.py
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import os
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from typing import Dict, List, Union, Optional
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import json
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from loguru import logger
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# IMPORTS REMOVED: agno.agent, get_model
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# Internal LLM logic has been removed to delegate analysis to the calling Agent.
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from .database_manager import DatabaseManager
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# 从环境变量读取默认情绪分析模式
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DEFAULT_SENTIMENT_MODE = os.getenv("SENTIMENT_MODE", "auto") # auto, bert, llm
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class SentimentTools:
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"""
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情绪分析工具 - 支持 LLM 和 BERT 两种模式
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模式说明:
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- "auto": 自动选择，优先使用 BERT（速度快），不可用时回退到 LLM
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- "bert": 强制使用 BERT 模型（需要 transformers 库）
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- "llm": 强制使用 LLM（更准确但较慢）
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可通过环境变量 SENTIMENT_MODE 设置默认模式。
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"""
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def __init__(self, db: DatabaseManager, mode: Optional[str] = None):
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"""
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初始化情绪分析工具。
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Args:
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db: 数据库管理器实例
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mode: 分析模式，可选 "auto", "bert", "llm"。None 则使用环境变量默认值。
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model_provider: LLM 提供商，如 "openai", "ust", "deepseek"
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model_id: 模型标识符
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"""
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self.db = db
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self.mode = mode or DEFAULT_SENTIMENT_MODE
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self.bert_pipeline = None
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# LLM initialization removed. Agent should perform analysis if needed.
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# Initialize BERT if needed
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if self.mode in ["bert", "auto"]:
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try:
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
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from transformers.utils import logging as transformers_logging
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transformers_logging.set_verbosity_error() # 减少冗余日志
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bert_model = os.getenv("BERT_SENTIMENT_MODEL", "uer/roberta-base-finetuned-chinanews-chinese")
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# 优先使用本地缓存
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try:
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tokenizer = AutoTokenizer.from_pretrained(bert_model, local_files_only=True)
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model = AutoModelForSequenceClassification.from_pretrained(bert_model, local_files_only=True)
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self.bert_pipeline = pipeline(
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"sentiment-analysis",
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model=model,
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tokenizer=tokenizer,
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device=-1
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)
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logger.info(f"✅ BERT pipeline loaded from local cache: {bert_model}")
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except (OSError, ValueError, ImportError):
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# 本地没有，则从网络下载
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logger.info(f"📡 Downloading BERT model: {bert_model}...")
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tokenizer = AutoTokenizer.from_pretrained(bert_model)
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model = AutoModelForSequenceClassification.from_pretrained(bert_model)
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self.bert_pipeline = pipeline(
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"sentiment-analysis",
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model=model,
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tokenizer=tokenizer,
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device=-1
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)
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logger.info(f"✅ BERT Sentiment pipeline ({bert_model}) initialized.")
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except ImportError:
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logger.warning("Transformers library not installed. BERT sentiment analysis disabled.")
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except Exception as e:
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if self.mode == "bert":
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logger.error(f"BERT mode requested but failed: {e}")
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else:
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logger.warning(f"BERT unavailable, using LLM only. Error: {e}")
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self.bert_pipeline = None
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def analyze_sentiment(self, text: str) -> Dict[str, Union[float, str]]:
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"""
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分析文本的情绪极性。仅支持 BERT 模式。
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如需 LLM 分析，请 Agent 按照 SKILL.md 中的 Prompt 自行执行。
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Args:
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text: 需要分析的文本内容。
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Returns:
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BERT 分析结果，或错误信息。
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"""
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if self.bert_pipeline:
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results = self.analyze_sentiment_bert([text])
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return results[0] if results else {"score": 0.0, "label": "error"}
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else:
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return {
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"score": 0.0,
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"label": "error",
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"reason": "BERT pipeline not initialized. For LLM analysis, please manually execute the prompt in SKILL.md."
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}
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def update_single_news_sentiment(self, news_id: Union[str, int], score: float, reason: str = "") -> bool:
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"""
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允许 Agent 将手动分析的结果保存到数据库。
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Args:
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news_id: 新闻 ID
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score: -1.0 到 1.0
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reason: 分析理由
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Returns:
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Success bool
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"""
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try:
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cursor = self.db.conn.cursor()
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cursor.execute("""
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UPDATE daily_news
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SET sentiment_score = ?, meta_data = json_set(COALESCE(meta_data, '{}'), '$.sentiment_reason', ?)
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WHERE id = ?
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""", (score, reason, news_id))
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self.db.conn.commit()
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return True
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except Exception as e:
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logger.error(f"Failed to update sentiment for {news_id}: {e}")
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return False
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def analyze_sentiment_bert(self, texts: List[str]) -> List[Dict]:
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"""
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使用 BERT 进行批量高速情绪分析。
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Args:
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texts: 需要分析的文本列表。
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Returns:
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与输入列表等长的分析结果列表。
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"""
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if not self.bert_pipeline:
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return [{"score": 0.0, "label": "error", "reason": "BERT not available"}] * len(texts)
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try:
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results = self.bert_pipeline(texts, truncation=True, max_length=512)
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processed = []
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for r in results:
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label = r['label'].lower()
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score = r['score']
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# 标准化不同模型的标签格式
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if 'negative' in label or 'neg' in label:
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score = -score
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elif 'neutral' in label or 'neu' in label:
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score = 0.0
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processed.append({
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"score": float(round(score, 3)),
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"label": "positive" if score > 0.1 else ("negative" if score < -0.1 else "neutral"),
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"reason": "BERT automated analysis"
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})
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return processed
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except Exception as e:
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logger.error(f"BERT analysis failed: {e}")
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return [{"score": 0.0, "label": "error", "reason": str(e)}] * len(texts)
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def batch_update_news_sentiment(self, source: Optional[str] = None, limit: int = 50, use_bert: Optional[bool] = None):
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"""
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批量更新数据库中新闻的情绪分数。
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Args:
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source: 筛选特定新闻源，如 "wallstreetcn"。None 则处理所有来源。
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limit: 最多处理的新闻数量。
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use_bert: 是否使用 BERT。None 则根据初始化模式自动决定。
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Returns:
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成功更新的新闻数量。
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"""
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news_items = self.db.get_daily_news(source=source, limit=limit)
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to_analyze = [item for item in news_items if not item.get('sentiment_score')]
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if not to_analyze:
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return 0
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updated_count = 0
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cursor = self.db.conn.cursor()
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# 决定使用哪种方法
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if self.bert_pipeline:
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logger.info(f"🚀 Using BERT for batch analysis of {len(to_analyze)} items...")
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titles = [item['title'] for item in to_analyze]
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results = self.analyze_sentiment_bert(titles)
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for item, analysis in zip(to_analyze, results):
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cursor.execute("""
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UPDATE daily_news
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SET sentiment_score = ?, meta_data = json_set(COALESCE(meta_data, '{}'), '$.sentiment_reason', ?)
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WHERE id = ?
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""", (analysis['score'], analysis['reason'], item['id']))
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updated_count += 1
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else:
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logger.warning("BERT pipeline not available. Batch update skipped. Please use Agentic analysis for high-quality results.")
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self.db.conn.commit()
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return updated_count
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