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Update alphaear skills to use MiniMax as default LLM

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Changes:
- Added 'minimax' provider to LLM factory
- Changed default LLM_PROVIDER from 'ust' to 'minimax'
- Changed default LLM_MODEL from 'Qwen' to 'MiniMax-Text-01'
- Updated REASONING_MODEL_PROVIDER and TOOL_MODEL_PROVIDER to minimax
- Sentiment tools now prefer MINIMAX_API_KEY over UST_KEY_API
- .env.example updated with MiniMax defaults
This commit is contained in:
Kunthawat Greethong
2026-03-27 10:27:00 +07:00
parent 58f9380ec4
commit 17930e9650
19 changed files with 425 additions and 680 deletions
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180
skills/alphaear-reporter/scripts/utils/sentiment_tools.py
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@@ -9,23 +9,29 @@ 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,
model_provider: str = "openai", model_id: str = "gpt-4o"):
def __init__(
self,
db: DatabaseManager,
mode: Optional[str] = None,
model_provider: str = "openai",
model_id: str = "gpt-4o",
):
"""
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。
@@ -36,11 +42,15 @@ class SentimentTools:
self.mode = mode or DEFAULT_SENTIMENT_MODE
self.llm_model = None
self.bert_pipeline = None
# Initialize LLM
try:
provider = "ust" if os.getenv("UST_KEY_API") else model_provider
m_id = "Qwen" if provider == "ust" else model_id
provider = "minimax" if os.getenv("MINIMAX_API_KEY") else model_provider
m_id = (
os.getenv("LLM_MODEL", "MiniMax-Text-01")
if provider == "minimax"
else model_id
)
self.llm_model = get_model(provider, m_id)
except Exception as e:
logger.warning(f"LLM initialization skipped: {e}")
@@ -48,39 +58,59 @@ class SentimentTools:
# Initialize BERT if needed
if self.mode in ["bert", "auto"]:
try:
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
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")
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)
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",
"sentiment-analysis",
model=model,
tokenizer=tokenizer,
device=-1
device=-1,
)
logger.info(
f"✅ BERT pipeline loaded from local cache: {bert_model}"
)
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)
model = AutoModelForSequenceClassification.from_pretrained(
bert_model
)
self.bert_pipeline = pipeline(
"sentiment-analysis",
"sentiment-analysis",
model=model,
tokenizer=tokenizer,
device=-1
device=-1,
)
logger.info(
f"✅ BERT Sentiment pipeline ({bert_model}) initialized."
)
logger.info(f"✅ BERT Sentiment pipeline ({bert_model}) initialized.")
except ImportError:
logger.warning("Transformers library not installed. BERT sentiment analysis disabled.")
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}")
@@ -88,14 +118,13 @@ class SentimentTools:
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。根据初始化时的 mode 自动选择分析方法。
Args:
text: 需要分析的文本内容，如新闻标题或摘要。
Returns:
包含以下字段的字典:
- score: 情绪分值，范围 -1.0（极度负面）到 1.0（极度正面），0.0 为中性
@@ -115,10 +144,10 @@ class SentimentTools:
def analyze_sentiment_llm(self, text: str) -> Dict[str, Union[float, str]]:
"""
使用 LLM 进行深度情绪分析，可获得详细的分析理由。
Args:
text: 需要分析的文本，最多处理前 1000 字符。
Returns:
包含 score, label, reason 的字典。
"""
@@ -147,85 +176,112 @@ class SentimentTools:
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)
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']
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:
if "negative" in label or "neg" in label:
score = -score
elif 'neutral' in label or 'neu' in label:
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"
})
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):
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')]
to_analyze = [item for item in news_items if not item.get("sentiment_score")]
if not to_analyze:
return 0
# 决定使用哪种方法
should_use_bert = use_bert if use_bert is not None else (self.bert_pipeline is not None and self.mode != "llm")
should_use_bert = (
use_bert
if use_bert is not None
else (self.bert_pipeline is not None and self.mode != "llm")
)
updated_count = 0
cursor = self.db.conn.cursor()
if should_use_bert and self.bert_pipeline:
logger.info(f"🚀 Using BERT for batch analysis of {len(to_analyze)} items...")
titles = [item['title'] for item in to_analyze]
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("""
cursor.execute(
"""
UPDATE daily_news
SET sentiment_score = ?, meta_data = json_set(COALESCE(meta_data, '{}'), '$.sentiment_reason', ?)
WHERE id = ?
""", (analysis['score'], analysis['reason'], item['id']))
""",
(analysis["score"], analysis["reason"], item["id"]),
)
updated_count += 1
else:
logger.info(f"🚶 Using LLM for analysis of {len(to_analyze)} items...")
for item in to_analyze:
analysis = self.analyze_sentiment_llm(item['title'])
cursor.execute("""
analysis = self.analyze_sentiment_llm(item["title"])
cursor.execute(
"""
UPDATE daily_news
SET sentiment_score = ?, meta_data = json_set(COALESCE(meta_data, '{}'), '$.sentiment_reason', ?)
WHERE id = ?
""", (analysis.get('score', 0.0), analysis.get('reason', ''), item['id']))
""",
(
analysis.get("score", 0.0),
analysis.get("reason", ""),
item["id"],
),
)
updated_count += 1
self.db.conn.commit()
return updated_count