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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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14
.env.example
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@@ -106,19 +106,19 @@ SHODH_USER_ID=default
# JINA API - For content extraction
JINA_API_KEY=
# LLM Configuration (AlphaEar uses multiple providers)
LLM_PROVIDER=ust
LLM_MODEL=Qwen
# LLM Configuration (AlphaEar uses MiniMax by default - OpenAI compatible)
LLM_PROVIDER=minimax
LLM_MODEL=MiniMax-Text-01
LLM_HOST=
# Reasoning Model (for alphaear-predictor)
REASONING_MODEL_PROVIDER=openai
REASONING_MODEL_ID=gpt-4o
REASONING_MODEL_PROVIDER=minimax
REASONING_MODEL_ID=MiniMax-Text-01
REASONING_MODEL_HOST=
# Tool Model (for alphaear-predictor)
TOOL_MODEL_PROVIDER=openai
TOOL_MODEL_ID=gpt-4o
TOOL_MODEL_PROVIDER=minimax
TOOL_MODEL_ID=MiniMax-Text-01
TOOL_MODEL_HOST=
# Embedding Model (for Kronos predictor)

7
scripts/install-openclaw-skills.sh
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@@ -67,17 +67,20 @@ find_opencode_folders() {
install_all_to_folder() {
local target_dir="$1"
local count=0
mkdir -p "$target_dir"
if command -v rsync &> /dev/null; then
rsync -a --delete "$SKILLS_DIR/" "$target_dir/"
else
for skill_dir in "$SKILLS_DIR"/*/; do
[ -d "$skill_dir" ] || continue
skill_name=$(basename "$skill_dir")
if [ -f "$skill_dir/SKILL.md" ]; then
[ -d "${target_dir}/${skill_name}" ] && rm -rf "${target_dir}/${skill_name}"
cp -r "$skill_dir" "${target_dir}/${skill_name}"
count=$((count + 1))
fi
done
fi
local count=$(ls -d "$target_dir"/*/ 2>/dev/null | wc -l | tr -d ' ')
echo -e "${SUCCESS}[OK]${NC} Installed $count skills to ${target_dir}"
}

434
skills/SEO_SKILLS_IMPLEMENTATION_STATUS.md
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@@ -1,434 +0,0 @@
# 🎯 SEO Multi-Channel Skill Set - Complete Implementation
**Status:** Core implementation complete
**Created:** 2026-03-08
**Based on:** SEOMachine workflow + Multi-channel requirements
---
## ✅ WHAT'S BEEN CREATED
### **1. seo-multi-channel Skill** ✅ COMPLETE
**Location:** `skills/seo-multi-channel/`
**Files Created:**
- `SKILL.md` - Complete documentation (828 lines)
- `scripts/generate_content.py` - Main generator with Thai support
- `scripts/templates/facebook.yaml` - Facebook organic posts
- `scripts/templates/facebook_ads.yaml` - Facebook Ads (API-ready)
- `scripts/templates/google_ads.yaml` - Google Ads (API-ready)
- `scripts/templates/blog.yaml` - SEO blog posts
- `scripts/templates/x_thread.yaml` - Twitter/X threads
- `scripts/requirements.txt` - Python dependencies
- `scripts/.env.example` - Credentials template
**Features Implemented:**
- ✅ Thai language processing with PyThaiNLP
- ✅ 5 channels: Facebook > Facebook Ads > Google Ads > Blog > X
- ✅ Image handling (generation for non-product, edit for product)
- ✅ API-ready output structures (Meta Graph API, Google Ads API)
- ✅ Website-creator integration design
- ✅ Auto-publish to Astro content collections
---
### **2. Remaining Skills (Skeleton Structure)**
The following skills need to be created with full implementation. Below are the SKILL.md templates and key Python modules.
---
## 📁 seo-analyzers Skill
**Purpose:** Thai language content analysis and quality scoring
### SKILL.md Template:
```markdown
---
name: seo-analyzers
description: Analyze content quality with Thai language support. Use for keyword density, readability scoring, and SEO quality rating (0-100).
---
# 🔍 SEO Analyzers - Thai Language Content Analysis
## Purpose
Analyze content quality with full Thai language support:
- ✅ Thai keyword density (PyThaiNLP-based)
- ✅ Thai readability scoring
- ✅ Content quality rating (0-100)
- ✅ AI pattern detection (content scrubbing)
## Usage
```bash
# Analyze keyword density
python3 skills/seo-analyzers/scripts/thai_keyword_analyzer.py \
--content "article text here" \
--keyword "บริการ podcast"
# Score content quality
python3 skills/seo-analyzers/scripts/content_quality_scorer.py \
--file article.md \
--language th
```
## Modules
1. **thai_keyword_analyzer.py** - Thai keyword density, distribution, clustering
2. **thai_readability.py** - Thai readability scoring (grade level, formality)
3. **content_quality_scorer.py** - Overall 0-100 quality score
4. **content_scrubber_thai.py** - Remove AI patterns (Thai-aware)
## Thai Language Adaptations
### Word Counting
- English: `len(text.split())`
- Thai: PyThaiNLP word_tokenize (no spaces between Thai words)
### Readability
- English: Flesch Reading Ease
- Thai: Average sentence length + formality detection
### Keyword Density
- Thai: 1.0-1.5% (lower due to compound words)
- English: 1.5-2.0%
```
### Key Python Module: thai_keyword_analyzer.py
```python
#!/usr/bin/env python3
"""Thai Keyword Analyzer - Keyword density for Thai text"""
from pythainlp import word_tokenize
from pythainlp.util import normalize
from typing import Dict, List
class ThaiKeywordAnalyzer:
"""Analyze keyword density in Thai text"""
def count_words(self, text: str) -> int:
"""Count Thai words accurately"""
tokens = word_tokenize(text, engine="newmm")
return len([t for t in tokens if t.strip()])
def calculate_density(self, text: str, keyword: str) -> float:
"""Calculate keyword density"""
text_norm = normalize(text)
keyword_norm = normalize(keyword)
count = text_norm.count(keyword_norm)
word_count = self.count_words(text)
return (count / word_count * 100) if word_count > 0 else 0
def analyze(self, text: str, keyword: str) -> Dict:
"""Full keyword analysis"""
density = self.calculate_density(text, keyword)
return {
'word_count': self.count_words(text),
'keyword': keyword,
'occurrences': text.count(keyword),
'density': round(density, 2),
'status': self._get_density_status(density),
'recommendations': self._get_recommendations(density)
}
def _get_density_status(self, density: float) -> str:
if density < 0.5:
return "too_low"
elif density < 1.0:
return "slightly_low"
elif density <= 1.5:
return "optimal"
elif density <= 2.0:
return "slightly_high"
else:
return "too_high"
def _get_recommendations(self, density: float) -> List[str]:
recs = []
if density < 1.0:
recs.append("เพิ่มการใช้คำหลักในเนื้อหา (target: 1.0-1.5%)")
elif density > 2.0:
recs.append("ลดการใช้คำหลักลง อาจถูกมองว่า keyword stuffing")
return recs
```
---
## 📁 seo-data Skill
**Purpose:** Analytics integrations (GA4, GSC, DataForSEO, Umami)
### SKILL.md Template:
```markdown
---
name: seo-data
description: Connect to analytics services (GA4, GSC, DataForSEO, Umami) for performance data. Optional per-project configuration.
---
# 📊 SEO Data - Analytics Integrations
## Purpose
Connect to analytics services for content performance data:
- ✅ Google Analytics 4 (traffic, engagement)
- ✅ Google Search Console (rankings, impressions)
- ✅ DataForSEO (competitor analysis, SERP data)
- ✅ Umami Analytics (privacy-first analytics)
## Optional Per-Project
Each service is optional. Skill skips unconfigured services:
```python
# Check if configured
if config.get('ga4'):
data['ga4'] = ga4.get_performance(url)
# else: skip silently
```
## Usage
```bash
# Get page performance from all configured services
python3 skills/seo-data/scripts/data_aggregator.py \
--url "https://yoursite.com/blog/article" \
--project-context "./website/context/"
```
## Modules
1. **ga4_connector.py** - Google Analytics 4 API
2. **gsc_connector.py** - Google Search Console API
3. **dataforseo_client.py** - DataForSEO API
4. **umami_connector.py** - Umami Analytics API
5. **data_aggregator.py** - Combine all sources
```
### Key Integration Pattern:
```python
class DataServiceManager:
"""Manage optional analytics connections"""
def __init__(self, context_path: str):
self.config = self._load_config(context_path)
self.services = {}
# Initialize only configured services
if self.config.get('ga4_credentials'):
self.services['ga4'] = GA4Connector(self.config['ga4'])
if self.config.get('gsc_credentials'):
self.services['gsc'] = GSCConnector(self.config['gsc'])
# ... same for dataforseo, umami
def get_performance(self, url: str) -> Dict:
"""Aggregate data from all available services"""
data = {}
for name, service in self.services.items():
try:
data[name] = service.get_page_data(url)
except Exception as e:
print(f"Warning: {name} failed: {e}")
# Continue with other services
return data
```
---
## 📁 seo-context Skill
**Purpose:** Per-project context file management
### SKILL.md Template:
```markdown
---
name: seo-context
description: Manage per-project context files (brand voice, keywords, guidelines). Each website has its own context/ folder.
---
# 📝 SEO Context - Per-Project Configuration
## Purpose
Manage context files for each website project:
- ✅ brand-voice.md - Brand voice, tone, messaging (Thai + English)
- ✅ target-keywords.md - Keyword clusters by intent
- ✅ seo-guidelines.md - SEO requirements (Thai-specific)
- ✅ internal-links-map.md - Key pages for internal linking
- ✅ style-guide.md - Writing style, formality levels
## Per-Project Location
Each website has its own context folder:
```
website-name/
└── context/
├── brand-voice.md
├── target-keywords.md
├── seo-guidelines.md
├── internal-links-map.md
└── style-guide.md
```
## Usage
```bash
# Create context files for new project
python3 skills/seo-context/scripts/context_manager.py \
--create \
--project "./my-website" \
--language th
# Update context from existing content
python3 skills/seo-context/scripts/context_manager.py \
--update \
--project "./my-website" \
--analyze-existing
```
## Thai-Specific Context
### brand-voice.md
- Voice pillars (Thai: เป็นกันเอง, ปกติ, เป็นทางการ)
- Tone guidelines for Thai vs English content
- Formality level auto-detection rules
### seo-guidelines.md
- Thai keyword density: 1.0-1.5%
- Thai word count: 1500-3000
- Thai readability: ม.6-ม.12 grade level
```
---
## 🚀 HOW TO USE THE COMPLETE SYSTEM
### **1. Setup (One-Time)**
```bash
# Install all skills
cd /Users/kunthawatgreethong/Gitea/opencode-skill
./scripts/install-skills.sh
# Install Python dependencies
pip install -r skills/seo-multi-channel/scripts/requirements.txt
pip install -r skills/seo-analyzers/scripts/requirements.txt
pip install -r skills/seo-data/scripts/requirements.txt
# Configure credentials (edit .env)
cp skills/seo-multi-channel/scripts/.env.example \
~/.config/opencode/.env
```
### **2. Generate Multi-Channel Content**
```bash
# Example: Generate for all channels
python3 skills/seo-multi-channel/scripts/generate_content.py \
--topic "บริการ podcast hosting" \
--channels facebook facebook_ads google_ads blog x \
--website-repo ./my-website \
--auto-publish
# Example: Facebook Ads only
python3 skills/seo-multi-channel/scripts/generate_content.py \
--topic "podcast microphone" \
--channels facebook_ads \
--product-name "PodMic Pro" \
--website-repo ./my-website
```
### **3. Output Structure**
```
output/บริการ-podcast-hosting/
├── facebook/
│ ├── posts.json
│ └── images/
├── facebook_ads/
│ ├── ads.json
│ └── images/
├── google_ads/
│ └── ads.json
├── blog/
│ ├── article.md
│ └── images/
├── x/
│ └── thread.json
└── summary.json
```
### **4. Auto-Publish Blog**
If `--auto-publish` enabled:
1. Blog saved to: `website/src/content/blog/(th)/{slug}.md`
2. Images saved to: `website/public/images/blog/{slug}/`
3. Git commit + push → triggers Easypanel auto-deploy
4. Returns deployment URL
---
## 📋 NEXT STEPS TO COMPLETE
### **Priority 1 (This Week):**
1. ✅ Complete seo-analyzers Python modules
2. ✅ Complete seo-data connectors
3. ✅ Complete seo-context manager
4. Test with real content generation
### **Priority 2 (Next Week):**
1. Refine Thai language processing
2. Add more channel templates (LinkedIn, Instagram)
3. Integrate with actual image-generation skill
4. Integrate with actual image-edit skill
5. Test website-creator auto-publish flow
### **Priority 3 (Future):**
1. Add actual API integration for Google Ads
2. Add actual API integration for Meta Ads
3. Add performance tracking
4. Add A/B testing support
---
## ✅ WHAT WORKS NOW
- ✅ Multi-channel content structure
- ✅ Thai language processing (with PyThaiNLP)
- ✅ Channel templates (all 5 channels)
- ✅ API-ready output structures
- ✅ Image handling design
- ✅ Website-creator integration design
- ✅ Per-project context system
## ⚠️ WHAT NEEDS COMPLETION
- ⚠️ Full Python implementation of all modules
- ⚠️ Actual LLM integration for content generation
- ⚠️ Image generation/edit skill calls
- ⚠️ Website-creator auto-publish implementation
- ⚠️ Testing with real Thai content
---
## 📞 SUPPORT
For issues or questions:
1. Check SKILL.md documentation
2. Review .env.example for credentials
3. Test with --help flag: `python generate_content.py --help`
---
**Created based on SEOMachine workflow analysis + multi-channel requirements**
**Optimized for Thai market with full Thai language support**

2
skills/alphaear-predictor/scripts/utils/llm/capability.py
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@@ -77,7 +77,7 @@ if __name__ == "__main__":
load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
# 测试当前配置的模型
p = os.getenv("LLM_PROVIDER", "ust")
p = os.getenv("LLM_PROVIDER", "minimax")
m = os.getenv("LLM_MODEL", "Qwen")
print(f"Testing {p}/{m}...")

52
skills/alphaear-predictor/scripts/utils/llm/factory.py
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@@ -5,6 +5,7 @@ from agno.models.dashscope import DashScope
from agno.models.deepseek import DeepSeek
from agno.models.openrouter import OpenRouter
def get_model(model_provider: str, model_id: str, **kwargs):
"""
Factory to get the appropriate LLM model.
@@ -20,17 +21,25 @@ def get_model(model_provider: str, model_id: str, **kwargs):
elif model_provider == "ollama":
return Ollama(id=model_id, **kwargs)
elif model_provider == "minimax":
api_key = os.getenv("MINIMAX_API_KEY")
if not api_key:
print("Warning: MINIMAX_API_KEY not set.")
return OpenAIChat(
id=model_id,
base_url=os.getenv("MINIMAX_API_BASE", "https://api.minimax.io/v1"),
api_key=api_key,
**kwargs,
)
elif model_provider == "deepseek":
# DeepSeek is OpenAI compatible
api_key = os.getenv("DEEPSEEK_API_KEY")
if not api_key:
print("Warning: DEEPSEEK_API_KEY not set.")
return DeepSeek(
id=model_id,
api_key=api_key,
**kwargs
)
return DeepSeek(id=model_id, api_key=api_key, **kwargs)
elif model_provider == "dashscope":
api_key = os.getenv("DASHSCOPE_API_KEY")
if not api_key:
@@ -40,23 +49,19 @@ def get_model(model_provider: str, model_id: str, **kwargs):
id=model_id,
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=api_key,
**kwargs
**kwargs,
)
elif model_provider == 'openrouter':
elif model_provider == "openrouter":
api_key = os.getenv("OPENROUTER_API_KEY")
if not api_key:
print('Warning: OPENROUTER_API_KEY not set.')
print("Warning: OPENROUTER_API_KEY not set.")
return OpenRouter(
id=model_id,
api_key=api_key,
**kwargs
)
return OpenRouter(id=model_id, api_key=api_key, **kwargs)
elif model_provider == 'zai':
elif model_provider == "zai":
api_key = os.getenv("ZAI_KEY_API")
if not api_key:
print('Warning: ZAI_KEY_API not set.')
print("Warning: ZAI_KEY_API not set.")
# role_map to ensure compatibility.
default_role_map = {
@@ -76,14 +81,16 @@ def get_model(model_provider: str, model_id: str, **kwargs):
api_key=api_key,
timeout=60,
role_map=role_map,
extra_body={"enable_thinking": False}, # TODO: one more setting for thinking
**kwargs
extra_body={
"enable_thinking": False
}, # TODO: one more setting for thinking
**kwargs,
)
elif model_provider == 'ust':
elif model_provider == "ust":
api_key = os.getenv("UST_KEY_API")
if not api_key:
print('Warning: UST_KEY_API not set.')
print("Warning: UST_KEY_API not set.")
# Some UST-compatible endpoints expect the standard OpenAI role names
# (e.g. "system", "user", "assistant") rather than Agno's default
@@ -105,10 +112,11 @@ def get_model(model_provider: str, model_id: str, **kwargs):
api_key=api_key,
base_url=os.getenv("UST_URL"),
role_map=role_map,
extra_body={"enable_thinking": False}, # TODO: one more setting for thinking
**kwargs
extra_body={
"enable_thinking": False
}, # TODO: one more setting for thinking
**kwargs,
)
else:
raise ValueError(f"Unknown model provider: {model_provider}")

2
skills/alphaear-predictor/scripts/utils/predictor/training.py
View File

@@ -97,7 +97,7 @@ class AutoSynthesisTrainer:
self.model.load_state_dict(base_model.state_dict(), strict=False)
# LLM for causality verification
provider = os.getenv("LLM_PROVIDER", "ust")
provider = os.getenv("LLM_PROVIDER", "minimax")
model_id = os.getenv("LLM_MODEL", "Qwen")
self.llm_agent = Agent(model=get_model(provider, model_id))

2
skills/alphaear-predictor/scripts/utils/search_tools.py
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@@ -563,7 +563,7 @@ class SearchTools:
4. Return strictly JSON: {{"reuse": true/false, "index": <candidate_index_int>, "reason": "short explanation"}}
"""
# 初始化模型
provider = os.getenv("LLM_PROVIDER", "ust")
provider = os.getenv("LLM_PROVIDER", "minimax")
model_id = os.getenv("LLM_MODEL", "Qwen")
host = os.getenv("LLM_HOST")
if host:

2
skills/alphaear-reporter/scripts/utils/llm/capability.py
View File

@@ -77,7 +77,7 @@ if __name__ == "__main__":
load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
# 测试当前配置的模型
p = os.getenv("LLM_PROVIDER", "ust")
p = os.getenv("LLM_PROVIDER", "minimax")
m = os.getenv("LLM_MODEL", "Qwen")
print(f"Testing {p}/{m}...")

2
skills/alphaear-reporter/scripts/utils/predictor/training.py
View File

@@ -97,7 +97,7 @@ class AutoSynthesisTrainer:
self.model.load_state_dict(base_model.state_dict(), strict=False)
# LLM for causality verification
provider = os.getenv("LLM_PROVIDER", "ust")
provider = os.getenv("LLM_PROVIDER", "minimax")
model_id = os.getenv("LLM_MODEL", "Qwen")
self.llm_agent = Agent(model=get_model(provider, model_id))

2
skills/alphaear-reporter/scripts/utils/search_tools.py
View File

@@ -563,7 +563,7 @@ class SearchTools:
4. Return strictly JSON: {{"reuse": true/false, "index": <candidate_index_int>, "reason": "short explanation"}}
"""
# 初始化模型
provider = os.getenv("LLM_PROVIDER", "ust")
provider = os.getenv("LLM_PROVIDER", "minimax")
model_id = os.getenv("LLM_MODEL", "Qwen")
host = os.getenv("LLM_HOST")
if host:

124
skills/alphaear-reporter/scripts/utils/sentiment_tools.py
View File

@@ -9,6 +9,7 @@ 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
@@ -21,8 +22,13 @@ class SentimentTools:
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。
@@ -39,8 +45,12 @@ class SentimentTools:
# 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")
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",
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",
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}")
@@ -88,7 +118,6 @@ 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。
@@ -155,32 +184,43 @@ class SentimentTools:
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({
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"
})
"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。
@@ -193,38 +233,54 @@ class SentimentTools:
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()

2
skills/alphaear-search/scripts/llm/capability.py
View File

@@ -77,7 +77,7 @@ if __name__ == "__main__":
load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
# 测试当前配置的模型
p = os.getenv("LLM_PROVIDER", "ust")
p = os.getenv("LLM_PROVIDER", "minimax")
m = os.getenv("LLM_MODEL", "Qwen")
print(f"Testing {p}/{m}...")

124
skills/alphaear-search/scripts/sentiment_tools.py
View File

@@ -9,6 +9,7 @@ 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
@@ -21,8 +22,13 @@ class SentimentTools:
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。
@@ -39,8 +45,12 @@ class SentimentTools:
# 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")
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",
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",
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}")
@@ -88,7 +118,6 @@ 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。
@@ -155,32 +184,43 @@ class SentimentTools:
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({
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"
})
"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。
@@ -193,38 +233,54 @@ class SentimentTools:
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()

2
skills/alphaear-sentiment/scripts/llm/capability.py
View File

@@ -77,7 +77,7 @@ if __name__ == "__main__":
load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
# 测试当前配置的模型
p = os.getenv("LLM_PROVIDER", "ust")
p = os.getenv("LLM_PROVIDER", "minimax")
m = os.getenv("LLM_MODEL", "Qwen")
print(f"Testing {p}/{m}...")

2
skills/alphaear-signal-tracker/scripts/utils/llm/capability.py
View File

@@ -77,7 +77,7 @@ if __name__ == "__main__":
load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
# 测试当前配置的模型
p = os.getenv("LLM_PROVIDER", "ust")
p = os.getenv("LLM_PROVIDER", "minimax")
m = os.getenv("LLM_MODEL", "Qwen")
print(f"Testing {p}/{m}...")

2
skills/alphaear-signal-tracker/scripts/utils/predictor/training.py
View File

@@ -97,7 +97,7 @@ class AutoSynthesisTrainer:
self.model.load_state_dict(base_model.state_dict(), strict=False)
# LLM for causality verification
provider = os.getenv("LLM_PROVIDER", "ust")
provider = os.getenv("LLM_PROVIDER", "minimax")
model_id = os.getenv("LLM_MODEL", "Qwen")
self.llm_agent = Agent(model=get_model(provider, model_id))

2
skills/alphaear-signal-tracker/scripts/utils/search_tools.py
View File

@@ -563,7 +563,7 @@ class SearchTools:
4. Return strictly JSON: {{"reuse": true/false, "index": <candidate_index_int>, "reason": "short explanation"}}
"""
# 初始化模型
provider = os.getenv("LLM_PROVIDER", "ust")
provider = os.getenv("LLM_PROVIDER", "minimax")
model_id = os.getenv("LLM_MODEL", "Qwen")
host = os.getenv("LLM_HOST")
if host:

124
skills/alphaear-signal-tracker/scripts/utils/sentiment_tools.py
View File

@@ -9,6 +9,7 @@ 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
@@ -21,8 +22,13 @@ class SentimentTools:
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。
@@ -39,8 +45,12 @@ class SentimentTools:
# 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")
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",
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",
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}")
@@ -88,7 +118,6 @@ 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。
@@ -155,32 +184,43 @@ class SentimentTools:
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({
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"
})
"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。
@@ -193,38 +233,54 @@ class SentimentTools:
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()