Quick Reference for Hyper-Advanced Features
Solves problems step-by-step with explicit reasoning:
from src.cognitive.reasoning import ChainOfThoughtReasoner
from src.cognitive.llm import ModelLoader
# Initialize
llm = ModelLoader()
reasoner = ChainOfThoughtReasoner(llm)
# Solve complex problems
result = reasoner.reason(
"How can we reduce customer churn by 30% in 6 months?",
context={"current_churn": "15%", "budget": "$500k"}
)
print("Reasoning Path:")
for step in result['steps']:
print(f"Step {step['step_number']}: {step['thought']}")
print(f"\nFinal Answer: {result['answer']}")
print(f"Confidence: {result['confidence']}")Output Example:
Step 1: Analyze current churn drivers
Step 2: Identify quick-win interventions
Step 3: Calculate impact of each intervention
Step 4: Develop 6-month implementation plan
Step 5: Forecast expected churn reduction
Final Answer: Implement 3-tier retention strategy:
1. Automated at-risk customer detection
2. Personalized re-engagement campaigns
3. Premium customer success program
Expected reduction: 32% churn decrease
Confidence: 0.85
Explores multiple solution paths for complex problems:
from src.cognitive.reasoning import TreeOfThoughtReasoner
tot = TreeOfThoughtReasoner(llm, max_depth=5, branches_per_node=3)
solution = tot.reason(
"Design a scalable microservices architecture for 10M users",
strategy='best_first' # or 'breadth_first', 'beam'
)
print(tot.visualize_tree()) # ASCII tree visualization
print(f"Optimal Solution: {solution['answer']}")
print(f"Explored {solution['nodes_explored']} solution paths")Analyzes AI's own responses and improves them:
from src.cognitive.reasoning import SelfReflectionEngine
reflector = SelfReflectionEngine(llm)
original_response = "Just increase marketing budget."
reflection = reflector.reflect(
response=original_response,
question="How to grow revenue?"
)
print(f"Issues Found: {reflection.issues_found}")
print(f"Improved Response: {reflection.improved_response}")
print(f"Confidence Before: {reflection.confidence_before}")
print(f"Confidence After: {reflection.confidence_after}")Example Output:
Issues Found:
- Lacks strategic depth
- No data-driven reasoning
- Missing risk assessment
Improved Response:
To grow revenue sustainably:
1. Analyze CAC/LTV ratios to identify high-ROI channels
2. A/B test marketing campaigns before scaling
3. Diversify revenue streams (upsells, new markets)
4. Implement retention programs (higher LTV)
Expected impact: 25-40% revenue growth over 12 months
Confidence Before: 0.45
Confidence After: 0.82
Monitors the AI's own thinking process:
from src.cognitive.reasoning import MetacognitiveMonitor
monitor = MetacognitiveMonitor()
# Monitor task execution
monitoring = monitor.monitor_task(
task="Optimize database query performance",
strategy="chain_of_thought"
)
print(f"Cognitive State: {monitoring['initial_state']}")
print(f"Task Complexity: {monitoring['complexity']}")
print(f"Recommended Strategy: {monitoring['recommended_strategy']}")
if monitoring['should_switch']:
print("⚠️ Consider switching strategy for better results")from src.perception.voice.multilang_support import MultiLanguageManager
mlm = MultiLanguageManager()
# Set language
mlm.set_language("hi") # Hindi
# Get current language info
current = mlm.get_current_language()
print(f"Language: {current.name} ({current.native_name})")
print(f"TTS Voice (Male): {current.tts_voice_male}")
print(f"TTS Voice (Female): {current.tts_voice_female}")text = "Bonjour, comment allez-vous?"
detected = mlm.detect_language(text)
print(f"Detected Language: {detected}") # Output: "fr"
# Auto-switch language
mlm.auto_switch_language(text)# Get greeting in current language
greeting = mlm.translate_system_messages("greeting")
print(greeting)
# Hindi: "नमस्ते! मैं आज आपकी कैसे मदद कर सकता हूं?"
# Spanish: "¡Hola! ¿Cómo puedo ayudarte hoy?"
# Arabic: "مرحبا! كيف يمكنني مساعدتك اليوم؟"European: English, Spanish, French, German, Italian, Portuguese, Russian, Dutch, Polish, Ukrainian, Turkish
Asian: Chinese, Japanese, Korean, Hindi, Bengali, Punjabi, Telugu, Marathi, Tamil, Urdu, Gujarati, Kannada, Vietnamese, Thai, Indonesian, Malay, Filipino
Other: Arabic, Swahili
from src.action.analytics import SWOTAnalyzer
analyzer = SWOTAnalyzer(llm)
analysis = analyzer.analyze(
subject="Tesla Inc",
context={
"industry": "Electric Vehicles",
"year": "2026",
"market_data": {"competitors": ["BYD", "Rivian"], "growth_rate": "15%"}
},
include_recommendations=True
)
# View results
print("\n=== STRENGTHS ===")
for s in analysis['strengths']:
print(f"• {s.item}: {s.description} (Impact: {s.impact})")
print("\n=== STRATEGIC INSIGHTS ===")
for insight in analysis['strategic_insights']:
print(f"• {insight}")
print("\n=== RECOMMENDATIONS ===")
for rec in analysis['recommendations']:
print(f"• [{rec['strategy_type']}] {rec['recommendation']}")
# Export report
markdown_report = analyzer.export_report(analysis, format='markdown')
html_report = analyzer.export_report(analysis, format='html')from src.action.analytics import DataAnalyzer
analyzer = DataAnalyzer(llm)
# Analyze CSV/Excel file
analysis = analyzer.analyze_file(
filepath="sales_data.csv",
analysis_type='comprehensive' # or 'statistical', 'ml', 'quick'
)
print(f"Dataset: {analysis['rows']} rows x {analysis['columns']} columns")
print(f"\nInsights Found: {len(analysis['insights'])}")
# Statistical summary
for col, stats in analysis['basic_statistics'].items():
print(f"\n{col}:")
print(f" Mean: {stats['mean']:.2f}")
print(f" Median: {stats['median']:.2f}")
print(f" Std Dev: {stats['std']:.2f}")
# Correlations
print("\n=== STRONG CORRELATIONS ===")
for corr in analysis['correlations']['strong_correlations']:
print(f"• {corr}")
# Trends
print("\n=== TRENDS DETECTED ===")
for trend in analysis['trends']:
print(f"• {trend['column']}: {trend['direction']} (confidence: {trend['confidence']})")
# ML Insights
if 'clustering' in analysis['ml_analysis']:
print(f"\n=== CLUSTERING (K-Means) ===")
print(f"Optimal clusters: {analysis['ml_analysis']['clustering']['n_clusters']}")
for cluster in analysis['ml_analysis']['clustering']['cluster_summary']:
print(f" Cluster {cluster['cluster_id']}: {cluster['size']} items")from src.action.analytics import FinancialAnalyzer
fa = FinancialAnalyzer(llm)
# Analyze stock
stock_analysis = fa.analyze_stock(
symbol="AAPL",
context={"sector": "Technology", "market_cap": "3T"}
)
print(f"Stock: {stock_analysis['symbol']}")
print(f"Recommendation: {stock_analysis['recommendation']['action']}")
print(f"Target Price: ${stock_analysis['recommendation']['target_price']}")
print(f"Risk Score: {stock_analysis['risk_score']}/10")
print("\n=== TECHNICAL ANALYSIS ===")
tech = stock_analysis['technical_analysis']
print(f"Trend: {tech['trend']}")
print(f"RSI: {tech['rsi']} ({tech['rsi_signal']})")
print(f"Support: ${tech['support_level']}")
print(f"Resistance: ${tech['resistance_level']}")
# Portfolio analysis
portfolio_analysis = fa.analyze_portfolio({
"AAPL": 100,
"TSLA": 50,
"MSFT": 75,
"GOOGL": 40
})
print(f"\n=== PORTFOLIO ANALYSIS ===")
print(f"Total Value: ${portfolio_analysis['total_value']:,.2f}")
print(f"Total Gain/Loss: ${portfolio_analysis['total_gain_loss']:,.2f}")
print(f"Diversification Score: {portfolio_analysis['diversification_score']}/10")
print("\n=== REBALANCING SUGGESTIONS ===")
for suggestion in portfolio_analysis['rebalancing_suggestions']:
print(f"• {suggestion}")
# Predict trend
prediction = fa.predict_trend("NVDA", days_ahead=30)
print(f"\n=== 30-DAY PREDICTION ===")
print(f"Predicted Trend: {prediction['predicted_trend']}")
print(f"Confidence: {prediction['confidence']}")
print(f"Expected Return: {prediction['expected_return']}%")from src.action.analytics import MarketResearchEngine
mre = MarketResearchEngine(llm)
# Market analysis
market = mre.analyze_market(
industry="Artificial Intelligence",
region="Global"
)
print(f"Industry: {market['industry']}")
print(f"Market Size: ${market['market_size']:,.0f}")
print(f"Growth Rate: {market['growth_rate']}%")
print("\n=== KEY TRENDS ===")
for trend in market['key_trends']:
print(f"• {trend}")
print("\n=== TOP COMPETITORS ===")
for comp in market['competitors']:
print(f"• {comp.name} - Market Share: {comp.market_share}%")
print("\n=== OPPORTUNITIES ===")
for opp in market['opportunities']:
print(f"• {opp}")
# Competitor analysis
competitor = mre.analyze_competitor(
competitor="OpenAI",
your_company="Aether AI"
)
print(f"\n=== COMPETITIVE ANALYSIS ===")
print(f"Threat Level: {competitor['threat_level']}")
print(f"Competitive Advantages:")
for advantage in competitor['your_advantages']:
print(f" ✓ {advantage}")
# Identify opportunities
opportunities = mre.identify_opportunities(
industry="AI Assistants",
your_strengths={"advanced_reasoning": True, "multi_language": True}
)
print("\n=== TOP OPPORTUNITIES ===")
for i, opp in enumerate(opportunities[:3], 1):
print(f"{i}. {opp['opportunity']} (Score: {opp['score']}/10)")
print(f" Success Probability: {opp['success_probability']}%")# Use Tree-of-Thought for complex strategy
tot = TreeOfThoughtReasoner(llm, max_depth=6, branches_per_node=4)
decision = tot.reason(
"Should we enter the European market in 2026?",
context={
"current_market": "North America",
"revenue": "$50M",
"team_size": 100
},
strategy='best_first'
)
# Get market intelligence
market = mre.analyze_market("AI Assistants", region="Europe")
# Perform SWOT for expansion
swot = analyzer.analyze("European Market Entry", context=market)
# Make data-driven decision
print(f"Decision: {decision['answer']}")
print(f"Market Opportunity: ${market['market_size']:,.0f}")
print(f"Key Risks: {swot['risk_assessment']}")# Analyze multiple stocks
stocks = ["AAPL", "MSFT", "GOOGL", "NVDA", "TSLA"]
for symbol in stocks:
analysis = fa.analyze_stock(symbol)
print(f"{symbol}: {analysis['recommendation']['action']} "
f"(Risk: {analysis['risk_score']}/10)")
# Optimize portfolio
current_portfolio = {"AAPL": 100, "TSLA": 50}
portfolio = fa.analyze_portfolio(current_portfolio)
print(f"Diversification: {portfolio['diversification_score']}/10")
for suggestion in portfolio['rebalancing_suggestions']:
print(f"💡 {suggestion}")# Analyze sales data
sales_analysis = analyzer.analyze_file("sales_data.csv", 'comprehensive')
# Identify trends
print("=== KEY INSIGHTS ===")
for insight in sales_analysis['insights']:
print(f"• [{insight.type}] {insight.title}")
print(f" {insight.description}")
# Get recommendations
for rec in sales_analysis['recommendations']:
print(f"📌 {rec}")- Document Intelligence: PDF/DOCX/PPT ingestion with RAG
- Code Generation: Multi-language code assistant
- Enterprise Integrations: Jira, Slack, Teams, GitHub
- Screen Understanding: OCR and visual AI
- Web Research: Automated research and synthesis
- Self-Improvement: Continuous learning from feedback
Full Documentation: See UPGRADE_v0.2.0_SUMMARY.md for complete technical details.