# 🚀 Advanced AI Features Guide

Unlock the full potential of Zenoo RPC's AI capabilities with advanced features and techniques!

## 🎯 Overview

This guide covers advanced AI features including:
- **Custom AI workflows** and automation
- **Multi-step AI reasoning** for complex problems
- **AI-powered data analysis** and insights
- **Integration patterns** for enterprise applications
- **Performance tuning** and optimization strategies

## 🔧 Advanced AI Workflows

### Multi-Step AI Reasoning

```python
import asyncio
from zenoo_rpc import ZenooClient

class AIWorkflowEngine:
    """Advanced AI workflow engine for complex operations."""
    
    def __init__(self, client):
        self.client = client
        self.workflow_history = []
    
    async def execute_complex_analysis(self, business_question):
        """Execute multi-step AI analysis for complex business questions."""
        
        workflow_steps = []
        
        # Step 1: Break down the question
        breakdown = await self.client.ai.chat(
            f"Break down this business question into specific data queries: {business_question}",
            context="Complex business analysis workflow"
        )
        workflow_steps.append({"step": "breakdown", "result": breakdown})
        
        # Step 2: Identify required models and data
        data_requirements = await self.client.ai.chat(
            f"Based on this breakdown: {breakdown}\n\nWhat Odoo models and fields do I need?",
            context="Data requirement analysis"
        )
        workflow_steps.append({"step": "data_requirements", "result": data_requirements})
        
        # Step 3: Generate queries
        queries = await self.client.ai.chat(
            f"Generate specific Odoo queries for: {data_requirements}",
            context="Query generation for business analysis"
        )
        workflow_steps.append({"step": "query_generation", "result": queries})
        
        # Step 4: Execute and analyze
        analysis = await self.client.ai.chat(
            f"Analyze the workflow results and provide business insights:\n{workflow_steps}",
            context="Final business analysis and insights"
        )
        workflow_steps.append({"step": "final_analysis", "result": analysis})
        
        self.workflow_history.append({
            "question": business_question,
            "steps": workflow_steps,
            "timestamp": time.time()
        })
        
        return {
            "question": business_question,
            "analysis": analysis,
            "workflow": workflow_steps
        }

# Usage
async def complex_business_analysis():
    async with ZenooClient("http://localhost:8069") as client:
        await client.login("demo", "admin", "admin")
        await client.setup_ai(provider="gemini", api_key="your-key")
        
        engine = AIWorkflowEngine(client)
        
        result = await engine.execute_complex_analysis(
            "What are the key factors affecting our customer retention rate and how can we improve it?"
        )
        
        print("🧠 Complex Analysis Result:")
        print(f"Question: {result['question']}")
        print(f"Analysis: {result['analysis']}")

asyncio.run(complex_business_analysis())
```

### AI-Powered Data Pipeline

```python
class AIDataPipeline:
    """AI-powered data processing and transformation pipeline."""
    
    def __init__(self, client):
        self.client = client
        self.pipeline_stages = []
    
    async def create_intelligent_pipeline(self, source_data, target_format, business_rules):
        """Create an AI-guided data transformation pipeline."""
        
        # Stage 1: Data Analysis
        data_analysis = await self.client.ai.chat(
            f"Analyze this data structure and identify transformation needs:\n"
            f"Source: {source_data}\n"
            f"Target: {target_format}\n"
            f"Rules: {business_rules}",
            context="Data pipeline analysis"
        )
        
        # Stage 2: Transformation Strategy
        strategy = await self.client.ai.chat(
            f"Based on this analysis: {data_analysis}\n"
            f"Create a step-by-step transformation strategy",
            context="Data transformation strategy"
        )
        
        # Stage 3: Validation Rules
        validation = await self.client.ai.chat(
            f"Generate validation rules for this transformation: {strategy}",
            context="Data validation rules generation"
        )
        
        # Stage 4: Error Handling
        error_handling = await self.client.ai.chat(
            f"Design error handling for this pipeline: {strategy}",
            context="Pipeline error handling design"
        )
        
        pipeline = {
            "analysis": data_analysis,
            "strategy": strategy,
            "validation": validation,
            "error_handling": error_handling
        }
        
        self.pipeline_stages.append(pipeline)
        return pipeline
    
    async def execute_pipeline(self, data, pipeline_config):
        """Execute the AI-designed pipeline."""
        
        try:
            # AI-guided execution with real-time adaptation
            execution_result = await self.client.ai.chat(
                f"Execute this data transformation:\n"
                f"Data: {data}\n"
                f"Pipeline: {pipeline_config}",
                context="Pipeline execution guidance"
            )
            
            return {
                "status": "success",
                "result": execution_result,
                "pipeline": pipeline_config
            }
            
        except Exception as e:
            # AI-powered error recovery
            recovery_plan = await self.client.ai.diagnose(e, {
                "operation": "pipeline_execution",
                "pipeline_config": pipeline_config,
                "data_sample": str(data)[:200]
            })
            
            return {
                "status": "error",
                "error": str(e),
                "recovery_plan": recovery_plan,
                "pipeline": pipeline_config
            }
```

## 🧠 Intelligent Automation

### Smart Business Process Automation

```python
class SmartProcessAutomation:
    """AI-driven business process automation."""
    
    def __init__(self, client):
        self.client = client
        self.process_patterns = {}
    
    async def learn_process_pattern(self, process_name, user_actions):
        """Learn from user actions to automate processes."""
        
        # Analyze user behavior patterns
        pattern_analysis = await self.client.ai.chat(
            f"Analyze this sequence of user actions and identify automation opportunities:\n"
            f"Process: {process_name}\n"
            f"Actions: {user_actions}",
            context="Process automation analysis"
        )
        
        # Generate automation rules
        automation_rules = await self.client.ai.chat(
            f"Based on this pattern analysis: {pattern_analysis}\n"
            f"Generate specific automation rules and triggers",
            context="Automation rule generation"
        )
        
        # Create decision tree
        decision_tree = await self.client.ai.chat(
            f"Create a decision tree for this automation: {automation_rules}",
            context="Automation decision tree"
        )
        
        self.process_patterns[process_name] = {
            "analysis": pattern_analysis,
            "rules": automation_rules,
            "decision_tree": decision_tree,
            "confidence": 0.8  # Initial confidence
        }
        
        return self.process_patterns[process_name]
    
    async def suggest_automation(self, current_context):
        """Suggest automation based on current context."""
        
        suggestions = await self.client.ai.chat(
            f"Based on these learned patterns: {self.process_patterns}\n"
            f"And current context: {current_context}\n"
            f"Suggest relevant automations",
            context="Automation suggestion engine"
        )
        
        return suggestions
    
    async def execute_smart_automation(self, process_name, trigger_data):
        """Execute AI-guided automation."""
        
        if process_name not in self.process_patterns:
            return {"error": "Process pattern not learned"}
        
        pattern = self.process_patterns[process_name]
        
        # AI-guided execution decision
        execution_plan = await self.client.ai.chat(
            f"Should I execute this automation?\n"
            f"Pattern: {pattern}\n"
            f"Trigger: {trigger_data}\n"
            f"Provide execution plan or skip reason",
            context="Automation execution decision"
        )
        
        return {
            "process": process_name,
            "execution_plan": execution_plan,
            "pattern_confidence": pattern["confidence"]
        }
```

### Predictive Analytics Engine

```python
class PredictiveAnalytics:
    """AI-powered predictive analytics for business insights."""
    
    def __init__(self, client):
        self.client = client
        self.prediction_models = {}
    
    async def create_prediction_model(self, model_name, historical_data, target_metric):
        """Create AI-powered prediction model."""
        
        # Analyze historical patterns
        pattern_analysis = await self.client.ai.chat(
            f"Analyze these historical patterns for prediction modeling:\n"
            f"Data: {historical_data}\n"
            f"Target: {target_metric}",
            context="Predictive pattern analysis"
        )
        
        # Identify key factors
        key_factors = await self.client.ai.chat(
            f"Based on this analysis: {pattern_analysis}\n"
            f"Identify the key factors that influence {target_metric}",
            context="Predictive factor identification"
        )
        
        # Generate prediction logic
        prediction_logic = await self.client.ai.chat(
            f"Create prediction logic using these factors: {key_factors}\n"
            f"For target metric: {target_metric}",
            context="Prediction logic generation"
        )
        
        self.prediction_models[model_name] = {
            "target_metric": target_metric,
            "pattern_analysis": pattern_analysis,
            "key_factors": key_factors,
            "prediction_logic": prediction_logic,
            "accuracy": 0.0  # To be updated with validation
        }
        
        return self.prediction_models[model_name]
    
    async def make_prediction(self, model_name, current_data):
        """Make prediction using AI model."""
        
        if model_name not in self.prediction_models:
            return {"error": "Prediction model not found"}
        
        model = self.prediction_models[model_name]
        
        prediction = await self.client.ai.chat(
            f"Make a prediction using this model:\n"
            f"Model: {model}\n"
            f"Current Data: {current_data}\n"
            f"Provide prediction with confidence level and reasoning",
            context="AI prediction generation"
        )
        
        return {
            "model": model_name,
            "prediction": prediction,
            "target_metric": model["target_metric"]
        }
    
    async def validate_prediction(self, model_name, actual_result, predicted_result):
        """Validate and improve prediction accuracy."""
        
        validation = await self.client.ai.chat(
            f"Analyze prediction accuracy:\n"
            f"Predicted: {predicted_result}\n"
            f"Actual: {actual_result}\n"
            f"Suggest model improvements",
            context="Prediction validation and improvement"
        )
        
        return validation
```

## 🔍 Advanced Data Analysis

### Intelligent Data Discovery

```python
class IntelligentDataDiscovery:
    """AI-powered data discovery and insights engine."""
    
    def __init__(self, client):
        self.client = client
        self.discovery_cache = {}
    
    async def discover_data_insights(self, dataset_description, analysis_goals):
        """Discover insights from data using AI analysis."""
        
        # Generate exploration strategy
        exploration_strategy = await self.client.ai.chat(
            f"Create a data exploration strategy for:\n"
            f"Dataset: {dataset_description}\n"
            f"Goals: {analysis_goals}",
            context="Data exploration strategy"
        )
        
        # Identify key metrics
        key_metrics = await self.client.ai.chat(
            f"Based on this strategy: {exploration_strategy}\n"
            f"Identify the most important metrics to analyze",
            context="Key metrics identification"
        )
        
        # Generate analysis queries
        analysis_queries = await self.client.ai.chat(
            f"Generate specific Odoo queries to analyze these metrics: {key_metrics}",
            context="Analysis query generation"
        )
        
        # Suggest visualizations
        visualizations = await self.client.ai.chat(
            f"Suggest appropriate visualizations for these metrics: {key_metrics}",
            context="Data visualization suggestions"
        )
        
        discovery_result = {
            "strategy": exploration_strategy,
            "key_metrics": key_metrics,
            "queries": analysis_queries,
            "visualizations": visualizations
        }
        
        self.discovery_cache[dataset_description] = discovery_result
        return discovery_result
    
    async def generate_insights_report(self, data_results, analysis_context):
        """Generate comprehensive insights report."""
        
        insights_report = await self.client.ai.chat(
            f"Generate a comprehensive insights report:\n"
            f"Data Results: {data_results}\n"
            f"Context: {analysis_context}\n"
            f"Include trends, anomalies, recommendations, and action items",
            context="Comprehensive insights report generation"
        )
        
        return insights_report
```

### Anomaly Detection System

```python
class AIAnomalyDetection:
    """AI-powered anomaly detection for business data."""
    
    def __init__(self, client):
        self.client = client
        self.baseline_patterns = {}
    
    async def establish_baseline(self, data_type, historical_data):
        """Establish baseline patterns for anomaly detection."""
        
        baseline_analysis = await self.client.ai.chat(
            f"Analyze this historical data to establish normal patterns:\n"
            f"Data Type: {data_type}\n"
            f"Historical Data: {historical_data}",
            context="Baseline pattern establishment"
        )
        
        self.baseline_patterns[data_type] = {
            "analysis": baseline_analysis,
            "established_date": time.time()
        }
        
        return baseline_analysis
    
    async def detect_anomalies(self, data_type, current_data):
        """Detect anomalies in current data."""
        
        if data_type not in self.baseline_patterns:
            return {"error": "Baseline not established for this data type"}
        
        baseline = self.baseline_patterns[data_type]
        
        anomaly_analysis = await self.client.ai.chat(
            f"Detect anomalies by comparing current data to baseline:\n"
            f"Baseline: {baseline['analysis']}\n"
            f"Current Data: {current_data}\n"
            f"Identify anomalies, severity, and potential causes",
            context="Anomaly detection analysis"
        )
        
        return {
            "data_type": data_type,
            "anomaly_analysis": anomaly_analysis,
            "baseline_age": time.time() - baseline["established_date"]
        }
    
    async def investigate_anomaly(self, anomaly_data, business_context):
        """Investigate detected anomalies with business context."""
        
        investigation = await self.client.ai.chat(
            f"Investigate this anomaly with business context:\n"
            f"Anomaly: {anomaly_data}\n"
            f"Business Context: {business_context}\n"
            f"Provide root cause analysis and recommended actions",
            context="Anomaly investigation and resolution"
        )
        
        return investigation
```

## 🎯 Enterprise Integration Patterns

### AI-Powered API Gateway

```python
class AIAPIGateway:
    """Intelligent API gateway with AI-powered routing and optimization."""
    
    def __init__(self, client):
        self.client = client
        self.routing_intelligence = {}
        self.performance_patterns = {}
    
    async def intelligent_routing(self, request_data, available_endpoints):
        """Use AI to determine optimal routing."""
        
        routing_decision = await self.client.ai.chat(
            f"Determine optimal routing for this request:\n"
            f"Request: {request_data}\n"
            f"Available Endpoints: {available_endpoints}\n"
            f"Consider performance, load, and business rules",
            context="Intelligent API routing"
        )
        
        return routing_decision
    
    async def adaptive_rate_limiting(self, client_profile, current_load):
        """AI-powered adaptive rate limiting."""
        
        rate_limit_strategy = await self.client.ai.chat(
            f"Determine appropriate rate limiting:\n"
            f"Client Profile: {client_profile}\n"
            f"Current Load: {current_load}\n"
            f"Balance performance and fairness",
            context="Adaptive rate limiting strategy"
        )
        
        return rate_limit_strategy
    
    async def predict_capacity_needs(self, usage_patterns, business_forecast):
        """Predict future capacity requirements."""
        
        capacity_prediction = await self.client.ai.chat(
            f"Predict capacity needs based on:\n"
            f"Usage Patterns: {usage_patterns}\n"
            f"Business Forecast: {business_forecast}\n"
            f"Provide scaling recommendations",
            context="Capacity planning prediction"
        )
        
        return capacity_prediction
```

### Smart Caching Strategy

```python
class SmartCachingEngine:
    """AI-powered intelligent caching system."""
    
    def __init__(self, client):
        self.client = client
        self.cache_patterns = {}
        self.access_patterns = {}
    
    async def optimize_cache_strategy(self, access_patterns, data_characteristics):
        """Optimize caching strategy using AI analysis."""
        
        cache_strategy = await self.client.ai.chat(
            f"Optimize caching strategy for:\n"
            f"Access Patterns: {access_patterns}\n"
            f"Data Characteristics: {data_characteristics}\n"
            f"Recommend cache levels, TTL, and invalidation strategies",
            context="Cache strategy optimization"
        )
        
        return cache_strategy
    
    async def predict_cache_misses(self, current_cache_state, upcoming_requests):
        """Predict and prevent cache misses."""
        
        miss_prediction = await self.client.ai.chat(
            f"Predict cache misses and suggest preloading:\n"
            f"Cache State: {current_cache_state}\n"
            f"Upcoming Requests: {upcoming_requests}",
            context="Cache miss prediction and prevention"
        )
        
        return miss_prediction
    
    async def adaptive_cache_warming(self, business_schedule, historical_patterns):
        """AI-guided cache warming based on business patterns."""
        
        warming_strategy = await self.client.ai.chat(
            f"Create cache warming strategy:\n"
            f"Business Schedule: {business_schedule}\n"
            f"Historical Patterns: {historical_patterns}",
            context="Adaptive cache warming strategy"
        )
        
        return warming_strategy
```

## 🛠️ AI Performance Tuning

### Dynamic AI Configuration

```python
class DynamicAIConfiguration:
    """Dynamically optimize AI configuration based on usage patterns."""
    
    def __init__(self, client):
        self.client = client
        self.performance_history = []
        self.configuration_experiments = {}
    
    async def optimize_ai_parameters(self, usage_context, performance_requirements):
        """Optimize AI parameters for specific use cases."""
        
        optimization_strategy = await self.client.ai.chat(
            f"Optimize AI configuration for:\n"
            f"Usage Context: {usage_context}\n"
            f"Performance Requirements: {performance_requirements}\n"
            f"Suggest temperature, max_tokens, and other parameters",
            context="AI parameter optimization"
        )
        
        return optimization_strategy
    
    async def a_b_test_configurations(self, config_a, config_b, test_scenarios):
        """A/B test different AI configurations."""
        
        test_plan = await self.client.ai.chat(
            f"Design A/B test for AI configurations:\n"
            f"Config A: {config_a}\n"
            f"Config B: {config_b}\n"
            f"Test Scenarios: {test_scenarios}",
            context="AI configuration A/B testing"
        )
        
        return test_plan
    
    async def adaptive_configuration(self, real_time_metrics):
        """Adapt AI configuration based on real-time performance."""
        
        adaptation_strategy = await self.client.ai.chat(
            f"Adapt AI configuration based on metrics:\n"
            f"Real-time Metrics: {real_time_metrics}\n"
            f"Suggest configuration adjustments",
            context="Real-time AI configuration adaptation"
        )
        
        return adaptation_strategy
```

## 🎯 Best Practices for Advanced AI

### 1. AI Workflow Orchestration

```python
class AIWorkflowOrchestrator:
    """Orchestrate complex AI workflows with error handling and recovery."""
    
    def __init__(self, client):
        self.client = client
        self.workflow_state = {}
        self.error_recovery_strategies = {}
    
    async def execute_resilient_workflow(self, workflow_definition):
        """Execute AI workflow with built-in resilience."""
        
        try:
            # Execute workflow steps
            for step in workflow_definition["steps"]:
                result = await self._execute_step(step)
                self.workflow_state[step["id"]] = result
                
                # Validate step result
                if not await self._validate_step_result(step, result):
                    await self._handle_step_failure(step, result)
            
            return {"status": "success", "state": self.workflow_state}
            
        except Exception as e:
            recovery_plan = await self._create_recovery_plan(e, self.workflow_state)
            return {"status": "error", "error": str(e), "recovery_plan": recovery_plan}
    
    async def _execute_step(self, step):
        """Execute individual workflow step."""
        
        if step["type"] == "ai_analysis":
            return await self.client.ai.chat(step["prompt"], context=step.get("context"))
        elif step["type"] == "data_query":
            return await self.client.ai.query(step["query"])
        elif step["type"] == "error_diagnosis":
            return await self.client.ai.diagnose(step["error"], step.get("context"))
        else:
            raise ValueError(f"Unknown step type: {step['type']}")
    
    async def _validate_step_result(self, step, result):
        """Validate step result using AI."""
        
        validation = await self.client.ai.chat(
            f"Validate this workflow step result:\n"
            f"Step: {step}\n"
            f"Result: {result}\n"
            f"Is this result valid and complete?",
            context="Workflow step validation"
        )
        
        return "valid" in validation.lower() or "complete" in validation.lower()
    
    async def _create_recovery_plan(self, error, current_state):
        """Create AI-powered recovery plan."""
        
        recovery_plan = await self.client.ai.chat(
            f"Create recovery plan for workflow failure:\n"
            f"Error: {error}\n"
            f"Current State: {current_state}\n"
            f"Suggest recovery steps",
            context="Workflow recovery planning"
        )
        
        return recovery_plan
```

### 2. AI Quality Assurance

```python
class AIQualityAssurance:
    """Quality assurance system for AI outputs."""
    
    def __init__(self, client):
        self.client = client
        self.quality_metrics = {}
        self.validation_rules = {}
    
    async def validate_ai_output(self, output, expected_criteria):
        """Validate AI output against quality criteria."""
        
        validation_result = await self.client.ai.chat(
            f"Validate this AI output against criteria:\n"
            f"Output: {output}\n"
            f"Criteria: {expected_criteria}\n"
            f"Provide quality score and improvement suggestions",
            context="AI output quality validation"
        )
        
        return validation_result
    
    async def continuous_quality_monitoring(self, ai_outputs, user_feedback):
        """Monitor AI quality continuously."""
        
        quality_analysis = await self.client.ai.chat(
            f"Analyze AI quality trends:\n"
            f"Recent Outputs: {ai_outputs}\n"
            f"User Feedback: {user_feedback}\n"
            f"Identify quality trends and improvement areas",
            context="Continuous AI quality monitoring"
        )
        
        return quality_analysis
```

## 🎯 Next Steps

- **[AI Configuration Guide](./ai-configuration.md)** - Fine-tune advanced configurations
- **[Performance Optimization](./performance-optimization.md)** - Optimize advanced workflows
- **[Error Diagnosis](./error-diagnosis.md)** - Handle complex error scenarios
- **Production Deployment** - Deploy advanced AI features to production

---

**💡 Pro Tip**: Advanced AI features require careful planning and monitoring. Start with simple implementations and gradually add complexity as you gain experience with the AI behavior patterns!
