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Model Selection

Overview​

The Model Selection system is responsible for determining which AI model is most appropriate for a given task within the Engineering AI Agent. This component optimizes for performance, cost-efficiency, and quality by dynamically routing requests to the most suitable models based on various criteria.

Core Components​

Decision Service​

The Decision Service is the brain of the Model Selection system, evaluating multiple factors to determine the optimal model for a specific request:

class DecisionService:
    def select_model(self, task_data, constraints=None):
        """
        Select the optimal model for a given task based on multiple criteria.
        
        Args:
            task_data: Information about the task including type, complexity, context length.
            constraints: Optional constraints such as max_cost, max_latency, required_capabilities.
            
        Returns:
            selected_model: The model identifier that best matches the requirements.
        """
        # 1. Analyze task requirements
        task_requirements = self._analyze_task(task_data)
        
        # 2. Get candidate models from registry
        candidate_models = self.model_registry.get_candidates(task_requirements)
        
        # 3. Apply filters based on constraints
        if constraints:
            candidate_models = self._apply_constraints(candidate_models, constraints)
        
        # 4. Score models based on multiple criteria
        model_scores = {}
        for model in candidate_models:
            performance_score = self.performance_monitor.get_score(model, task_requirements)
            cost_score = self.cost_manager.get_score(model, task_data)
            feature_score = self._calculate_feature_match(model, task_requirements)
            
            # Combined score with customizable weights
            model_scores[model] = (
                performance_score * self.weights["performance"] +
                cost_score * self.weights["cost"] +
                feature_score * self.weights["features"]
            )
        
        # 5. Apply heuristics for special cases
        model_scores = self.heuristics_manager.apply_heuristics(model_scores, task_data)
        
        # 6. Select the highest scoring model
        selected_model = max(model_scores.items(), key=lambda x: x[1])[0]
        
        # 7. Log the decision for future analysis
        self._log_decision(task_data, selected_model, model_scores)
        
        return selected_model

Model Registry​

The Model Registry maintains information about all available models, their capabilities, and current status:

{
  "models": [
    {
      "id": "gpt-4",
      "provider": "openai",
      "size_category": "large",
      "context_window": 8192,
      "capabilities": ["code_generation", "reasoning", "instruction_following", "creativity"],
      "specializations": ["software_development", "technical_writing"],
      "performance_tier": "premium",
      "cost_per_1k_tokens": {
        "input": 0.03,
        "output": 0.06
      },
      "average_latency_ms": 2500,
      "status": "active"
    },
    {
      "id": "gpt-3.5-turbo",
      "provider": "openai",
      "size_category": "medium",
      "context_window": 4096,
      "capabilities": ["code_generation", "reasoning", "instruction_following"],
      "specializations": ["general_purpose"],
      "performance_tier": "standard",
      "cost_per_1k_tokens": {
        "input": 0.0015,
        "output": 0.002
      },
      "average_latency_ms": 800,
      "status": "active"
    },
    {
      "id": "claude-2",
      "provider": "anthropic",
      "size_category": "large",
      "context_window": 100000,
      "capabilities": ["code_generation", "reasoning", "instruction_following", "long_context"],
      "specializations": ["documentation", "technical_analysis"],
      "performance_tier": "premium",
      "cost_per_1k_tokens": {
        "input": 0.011,
        "output": 0.032
      },
      "average_latency_ms": 3000,
      "status": "active"
    },
    {
      "id": "llama-2-70b",
      "provider": "meta",
      "deployment": "self_hosted",
      "size_category": "large",
      "context_window": 4096,
      "capabilities": ["code_generation", "reasoning", "instruction_following"],
      "specializations": ["general_purpose"],
      "performance_tier": "standard",
      "cost_per_1k_tokens": {
        "input": 0.0005,
        "output": 0.0005
      },
      "average_latency_ms": 4500,
      "status": "active"
    },
    {
      "id": "code-llama-34b",
      "provider": "meta",
      "deployment": "self_hosted",
      "size_category": "medium",
      "context_window": 16384,
      "capabilities": ["code_generation", "code_completion", "debugging"],
      "specializations": ["software_development"],
      "performance_tier": "specialized",
      "cost_per_1k_tokens": {
        "input": 0.0004,
        "output": 0.0004
      },
      "average_latency_ms": 3500,
      "status": "active"
    },
    {
      "id": "software-developer-specialized",
      "base_model": "gpt-4",
      "provider": "openai",
      "deployment": "fine_tuned",
      "size_category": "large",
      "context_window": 8192,
      "capabilities": ["code_generation", "debugging", "refactoring", "test_generation"],
      "specializations": ["software_development"],
      "performance_tier": "specialized",
      "cost_per_1k_tokens": {
        "input": 0.03,
        "output": 0.06
      },
      "average_latency_ms": 2600,
      "status": "active"
    }
  ]
}

Performance Monitor​

The Performance Monitor tracks and analyzes model performance across various dimensions:

class PerformanceMonitor:
    def __init__(self, metrics_service):
        self.metrics_service = metrics_service
        self.performance_cache = {}
        self.update_interval = 3600  # Update performance metrics hourly
        
    def get_score(self, model_id, task_requirements):
        """Calculate a performance score for a model on a given task type."""
        # Get recent performance metrics
        metrics = self._get_recent_metrics(model_id, task_requirements["type"])
        
        # Calculate success rate
        success_rate = metrics.get("success_rate", 0.5)  # Default if no data
        
        # Calculate average latency normalized to expectations
        latency_score = self._calculate_latency_score(
            metrics.get("avg_latency"), 
            task_requirements.get("latency_sensitivity", "medium")
        )
        
        # Calculate quality score based on human feedback
        quality_score = metrics.get("quality_rating", 0.5)  # Default if no data
        
        # Calculate error rate penalty
        error_penalty = 1.0 - metrics.get("error_rate", 0)
        
        # Combined weighted score
        weights = self._get_weights_for_task(task_requirements["type"])
        final_score = (
            success_rate * weights["success_rate"] +
            latency_score * weights["latency"] +
            quality_score * weights["quality"] +
            error_penalty * weights["reliability"]
        )
        
        return final_score
        
    def _get_recent_metrics(self, model_id, task_type):
        """Retrieve recent performance metrics for the model on similar tasks."""
        cache_key = f"{model_id}:{task_type}"
        
        # Check if we have fresh cached data
        if cache_key in self.performance_cache:
            cache_entry = self.performance_cache[cache_key]
            if time.time() - cache_entry["timestamp"] < self.update_interval:
                return cache_entry["metrics"]
        
        # Fetch new metrics
        metrics = self.metrics_service.get_model_metrics(
            model_id=model_id,
            task_type=task_type,
            time_window="7d"  # Last 7 days
        )
        
        # Cache the results
        self.performance_cache[cache_key] = {
            "metrics": metrics,
            "timestamp": time.time()
        }
        
        return metrics

Cost Manager​

The Cost Manager optimizes model selection based on cost constraints and budget considerations:

class CostManager:
    def __init__(self, budget_service):
        self.budget_service = budget_service
        
    def get_score(self, model_id, task_data):
        """
        Calculate a cost-efficiency score for a model on a given task.
        Higher score means better cost efficiency.
        """
        # Get model cost information
        model_costs = self.budget_service.get_model_costs(model_id)
        
        # Estimate token usage for this task
        estimated_tokens = self._estimate_token_usage(task_data)
        
        # Calculate estimated cost
        estimated_cost = (
            estimated_tokens["input"] * model_costs["cost_per_1k_tokens"]["input"] / 1000 +
            estimated_tokens["output"] * model_costs["cost_per_1k_tokens"]["output"] / 1000
        )
        
        # Get current budget status
        budget_status = self.budget_service.get_budget_status()
        
        # Apply budget considerations
        if budget_status["status"] == "critical":
            # Strongly prefer cheaper models when budget is critical
            cost_score = 1.0 - min(1.0, estimated_cost / budget_status["avg_task_budget"] * 5)
        elif budget_status["status"] == "warning":
            # Prefer cheaper models when budget is warning
            cost_score = 1.0 - min(1.0, estimated_cost / budget_status["avg_task_budget"] * 2)
        else:
            # Normal budget status
            cost_score = 1.0 - min(1.0, estimated_cost / budget_status["avg_task_budget"])
        
        return cost_score
        
    def _estimate_token_usage(self, task_data):
        """Estimate the token usage for input and output based on task data."""
        # Baseline estimations
        input_tokens = len(task_data.get("prompt", "")) / 4  # Rough estimate: 4 chars per token
        
        # Adjust based on task complexity
        complexity_multiplier = {
            "simple": 0.5,
            "medium": 1.0,
            "complex": 2.0
        }.get(task_data.get("complexity", "medium"), 1.0)
        
        # Estimate output tokens based on task type
        output_token_estimates = {
            "code_generation": 1000,
            "text_generation": 500,
            "summarization": 300,
            "classification": 50,
            "question_answering": 200
        }
        
        base_output_tokens = output_token_estimates.get(
            task_data.get("type", "text_generation"), 
            300  # Default value
        )
        
        estimated_output_tokens = base_output_tokens * complexity_multiplier
        
        return {
            "input": input_tokens,
            "output": estimated_output_tokens
        }

Heuristics Manager​

The Heuristics Manager applies special rules and considerations to model selection:

class HeuristicsManager:
    def __init__(self):
        self.heuristics = self._load_heuristics()
        
    def apply_heuristics(self, model_scores, task_data):
        """Apply heuristic rules to adjust model scores based on special cases."""
        adjusted_scores = model_scores.copy()
        
        for heuristic in self.heuristics:
            if self._should_apply_heuristic(heuristic, task_data):
                adjusted_scores = heuristic["adjust_function"](adjusted_scores, task_data)
                
        return adjusted_scores
        
    def _should_apply_heuristic(self, heuristic, task_data):
        """Determine if a heuristic should be applied to this task."""
        condition = heuristic["condition"]
        
        if condition["type"] == "task_type":
            return task_data.get("type") in condition["values"]
            
        elif condition["type"] == "complexity":
            return task_data.get("complexity") in condition["values"]
            
        elif condition["type"] == "token_count":
            estimated_tokens = task_data.get("estimated_tokens", {})
            return estimated_tokens.get("input", 0) > condition["threshold"]
            
        elif condition["type"] == "time_sensitivity":
            return task_data.get("time_sensitivity") in condition["values"]
            
        return False
        
    def _load_heuristics(self):
        """Load heuristic rules from configuration."""
        return [
            {
                "name": "prefer_specialized_for_code",
                "condition": {
                    "type": "task_type",
                    "values": ["code_generation", "code_review", "debugging"]
                },
                "adjust_function": lambda scores, task: self._boost_specialized_models(scores, ["code_generation"], 1.5)
            },
            {
                "name": "prefer_large_context_for_long_inputs",
                "condition": {
                    "type": "token_count",
                    "threshold": 4000
                },
                "adjust_function": lambda scores, task: self._boost_models_by_feature(scores, "large_context", 2.0)
            },
            {
                "name": "prefer_fast_models_for_interactive",
                "condition": {
                    "type": "time_sensitivity",
                    "values": ["high", "critical"]
                },
                "adjust_function": lambda scores, task: self._boost_fast_models(scores, 3.0)
            }
        ]

Request Handler​

The Request Handler manages the flow of requests through the Model Selection system:

class RequestHandler:
    def __init__(self, decision_service, model_clients):
        self.decision_service = decision_service
        self.model_clients = model_clients
        
    async def handle_request(self, request):
        """Process an incoming model request, select model, and route appropriately."""
        # Extract task data
        task_data = self._extract_task_data(request)
        
        # Extract constraints if any
        constraints = request.get("constraints")
        
        # Select the optimal model
        selected_model = self.decision_service.select_model(task_data, constraints)
        
        # Get the appropriate model client
        model_client = self._get_model_client(selected_model)
        
        # Pre-process the request for the selected model
        processed_request = self._preprocess_request(request, selected_model)
        
        # Send request to the model
        try:
            response = await model_client.generate(processed_request)
            return {
                "status": "success",
                "model_used": selected_model,
                "response": response
            }
        except Exception as e:
            # Handle errors and possibly retry with fallback model
            return await self._handle_model_error(e, request, selected_model)
            
    async def _handle_model_error(self, error, request, failed_model):
        """Handle model errors with appropriate fallback strategies."""
        # Log the error
        logger.error(f"Error using model {failed_model}: {str(error)}")
        
        # Get fallback models
        fallback_models = self.decision_service.get_fallbacks(failed_model)
        
        if not fallback_models:
            return {
                "status": "error",
                "error": str(error),
                "model_used": failed_model
            }
        
        # Try fallback models in order
        for fallback_model in fallback_models:
            try:
                model_client = self._get_model_client(fallback_model)
                processed_request = self._preprocess_request(request, fallback_model)
                response = await model_client.generate(processed_request)
                
                # Success with fallback
                return {
                    "status": "success",
                    "model_used": fallback_model,
                    "fallback_from": failed_model,
                    "response": response
                }
            except Exception as fallback_error:
                logger.error(f"Fallback error using {fallback_model}: {str(fallback_error)}")
                # Continue to next fallback
        
        # All fallbacks failed
        return {
            "status": "error",
            "error": "All models including fallbacks failed",
            "model_used": failed_model
        }

Selection Criteria​

Task Complexity Assessment​

The system evaluates task complexity to match with appropriate model capabilities:

Complexity LevelCharacteristicsPreferred Model Size
SimpleBasic tasks, straightforward instructions, limited contextSmall
MediumMulti-step tasks, moderate context, standard reasoningMedium
ComplexAdvanced reasoning, specialized domain knowledge, creative tasksLarge
ExpertSystem design, complex problem-solving, nuanced understandingLarge + Specialized

Context Length Optimization​

Model selection considers the input and expected output lengths:

Domain Specialization​

The system maps task domains to specialized models:

DomainSpecialized ModelsCharacteristics
Software Developmentsoftware-developer-specialized, code-llama-34bCode generation, debugging, software architecture
Requirements Analysisrequirements-analyst-ftNatural language understanding, ambiguity detection
Quality Assuranceqa-specialist-ftTest generation, bug identification, edge case analysis
DevOpsdevops-engineer-ftInfrastructure as code, deployment scripts, monitoring
Technical Writingtechnical-writer-ftDocumentation, clear explanations, formatting

Cost Optimization Strategies​

Different cost optimization strategies are applied based on budget status:

Budget StatusStrategyDescription
NormalBalancedConsider cost as one factor among many in selection
WarningCost-ConsciousGive higher weight to cost in selection criteria
CriticalMinimum ViableUse smallest/cheapest model that can accomplish the task
UnlimitedPerformance-FirstPrioritize performance and quality over cost considerations

Model Fallback Mechanisms​

Fallback Chains​

The system defines fallback sequences for different model categories:

{
  "fallback_chains": {
    "large_general": [
      "gpt-4",
      "claude-2",
      "llama-2-70b",
      "gpt-3.5-turbo"
    ],
    "code_specialized": [
      "software-developer-specialized",
      "code-llama-34b",
      "gpt-4",
      "gpt-3.5-turbo"
    ],
    "requirements_specialized": [
      "requirements-analyst-ft",
      "gpt-4",
      "claude-2",
      "gpt-3.5-turbo"
    ]
  }
}

Fallback Triggers​

Fallbacks are activated based on specific trigger conditions:

  1. Service Unavailability: API errors, timeouts, or service disruptions
  2. Rate Limiting: When usage quotas are exceeded
  3. Context Overflow: When input exceeds model's context window
  4. Quality Threshold: When confidence in output quality is below threshold
  5. Specialized Failure: When a specialized model fails at its primary task

Routing Strategies​

Load Balancing​

The system implements load balancing across equivalent models:

def load_balance_requests(models, request_history):
    """Distribute requests across equivalent models based on current load."""
    model_loads = {}
    
    # Calculate current load for each model
    for model in models:
        recent_requests = request_history.get_recent(model, window="5m")
        current_requests = request_history.get_in_progress(model)
        
        # Calculate load score
        load_score = len(current_requests) + (len(recent_requests) * 0.2)
        model_loads[model] = load_score
    
    # Find the model with minimum load
    min_load_model = min(model_loads.items(), key=lambda x: x[1])[0]
    
    return min_load_model

Scheduled Routing​

Different routing policies can be scheduled based on time periods:

{
  "routing_schedules": [
    {
      "name": "business_hours",
      "active": {
        "days": ["monday", "tuesday", "wednesday", "thursday", "friday"],
        "hours": [9, 10, 11, 12, 13, 14, 15, 16, 17]
      },
      "policy": "performance_priority"
    },
    {
      "name": "off_hours",
      "active": "outside_business_hours",
      "policy": "cost_priority"
    },
    {
      "name": "weekend",
      "active": {
        "days": ["saturday", "sunday"]
      },
      "policy": "balanced"
    }
  ]
}

Request Preprocessing​

Dynamic Prompt Optimization​

The system adapts prompts based on the selected model:

def optimize_prompt_for_model(prompt, model_id, model_registry):
    """Optimize a prompt for a specific model's capabilities and limitations."""
    model_info = model_registry.get_model(model_id)
    
    # Get model-specific optimization rules
    optimization_rules = get_optimization_rules(model_id)
    
    optimized_prompt = prompt
    
    # Apply model-specific optimizations
    for rule in optimization_rules:
        optimized_prompt = apply_optimization_rule(optimized_prompt, rule)
    
    # Handle context length limitations
    if len(optimized_prompt) > model_info["context_window"] * 3.5:  # Approximate tokens
        optimized_prompt = truncate_prompt(optimized_prompt, model_info["context_window"])
    
    # Add model-specific formatting
    optimized_prompt = format_for_model(optimized_prompt, model_id)
    
    return optimized_prompt

Context Windowing​

For long inputs that exceed context limits, the system implements context windowing:

def apply_context_windowing(full_context, query, model_context_limit):
    """Apply a sliding window approach for long contexts."""
    if len(full_context) + len(query) <= model_context_limit:
        return full_context + query
    
    # Calculate how much context we can keep
    available_context_space = model_context_limit - len(query) - 100  # Buffer
    
    # Strategy 1: Keep most recent context
    recent_context = full_context[-available_context_space:]
    
    # Strategy 2: Summarize older context
    older_context = full_context[:-available_context_space]
    summary = generate_summary(older_context)
    
    # Combine summarized older context with recent context
    windowed_context = summary + "\n\n" + recent_context
    
    return windowed_context + query

Monitoring and Feedback​

Performance Metrics​

The system tracks key metrics for continuous improvement:

MetricDescriptionUsage
Success RatePercentage of successful completionsModel reliability assessment
LatencyResponse time by model and task typePerformance optimization
Token EfficiencyOutput quality per token usedCost optimization
Fallback RateFrequency of fallback activationReliability improvement
User SatisfactionFeedback ratings from usersQuality assessment

Feedback Loop​

Continuous improvement through automated and human feedback:

Deployment Configuration​

Kubernetes Deployment​

apiVersion: apps/v1
kind: Deployment
metadata:
  name: model-selection-service
  namespace: engineering-ai-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: model-selection-service
  template:
    metadata:
      labels:
        app: model-selection-service
    spec:
      containers:
        - name: model-selection-service
          image: engineering-ai-agent/model-selection:latest
          ports:
            - containerPort: 8080
          env:
            - name: REDIS_URL
              valueFrom:
                secretKeyRef:
                  name: model-selection-secrets
                  key: redis-url
            - name: METRICS_SERVICE_URL
              valueFrom:
                configMapKeyRef:
                  name: service-endpoints
                  key: metrics-service
            - name: BUDGET_SERVICE_URL
              valueFrom:
                configMapKeyRef:
                  name: service-endpoints
                  key: budget-service
          resources:
            requests:
              memory: "256Mi"
              cpu: "100m"
            limits:
              memory: "512Mi"
              cpu: "300m"
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 30
            periodSeconds: 10
          readinessProbe:
            httpGet:
              path: /health/ready
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 5

API Endpoints​

The Model Selection system exposes the following API endpoints:

EndpointMethodDescription
/models/selectPOSTSelect optimal model for a given task
/modelsGETList all available models with capabilities
/models/{id}GETGet detailed information about a specific model
/models/{id}/performanceGETGet performance metrics for a specific model
/fallbacks/{id}GETGet fallback chain for a specific model
/healthGETHealth check endpoint

Testing Strategy​

Unit Testing​

Tests for individual components:

  • Decision service logic
  • Cost calculation algorithms
  • Performance scoring mechanisms
  • Fallback handling

Integration Testing​

Tests for interactions between components:

  • End-to-end model selection process
  • Metrics collection and analysis
  • Load balancing behavior

Simulation Testing​

Simulation of various scenarios:

  • High load conditions
  • API failures and timeouts
  • Budget constraint scenarios
  • Complex task processing

Future Enhancements​

  1. Reinforcement Learning: Using RL to optimize model selection based on outcomes
  2. Predictive Routing: Anticipating load patterns and preemptively adjusting routing
  3. Multi-model Ensembles: Combining results from multiple models for critical tasks
  4. Automated A/B Testing: Systematic comparison of different routing strategies
  5. User Preference Learning: Tailoring model selection to individual user preferences
  6. Adaptive Cost Management: Dynamic budget allocation based on business priorities
class HeuristicsManager:
    def __init__(self):
        self.heuristics = self._load_heuristics()
        
    def apply_heuristics(self, model_scores, task_data):
        """Apply heuristic rules to adjust model scores based on special cases."""
        adjusted_scores = model_scores.copy()
        
        for heuristic in self.heuristics:
            if self._should_apply_heuristic(heuristic, task_data):
                adjusted_scores = heuristic["adjust_function"](adjusted_scores, task_data)
                
        return adjusted_scores
        
    def _should_apply_heuristic(self, heuristic, task_data):
        """Determine if a heuristic should be applied to this task."""
        condition = heuristic["condition"]
        
        if condition["type"] == "task_type":
            return task_data.get("type") in condition["values"]
            
        elif condition["type"] == "complexity":
            return task_data.get("complexity") in condition["values"]
            
        elif condition["type"] == "token_count":
            estimated_tokens = task_data.get("estimated_tokens", {})
            return estimated_tokens.get("input", 0) > condition["threshold"]
            
        elif condition["type"] == "time_sensitivity":
            return task_data.get("time_sensitivity") in condition["values"]
            
        return False
        
    def _load_heuristics(self):
        """Load heuristic rules from configuration."""
        return [
            {
                "name": "prefer_specialized_for_code",
                "condition": {
                    "type": "task_type",
                    "values": ["code_generation", "code_review", "debugging"]
                },
                "adjust_function": lambda scores, task: self._boost_specialized_models(scores, ["code_generation"], 1.5)
            },
            {
                "name": "prefer_large_context_for_long_inputs",
                "condition": {
                    "type": "token_count",
                    "threshold": 4000
                },
                "adjust_function": lambda scores, task: self._boost_models_by_feature(scores, "large_context", 2.0)
            },
            {
                "name": "prefer_fast_models_for_interactive",
                "condition": {
                    "type": "time_sensitivity",
                    "values": ["high", "critical"]
                },
                "adjust_function": (lambda scores, task: self._boost_fast_models(scores, 3.0))  # Fix: Added parentheses around lambda function
            }
        ]