Framework Integration Architecture
Purpose: Technical specification for implementing progressive evaluation framework composition with intelligent routing, cost optimization, and enterprise-grade monitoring.
Integration Philosophy: Start with proven core frameworks, scale intelligently based on requirements and system maturity.
Technical Architecture Overview
Framework Adapter Pattern
python
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional
from dataclasses import dataclass
from datetime import datetime
@dataclass
class EvaluationRequest:
"""Standardized evaluation request across all frameworks"""
layer: str # L1, L2, L3, L4
query: str
context: Dict[str, Any]
response: str
priority: str = 'medium' # low, medium, high, critical
budget_constraint: Optional[float] = None # USD
quality_requirement: float = 0.8 # 0.0-1.0
@dataclass
class EvaluationResult:
"""Standardized evaluation result across all frameworks"""
framework_name: str
layer: str
overall_score: float
dimension_scores: Dict[str, float] # effectiveness, efficiency, safety, ux
cost_usd: float
latency_ms: int
confidence: float
metadata: Dict[str, Any]
timestamp: datetime
class EvaluationFrameworkAdapter(ABC):
"""Unified interface for all evaluation frameworks"""
@abstractmethod
async def evaluate(
self,
request: EvaluationRequest
) -> EvaluationResult:
"""Execute evaluation with framework-specific logic"""
pass
@abstractmethod
def get_capabilities(self) -> Dict[str, Any]:
"""Return framework capabilities and specializations"""
pass
@abstractmethod
def estimate_cost(self, request: EvaluationRequest) -> float:
"""Predict evaluation cost in USD"""
pass
@abstractmethod
def get_quality_score(self) -> int:
"""Return framework quality score from research analysis"""
passCore Framework Implementations
1. Semantic Kernel Adapter (Primary Orchestrator)
python
class SemanticKernelAdapter(EvaluationFrameworkAdapter):
"""Microsoft Semantic Kernel + Azure AI Foundry integration"""
def __init__(self, azure_config: Dict[str, str]):
self.kernel = self._setup_kernel_with_tracing(azure_config)
self.ai_foundry_client = self._setup_ai_foundry_client(azure_config)
self.quality_score = 91 # From research analysis
async def evaluate(self, request: EvaluationRequest) -> EvaluationResult:
"""Enterprise-grade evaluation with full observability"""
# Execute with automatic tracing
start_time = datetime.utcnow()
with self._trace_context(f"eval_{request.layer}_{request.priority}"):
# Azure AI Foundry evaluation
azure_result = await self.ai_foundry_client.evaluate(
query=request.query,
response=request.response,
context=request.context.get('retrieval_context', ''),
evaluators=['relevance', 'groundedness', 'coherence']
)
# Cost tracking
cost = self._calculate_azure_cost(azure_result)
# Enterprise compliance check
compliance_status = await self._check_enterprise_compliance(
request, azure_result
)
latency_ms = (datetime.utcnow() - start_time).total_seconds() * 1000
return EvaluationResult(
framework_name='semantic_kernel',
layer=request.layer,
overall_score=azure_result['composite_score'],
dimension_scores={
'effectiveness': azure_result['relevance']['score'],
'safety': azure_result['content_safety']['score'],
'coherence': azure_result['coherence']['score'],
'compliance': compliance_status['score']
},
cost_usd=cost,
latency_ms=int(latency_ms),
confidence=0.9, # High confidence for enterprise framework
metadata={
'trace_id': self._get_current_trace_id(),
'compliance_status': compliance_status,
'azure_endpoint': azure_result.get('endpoint_used')
},
timestamp=datetime.utcnow()
)
def get_capabilities(self) -> Dict[str, Any]:
return {
'layers': ['L1', 'L2', 'L3', 'L4'],
'specializations': ['enterprise_monitoring', 'compliance', 'cost_tracking'],
'deployment': 'azure_cloud',
'evaluation_types': ['quality', 'safety', 'performance'],
'enterprise_features': True,
'sla_support': True
}
def estimate_cost(self, request: EvaluationRequest) -> float:
"""Estimate Azure AI Foundry costs"""
base_cost = 0.01 # Base evaluation cost
# Priority multiplier
priority_multipliers = {'low': 0.5, 'medium': 1.0, 'high': 1.5, 'critical': 2.0}
# Layer complexity multiplier
layer_multipliers = {'L1': 1.0, 'L2': 1.2, 'L3': 1.5, 'L4': 1.8}
return base_cost * priority_multipliers[request.priority] * layer_multipliers[request.layer]2. RAGAS Framework Adapter (RAG Specialist)
python
class RAGASAdapter(EvaluationFrameworkAdapter):
"""RAGAS framework for RAG-specific evaluation"""
def __init__(self):
self.quality_score = 90 # From research analysis
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall
self.metrics = [faithfulness, answer_relevancy, context_precision, context_recall]
self.evaluate_func = evaluate
async def evaluate(self, request: EvaluationRequest) -> EvaluationResult:
"""RAG-specific evaluation with mathematical foundations"""
if request.layer != 'L1':
# RAGAS specialized for L1 Knowledge Graph layer
return self._create_not_applicable_result(request)
start_time = datetime.utcnow()
# Prepare RAGAS dataset format
ragas_dataset = {
'question': [request.query],
'answer': [request.response],
'contexts': [request.context.get('retrieval_context', [])],
'ground_truths': request.context.get('ground_truth', [''])
}
# Execute RAGAS evaluation
result = self.evaluate_func(
dataset=ragas_dataset,
metrics=self.metrics
)
latency_ms = (datetime.utcnow() - start_time).total_seconds() * 1000
return EvaluationResult(
framework_name='ragas',
layer=request.layer,
overall_score=result['ragas_score'],
dimension_scores={
'effectiveness': result['answer_relevancy'],
'faithfulness': result['faithfulness'],
'context_precision': result['context_precision'],
'context_recall': result['context_recall']
},
cost_usd=self._estimate_api_cost(request),
latency_ms=int(latency_ms),
confidence=0.85, # High confidence for RAG specialization
metadata={
'metrics_used': [metric.name for metric in self.metrics],
'mathematical_foundation': 'verified',
'specialization': 'RAG_evaluation'
},
timestamp=datetime.utcnow()
)
def get_capabilities(self) -> Dict[str, Any]:
return {
'layers': ['L1'], # Specialized for Knowledge Graph
'specializations': ['rag_evaluation', 'context_assessment', 'faithfulness'],
'deployment': 'local_or_api',
'evaluation_types': ['effectiveness', 'faithfulness'],
'mathematical_foundation': True,
'research_backed': True
}3. DeepEval Adapter (Development Testing)
python
class DeepEvalAdapter(EvaluationFrameworkAdapter):
"""DeepEval framework for developer-centric testing"""
def __init__(self):
self.quality_score = 87 # From research analysis
from deepeval.metrics import AnswerRelevancyMetric, ConversationCompletenessMetric
from deepeval.test_case import LLMTestCase, ConversationalTestCase
self.metrics = {
'answer_relevancy': AnswerRelevancyMetric(threshold=0.7),
'conversation_completeness': ConversationCompletenessMetric()
}
async def evaluate(self, request: EvaluationRequest) -> EvaluationResult:
"""Developer-friendly evaluation with pytest-style interface"""
start_time = datetime.utcnow()
if request.layer == 'L4' and 'conversation_history' in request.context:
# Conversational evaluation for L4 Experience Layer
result = await self._evaluate_conversation(request)
else:
# Standard single-turn evaluation
result = await self._evaluate_single_turn(request)
latency_ms = (datetime.utcnow() - start_time).total_seconds() * 1000
return EvaluationResult(
framework_name='deepeval',
layer=request.layer,
overall_score=result['overall_score'],
dimension_scores=result['dimension_scores'],
cost_usd=self._estimate_api_cost(request), # Local compute preferred
latency_ms=int(latency_ms),
confidence=0.8, # Good confidence for development testing
metadata={
'testing_framework': 'pytest_compatible',
'local_execution': True,
'developer_friendly': True
},
timestamp=datetime.utcnow()
)
def get_capabilities(self) -> Dict[str, Any]:
return {
'layers': ['L4', 'Development'],
'specializations': ['conversation_evaluation', 'pytest_integration', 'local_testing'],
'deployment': 'local_preferred',
'evaluation_types': ['quality', 'conversation_completeness'],
'developer_experience': True,
'ci_cd_integration': True
}Intelligent Framework Router
python
class IntelligentFrameworkRouter:
"""Smart framework selection based on requirements and constraints"""
def __init__(self):
self.framework_registry = {
'semantic_kernel': SemanticKernelAdapter,
'ragas': RAGASAdapter,
'deepeval': DeepEvalAdapter,
'langsmith': LangSmithAdapter, # Phase 2
'trulens': TruLensAdapter, # Phase 2
'hf_evaluate': HFEvaluateAdapter, # Phase 3
'llm_judge': LLMJudgeAdapter # Phase 3
}
self.layer_specializations = {
'L1': ['ragas', 'semantic_kernel', 'hf_evaluate'],
'L2': ['langsmith', 'semantic_kernel', 'deepeval'],
'L3': ['llm_judge', 'trulens', 'semantic_kernel'],
'L4': ['langsmith', 'deepeval', 'semantic_kernel']
}
def select_optimal_frameworks(
self,
request: EvaluationRequest,
constraints: Dict[str, Any]
) -> List[str]:
"""Intelligent framework selection algorithm"""
# Step 1: Get layer-appropriate frameworks
candidate_frameworks = self.layer_specializations.get(request.layer, [])
# Step 2: Filter by budget constraints
if request.budget_constraint:
candidate_frameworks = self._filter_by_budget(
candidate_frameworks, request
)
# Step 3: Select based on priority
if request.priority == 'critical':
# Use multiple frameworks for consensus
return candidate_frameworks[:3]
elif request.priority == 'high':
# Use primary + secondary framework
return candidate_frameworks[:2]
else:
# Use single best framework
return [candidate_frameworks[0]]
def _filter_by_budget(
self,
frameworks: List[str],
request: EvaluationRequest
) -> List[str]:
"""Filter frameworks by budget constraints"""
affordable_frameworks = []
for framework_name in frameworks:
framework_class = self.framework_registry[framework_name]
adapter = framework_class()
estimated_cost = adapter.estimate_cost(request)
if estimated_cost <= request.budget_constraint:
affordable_frameworks.append(framework_name)
return affordable_frameworksCost Optimization Strategies
Budget Management System
python
class EvaluationBudgetManager:
"""Intelligent budget management with cost optimization"""
def __init__(self, daily_budget: float = 100.0):
self.daily_budget = daily_budget
self.cost_tracker = CostTracker()
self.optimization_strategies = [
CachingStrategy(),
AdaptiveEvaluatorStrategy(),
BatchingStrategy()
]
async def optimize_evaluation_plan(
self,
requests: List[EvaluationRequest]
) -> List[EvaluationRequest]:
"""Optimize evaluation plan for cost efficiency"""
current_spend = await self.cost_tracker.get_daily_spend()
remaining_budget = self.daily_budget - current_spend
# Apply optimization strategies
optimized_requests = requests
for strategy in self.optimization_strategies:
optimized_requests = await strategy.optimize(
optimized_requests, remaining_budget
)
return optimized_requests
class CachingStrategy:
"""Intelligent caching for 60-80% cost reduction"""
def __init__(self):
self.cache = EvaluationCache()
async def optimize(
self,
requests: List[EvaluationRequest],
budget: float
) -> List[EvaluationRequest]:
"""Apply caching optimization"""
optimized = []
for request in requests:
cache_key = self._generate_cache_key(request)
if cached_result := await self.cache.get(cache_key):
# Use cached result, zero additional cost
request.cached_result = cached_result
request.estimated_cost = 0.0
else:
# Will need fresh evaluation
pass
optimized.append(request)
return optimizedProgressive Deployment Roadmap
Phase 1: Core Framework Deployment (Weeks 1-4)
yaml
deployment_plan:
week_1:
tasks:
- setup_azure_ai_foundry: "Configure Semantic Kernel integration"
- implement_ragas_adapter: "RAG evaluation for L1 layer"
- basic_deepeval_integration: "Development testing workflows"
deliverables:
- core_framework_orchestrator: "3 framework integration"
- cost_tracking_basic: "Budget monitoring"
- layer_routing_l1_l4: "Basic intelligent routing"
week_2:
tasks:
- intelligent_router_implementation: "Smart framework selection"
- cost_optimization_basic: "Caching and budget controls"
- cross_layer_evaluation_foundation: "Basic coherence analysis"
deliverables:
- production_ready_orchestrator: "80% evaluation coverage"
- cost_dashboard: "Real-time budget tracking"
- quality_monitoring: "Basic SLA tracking"
week_3_4:
tasks:
- enterprise_compliance_integration: "SOC2, GDPR compliance"
- advanced_routing_logic: "Priority-based framework selection"
- comprehensive_testing: "Full integration testing"
deliverables:
- enterprise_ready_system: "Production deployment ready"
- comprehensive_monitoring: "Full observability"
- documentation_complete: "Deployment guides"
success_criteria:
cost_efficiency: "30-50% cost reduction through optimization"
coverage: "80%+ evaluation needs met with 3 core frameworks"
latency: "<2 seconds average evaluation time"
reliability: ">99% uptime for core evaluation capabilities"Phase 2: Enhanced Framework Integration (Weeks 5-8)
yaml
enhanced_deployment:
langsmith_integration:
trigger: "Application tracing requirements"
timeline: "Week 5-6"
capabilities: ["conversation_evaluation", "human_annotation", "a_b_testing"]
trulens_integration:
trigger: "Comprehensive observability requirements"
timeline: "Week 7-8"
capabilities: ["system_instrumentation", "performance_monitoring", "anomaly_detection"]
advanced_features:
cross_layer_coherence: "Novel evaluation methodology implementation"
multi_framework_consensus: "Critical decision validation"
causal_evaluation: "Layer attribution analysis"
success_criteria:
coverage_improvement: "90%+ evaluation needs coverage"
advanced_capabilities: "Cross-layer coherence analysis operational"
enterprise_features: "Full enterprise monitoring and compliance"Documentation Index
- Cost Optimization Strategies — Budget management and optimization techniques
- Cross-Layer Evaluation Methodology — Novel coherence analysis implementation
- Framework Performance Benchmarks — Performance comparison and SLA definitions
Document Status: Alpha | Last Updated: 2025-09-07 | Next Review: Phase 1 Implementation Completion