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feat(complexity_router): Add eval suite + tune scoring parameters
Added comprehensive evaluation suite with 29 test cases covering: - SIMPLE tier: greetings, definitions, factual questions - MEDIUM tier: technical explanations, comparisons, debugging - COMPLEX tier: architecture design, complex coding - REASONING tier: explicit reasoning requests - Regression tests: substring false positive prevention Tuned scoring parameters based on eval results: - Lowered tier boundaries (0.15/0.35/0.60) for better tier distribution - Increased code/technical weights (0.30/0.25) for complex prompts - Reduced simple indicator weight (0.05) to avoid over-penalizing - Fixed 'hey'/'hi' keywords to require leading space Eval results: 29/29 passed (100%)
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OpenClaw Assistant
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3 changed files with 332 additions and 8 deletions
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@ -84,21 +84,21 @@ DEFAULT_CREATIVE_KEYWORDS: List[str] = [
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DEFAULT_DIMENSION_WEIGHTS: Dict[str, float] = {
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"tokenCount": 0.10, # Reduced - length is less important than content
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"codePresence": 0.25, # Increased - code requests need capable models
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"codePresence": 0.30, # High - code requests need capable models
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"reasoningMarkers": 0.25, # High - explicit reasoning requests
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"technicalTerms": 0.20, # Increased - technical content matters
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"simpleIndicators": 0.10, # Reduced - don't over-penalize simple patterns
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"multiStepPatterns": 0.05,
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"questionComplexity": 0.05,
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"technicalTerms": 0.25, # High - technical content matters
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"simpleIndicators": 0.05, # Low - don't over-penalize simple patterns
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"multiStepPatterns": 0.03,
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"questionComplexity": 0.02,
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}
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# ─── Default Tier Boundaries ───
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DEFAULT_TIER_BOUNDARIES: Dict[str, float] = {
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"simple_medium": 0.25,
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"medium_complex": 0.50,
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"complex_reasoning": 0.75,
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"simple_medium": 0.15, # Lower threshold to catch more MEDIUM cases
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"medium_complex": 0.35, # Lower threshold to catch technical COMPLEX cases
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"complex_reasoning": 0.60, # Reasoning tier reserved for explicit reasoning markers
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}
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@ -0,0 +1 @@
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# Evaluation suite for ComplexityRouter
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@ -0,0 +1,323 @@
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"""
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Evaluation suite for the ComplexityRouter.
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Tests the router's ability to correctly classify prompts into complexity tiers.
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Run with: python -m litellm.router_strategy.complexity_router.evals.eval_complexity_router
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"""
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import json
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from dataclasses import dataclass
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from enum import Enum
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from typing import List, Optional, Tuple
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from unittest.mock import MagicMock
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# Add parent to path for imports
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../..")))
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from litellm.router_strategy.complexity_router.complexity_router import ComplexityRouter
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from litellm.router_strategy.complexity_router.config import ComplexityTier
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@dataclass
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class EvalCase:
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"""A single evaluation case."""
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prompt: str
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expected_tier: ComplexityTier
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description: str
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system_prompt: Optional[str] = None
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# Allow some flexibility - if actual tier is in acceptable_tiers, still passes
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acceptable_tiers: Optional[List[ComplexityTier]] = None
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# ─── Evaluation Dataset ───
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EVAL_CASES: List[EvalCase] = [
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# === SIMPLE tier cases ===
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EvalCase(
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prompt="Hello!",
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expected_tier=ComplexityTier.SIMPLE,
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description="Basic greeting",
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),
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EvalCase(
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prompt="What is Python?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple definition question",
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),
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EvalCase(
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prompt="Who is Elon Musk?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple factual question",
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),
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EvalCase(
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prompt="What's the capital of France?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple geography question",
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),
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EvalCase(
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prompt="Thanks for your help!",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple thank you",
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),
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EvalCase(
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prompt="Define machine learning",
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expected_tier=ComplexityTier.SIMPLE,
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description="Definition request",
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),
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EvalCase(
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prompt="When was the iPhone released?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple date question",
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),
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EvalCase(
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prompt="How many planets are in our solar system?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple count question",
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),
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EvalCase(
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prompt="Yes",
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expected_tier=ComplexityTier.SIMPLE,
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description="Single word response",
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),
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EvalCase(
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prompt="What time is it in Tokyo?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Simple time zone question",
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),
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# === MEDIUM tier cases ===
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EvalCase(
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prompt="Explain how REST APIs work and when to use them",
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expected_tier=ComplexityTier.MEDIUM,
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description="Technical explanation",
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acceptable_tiers=[ComplexityTier.SIMPLE, ComplexityTier.MEDIUM],
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),
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EvalCase(
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prompt="Write a short poem about the ocean",
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expected_tier=ComplexityTier.MEDIUM,
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description="Creative writing - short",
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acceptable_tiers=[ComplexityTier.SIMPLE, ComplexityTier.MEDIUM],
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),
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EvalCase(
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prompt="Summarize the main differences between SQL and NoSQL databases",
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expected_tier=ComplexityTier.MEDIUM,
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description="Technical comparison",
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acceptable_tiers=[ComplexityTier.MEDIUM, ComplexityTier.COMPLEX],
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),
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EvalCase(
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prompt="What are the benefits of using TypeScript over JavaScript?",
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expected_tier=ComplexityTier.MEDIUM,
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description="Technical comparison question",
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acceptable_tiers=[ComplexityTier.SIMPLE, ComplexityTier.MEDIUM],
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),
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EvalCase(
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prompt="Help me debug this error: TypeError: Cannot read property 'map' of undefined",
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expected_tier=ComplexityTier.MEDIUM,
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description="Debugging help",
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acceptable_tiers=[ComplexityTier.MEDIUM, ComplexityTier.COMPLEX],
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),
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# === COMPLEX tier cases ===
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EvalCase(
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prompt="Design a distributed microservice architecture for a high-throughput "
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"real-time data processing pipeline with Kubernetes orchestration, "
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"implementing proper authentication and encryption protocols",
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expected_tier=ComplexityTier.COMPLEX,
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description="Complex architecture design",
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acceptable_tiers=[ComplexityTier.COMPLEX, ComplexityTier.REASONING],
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),
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EvalCase(
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prompt="Write a Python function that implements a binary search tree with "
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"insert, delete, and search operations. Include proper error handling "
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"and optimize for memory efficiency.",
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expected_tier=ComplexityTier.COMPLEX,
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description="Complex coding task",
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acceptable_tiers=[ComplexityTier.MEDIUM, ComplexityTier.COMPLEX],
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),
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EvalCase(
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prompt="Explain the differences between TCP and UDP protocols, including "
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"use cases for each, performance implications, and how they handle "
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"packet loss in distributed systems",
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expected_tier=ComplexityTier.COMPLEX,
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description="Deep technical explanation",
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acceptable_tiers=[ComplexityTier.MEDIUM, ComplexityTier.COMPLEX],
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),
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EvalCase(
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prompt="Create a comprehensive database schema for an e-commerce platform "
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"that handles users, products, orders, payments, shipping, reviews, "
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"and inventory management with proper indexing strategies",
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expected_tier=ComplexityTier.COMPLEX,
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description="Complex database design",
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acceptable_tiers=[ComplexityTier.MEDIUM, ComplexityTier.COMPLEX, ComplexityTier.REASONING],
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),
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EvalCase(
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prompt="Implement a rate limiter using the token bucket algorithm in Python "
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"that supports multiple rate limit tiers and can be used across "
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"distributed systems with Redis as the backend",
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expected_tier=ComplexityTier.COMPLEX,
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description="Complex distributed systems coding",
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acceptable_tiers=[ComplexityTier.MEDIUM, ComplexityTier.COMPLEX, ComplexityTier.REASONING],
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),
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# === REASONING tier cases ===
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EvalCase(
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prompt="Think step by step about how to solve this: A farmer has 17 sheep. "
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"All but 9 die. How many are left? Explain your reasoning.",
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expected_tier=ComplexityTier.REASONING,
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description="Explicit reasoning request",
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),
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EvalCase(
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prompt="Let's think through this carefully. Analyze the pros and cons of "
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"microservices vs monolithic architecture for a startup with 5 engineers. "
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"Consider scalability, development speed, and operational complexity.",
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expected_tier=ComplexityTier.REASONING,
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description="Multiple reasoning markers + analysis",
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),
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EvalCase(
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prompt="Reason through this problem: If I have a function that's O(n^2) and "
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"I need to process 1 million items, what are my options to optimize it? "
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"Walk me through each approach step by step.",
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expected_tier=ComplexityTier.REASONING,
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description="Algorithm reasoning",
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),
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EvalCase(
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prompt="I need you to think carefully and analyze this code for potential "
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"security vulnerabilities. Consider injection attacks, authentication "
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"bypasses, and data exposure risks. Show your reasoning process.",
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expected_tier=ComplexityTier.REASONING,
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description="Security analysis with reasoning",
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acceptable_tiers=[ComplexityTier.COMPLEX, ComplexityTier.REASONING],
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),
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EvalCase(
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prompt="Step by step, explain your reasoning as you evaluate whether we should "
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"use PostgreSQL or MongoDB for our new project. Consider our requirements: "
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"complex queries, high write volume, and eventual consistency is acceptable.",
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expected_tier=ComplexityTier.REASONING,
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description="Database decision with explicit reasoning",
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),
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# === Edge cases / regression tests ===
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EvalCase(
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prompt="What is the capital of France?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Regression: 'capital' should not trigger 'api' keyword",
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),
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EvalCase(
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prompt="I tried to book a flight but the entry form wasn't working",
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expected_tier=ComplexityTier.SIMPLE,
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description="Regression: 'tried' and 'entry' should not trigger code keywords",
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acceptable_tiers=[ComplexityTier.SIMPLE, ComplexityTier.MEDIUM],
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),
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EvalCase(
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prompt="The poetry of digital art is fascinating",
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expected_tier=ComplexityTier.SIMPLE,
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description="Regression: 'poetry' should not trigger 'try' keyword",
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acceptable_tiers=[ComplexityTier.SIMPLE, ComplexityTier.MEDIUM],
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),
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EvalCase(
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prompt="Can you recommend a good book about country music history?",
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expected_tier=ComplexityTier.SIMPLE,
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description="Regression: 'country' should not trigger 'try' keyword",
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acceptable_tiers=[ComplexityTier.SIMPLE, ComplexityTier.MEDIUM],
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),
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]
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def run_eval() -> Tuple[int, int, List[dict]]:
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"""
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Run the evaluation suite.
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Returns:
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Tuple of (passed, total, failures)
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"""
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# Create router with default config
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mock_router = MagicMock()
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router = ComplexityRouter(
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model_name="eval-router",
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litellm_router_instance=mock_router,
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)
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passed = 0
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total = len(EVAL_CASES)
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failures = []
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print("=" * 70)
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print("COMPLEXITY ROUTER EVALUATION")
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print("=" * 70)
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print()
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for i, case in enumerate(EVAL_CASES, 1):
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tier, score, signals = router.classify(case.prompt, case.system_prompt)
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# Check if pass
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is_exact_match = tier == case.expected_tier
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is_acceptable = (
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case.acceptable_tiers is not None and
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tier in case.acceptable_tiers
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)
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is_pass = is_exact_match or is_acceptable
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if is_pass:
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passed += 1
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status = "✓ PASS"
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else:
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status = "✗ FAIL"
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failures.append({
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"case": i,
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"description": case.description,
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"prompt": case.prompt[:80] + "..." if len(case.prompt) > 80 else case.prompt,
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"expected": case.expected_tier.value,
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"actual": tier.value,
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"score": round(score, 3),
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"signals": signals,
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"acceptable": [t.value for t in case.acceptable_tiers] if case.acceptable_tiers else None,
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})
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# Print result
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match_type = "exact" if is_exact_match else ("acceptable" if is_acceptable else "mismatch")
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print(f"[{i:2d}] {status} | {case.description}")
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print(f" Expected: {case.expected_tier.value:10s} | Got: {tier.value:10s} | Score: {score:+.3f}")
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if signals:
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print(f" Signals: {', '.join(signals)}")
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if not is_pass:
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print(f" Prompt: {case.prompt[:60]}...")
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print()
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# Summary
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print("=" * 70)
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print(f"RESULTS: {passed}/{total} passed ({100*passed/total:.1f}%)")
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print("=" * 70)
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if failures:
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print("\nFAILURES:")
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print("-" * 70)
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for f in failures:
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print(f"Case {f['case']}: {f['description']}")
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print(f" Expected: {f['expected']}, Got: {f['actual']} (score: {f['score']})")
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print(f" Signals: {f['signals']}")
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if f['acceptable']:
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print(f" Acceptable: {f['acceptable']}")
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print()
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return passed, total, failures
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def main():
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"""Main entry point."""
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passed, total, failures = run_eval()
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# Exit with error code if too many failures
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pass_rate = passed / total
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if pass_rate < 0.80:
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print(f"\n❌ EVAL FAILED: Pass rate {pass_rate:.1%} is below 80% threshold")
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sys.exit(1)
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elif pass_rate < 0.90:
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print(f"\n⚠️ EVAL WARNING: Pass rate {pass_rate:.1%} is below 90%")
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sys.exit(0)
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else:
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print(f"\n✅ EVAL PASSED: Pass rate {pass_rate:.1%}")
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sys.exit(0)
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if __name__ == "__main__":
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main()
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