chore: initial public snapshot for github upload
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"""
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Configuration for the Complexity Router.
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Contains default keyword lists, weights, tier boundaries, and configuration classes.
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All values are configurable via proxy config.yaml.
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"""
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from enum import Enum
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from typing import Dict, List, Optional
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from pydantic import BaseModel, ConfigDict, Field
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class ComplexityTier(str, Enum):
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"""Complexity tiers for routing decisions."""
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SIMPLE = "SIMPLE"
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MEDIUM = "MEDIUM"
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COMPLEX = "COMPLEX"
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REASONING = "REASONING"
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# ─── Default Keyword Lists ───
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# Note: Keywords should be full words/phrases to avoid substring false positives.
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# The matching logic uses word boundary detection for single-word keywords.
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DEFAULT_CODE_KEYWORDS: List[str] = [
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"function",
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"class",
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"def",
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"const",
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"let",
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"var",
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"import",
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"export",
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"return",
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"async",
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"await",
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"try",
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"catch",
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"exception",
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"error",
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"debug",
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"api",
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"endpoint",
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"request",
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"response",
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"database",
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"sql",
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"query",
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"schema",
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"algorithm",
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"implement",
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"refactor",
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"optimize",
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"python",
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"javascript",
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"typescript",
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"java",
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"rust",
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"golang",
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"react",
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"vue",
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"angular",
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"node",
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"docker",
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"kubernetes",
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"git",
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"commit",
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"merge",
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"branch",
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"pull request",
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]
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DEFAULT_REASONING_KEYWORDS: List[str] = [
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"step by step",
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"think through",
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"let's think",
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"reason through",
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"analyze this",
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"break down",
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"explain your reasoning",
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"show your work",
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"chain of thought",
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"think carefully",
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"consider all",
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"evaluate",
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"pros and cons",
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"compare and contrast",
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"weigh the options",
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"logical",
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"deduce",
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"infer",
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"conclude",
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]
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DEFAULT_TECHNICAL_KEYWORDS: List[str] = [
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"architecture",
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"distributed",
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"scalable",
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"microservice",
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"machine learning",
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"neural network",
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"deep learning",
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"encryption",
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"authentication",
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"authorization",
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"performance",
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"latency",
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"throughput",
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"benchmark",
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"concurrency",
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"parallel",
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"threading",
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"memory",
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"cpu",
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"gpu",
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"optimization",
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"protocol",
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"tcp",
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"http",
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"grpc",
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"websocket",
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"container",
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"orchestration",
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# Note: "async", "kubernetes", "docker" are in DEFAULT_CODE_KEYWORDS
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]
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DEFAULT_SIMPLE_KEYWORDS: List[str] = [
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"what is",
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"what's",
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"define",
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"definition of",
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"who is",
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"who was",
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"when did",
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"when was",
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"where is",
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"where was",
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"how many",
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"how much",
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"yes or no",
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"true or false",
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"simple",
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"brief",
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"short",
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"quick",
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"hello",
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"hi",
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"hey",
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"thanks",
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"thank you",
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"goodbye",
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"bye",
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"okay",
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# Note: "ok" removed due to false positives (matches "token", "book", etc.)
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]
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# ─── Default Dimension Weights ───
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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": 0.30, # High - code requests need capable models
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"reasoningMarkers": 0.25, # High - explicit reasoning requests
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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": 0.15, # 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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# ─── Default Token Thresholds ───
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DEFAULT_TOKEN_THRESHOLDS: Dict[str, int] = {
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"simple": 15, # Only very short prompts (<15 tokens) are penalized
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"complex": 400, # Long prompts (>400 tokens) get complexity boost
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}
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# ─── Default Tier to Model Mapping ───
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DEFAULT_TIER_MODELS: Dict[str, str] = {
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"SIMPLE": "gpt-4o-mini",
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"MEDIUM": "gpt-4o",
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"COMPLEX": "claude-sonnet-4-20250514",
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"REASONING": "claude-sonnet-4-20250514",
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}
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class ComplexityRouterConfig(BaseModel):
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"""Configuration for the ComplexityRouter."""
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# Tier to model mapping
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tiers: Dict[str, str] = Field(
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default_factory=lambda: DEFAULT_TIER_MODELS.copy(),
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description="Mapping of complexity tiers to model names",
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)
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# Tier boundaries (normalized scores)
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tier_boundaries: Dict[str, float] = Field(
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default_factory=lambda: DEFAULT_TIER_BOUNDARIES.copy(),
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description="Score boundaries between tiers",
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)
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# Token count thresholds
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token_thresholds: Dict[str, int] = Field(
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default_factory=lambda: DEFAULT_TOKEN_THRESHOLDS.copy(),
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description="Token count thresholds for simple/complex classification",
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)
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# Dimension weights
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dimension_weights: Dict[str, float] = Field(
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default_factory=lambda: DEFAULT_DIMENSION_WEIGHTS.copy(),
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description="Weights for each scoring dimension",
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)
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# Keyword lists (overridable)
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code_keywords: Optional[List[str]] = Field(
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default=None,
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description="Keywords indicating code-related content",
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)
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reasoning_keywords: Optional[List[str]] = Field(
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default=None,
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description="Keywords indicating reasoning-required content",
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)
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technical_keywords: Optional[List[str]] = Field(
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default=None,
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description="Keywords indicating technical content",
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)
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simple_keywords: Optional[List[str]] = Field(
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default=None,
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description="Keywords indicating simple/basic queries",
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)
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# Default model if scoring fails
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default_model: Optional[str] = Field(
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default=None,
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description="Default model to use if tier cannot be determined",
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)
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model_config = ConfigDict(extra="allow") # Allow additional fields
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# Combined default config
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DEFAULT_COMPLEXITY_CONFIG = ComplexityRouterConfig()
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