From 95c6bfaa061bccc8c2aa83729e89afd52decd8a6 Mon Sep 17 00:00:00 2001 From: OpenClaw Assistant Date: Sat, 21 Feb 2026 19:27:48 +0000 Subject: [PATCH] chore: remove preset feature, keep simple tier config --- PR_DESCRIPTION.md | 186 ------------------ .../complexity_router/config.py | 53 ----- .../add_model/ComplexityRouterConfig.tsx | 119 ----------- 3 files changed, 358 deletions(-) delete mode 100644 PR_DESCRIPTION.md delete mode 100644 ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx diff --git a/PR_DESCRIPTION.md b/PR_DESCRIPTION.md deleted file mode 100644 index 7d559c5c125..00000000000 --- a/PR_DESCRIPTION.md +++ /dev/null @@ -1,186 +0,0 @@ -# feat(router): Add complexity-based auto routing strategy - -## Summary - -This PR adds a new `complexity_router` - a rule-based routing strategy that uses weighted scoring to classify requests by complexity and route them to appropriate models. Unlike the existing `auto_router` which uses semantic/embedding matching (requiring API calls), the complexity router operates entirely locally in <1ms with zero cost. - -**Also included:** UI updates to make complexity routing accessible via a simple 4-dropdown interface. - -## Motivation - -Many users want intelligent model routing based on query complexity without: -- The latency of embedding API calls -- The cost of embedding API calls -- The complexity of configuring semantic routes with utterances - -The complexity router provides a simple, fast alternative that handles 70-80% of routing decisions well. - -## How It Works - -### Weighted Scoring Across 7 Dimensions - -| Dimension | What It Detects | Score Range | -|-----------|-----------------|-------------| -| `tokenCount` | Short prompts → simple, long → complex | -1.0 to 1.0 | -| `codePresence` | Code keywords (function, class, python, etc.) | 0 to 1.0 | -| `reasoningMarkers` | "step by step", "think through", etc. | 0 to 1.0 | -| `technicalTerms` | Architecture, distributed, ML terms | 0 to 1.0 | -| `simpleIndicators` | "what is", "define", greetings | -1.0 to 0 | -| `multiStepPatterns` | "first...then", numbered steps | 0 to 0.5 | -| `questionComplexity` | Multiple questions | 0 to 0.5 | - -### Tier Assignment - -The weighted score maps to 4 tiers: -- **SIMPLE** (score < 0.25): Quick factual questions, greetings -- **MEDIUM** (0.25 ≤ score < 0.50): Moderate complexity -- **COMPLEX** (0.50 ≤ score < 0.75): Technical, code-heavy requests -- **REASONING** (score ≥ 0.75): Multi-step reasoning required - -**Special Override:** If 2+ reasoning markers are detected in the user message, the request is automatically routed to REASONING tier regardless of overall score. - -## Configuration - -### Via proxy config.yaml - -```yaml -model_list: - - model_name: complexity_router_1 - litellm_params: - model: auto_router/complexity_router - complexity_router_config: - tiers: - SIMPLE: gemini-2.0-flash - MEDIUM: gpt-4o-mini - COMPLEX: claude-sonnet-4 - REASONING: claude-opus-4 - # Optional: adjust tier boundaries (defaults shown) - tier_boundaries: - simple_medium: 0.25 - medium_complex: 0.50 - complex_reasoning: 0.75 - # Optional: adjust token count thresholds - token_thresholds: - simple: 15 # Below = "short" - complex: 400 # Above = "long" -``` - -### Via UI (New!) - -The UI now has a "Router Type" selector with two options: - -1. **Complexity Router (Recommended)** - Simple 4-dropdown interface: - - Simple Tasks: [model dropdown] - - Medium Tasks: [model dropdown] - - Complex Tasks: [model dropdown] - - Reasoning Tasks: [model dropdown] - -2. **Semantic Router** - Existing utterance-based configuration - -Users can now set up smart routing in ~30 seconds by just picking 4 models. - -### Programmatic Usage - -```python -from litellm import Router - -router = Router(model_list=[{ - "model_name": "smart-router", - "litellm_params": { - "model": "auto_router/complexity_router", - "complexity_router_config": { - "tiers": { - "SIMPLE": "gpt-4o-mini", - "MEDIUM": "gpt-4o", - "COMPLEX": "claude-sonnet-4-20250514", - "REASONING": "claude-sonnet-4-20250514", - } - } - } -}]) - -# Routes automatically based on complexity -response = await router.acompletion( - model="smart-router", - messages=[{"role": "user", "content": "What is 2+2?"}] -) # → Routes to gpt-4o-mini (SIMPLE) - -response = await router.acompletion( - model="smart-router", - messages=[{"role": "user", "content": "Think step by step about this distributed systems architecture problem..."}] -) # → Routes to claude-sonnet-4-20250514 (REASONING) -``` - -## Files Changed - -### New Files - Backend -- `litellm/router_strategy/complexity_router/__init__.py` -- `litellm/router_strategy/complexity_router/complexity_router.py` - Main router implementation -- `litellm/router_strategy/complexity_router/config.py` - Configuration and defaults -- `litellm/router_strategy/complexity_router/README.md` - Documentation -- `tests/test_litellm/router_strategy/test_complexity_router.py` - 37 tests - -### New Files - UI -- `ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx` - 4-dropdown tier configuration component - -### Modified Files - Backend -- `litellm/router.py` - Added complexity router initialization and pre-routing hook -- `litellm/types/router.py` - Added `complexity_router_config` and `complexity_router_default_model` params - -### Modified Files - UI -- `ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx` - Added router type selector, integrated ComplexityRouterConfig -- `ui/litellm-dashboard/src/components/add_model/handle_add_auto_router_submit.tsx` - Handle complexity_router model type in submit - -## UI Changes - -### Router Type Selector -![Router Type Selector](docs/images/router-type-selector.png) - -The "Add Auto Router" page now shows: -- **Complexity Router (Recommended)** with a green badge - default selected -- **Semantic Router** for the existing utterance-based approach - -### Complexity Router Configuration -![Complexity Router Config](docs/images/complexity-router-config.png) - -Simple 4-dropdown interface: -- Each dropdown shows available models -- Tooltips explain what each tier handles -- Recommendation card with suggested model types - -## Testing - -```bash -pytest tests/test_litellm/router_strategy/test_complexity_router.py -v -# 37 passed in 0.21s -``` - -Tests cover: -- Scoring logic for each dimension -- Tier assignment at boundaries -- Reasoning marker override -- Model selection -- Pre-routing hook integration -- Edge cases (empty prompts, unicode, very long prompts) -- Configuration overrides - -## Performance - -- **Latency:** <1ms per classification (all local regex/string matching) -- **Cost:** $0 (no API calls) -- **Memory:** Minimal (pre-compiled regex patterns) - -## Inspiration - -This implementation is inspired by [ClawRouter](https://github.com/BlockRunAI/ClawRouter), which uses similar weighted scoring for complexity classification. - -## Checklist - -- [x] Tests added (37 tests) -- [x] Backend implementation complete -- [x] UI implementation complete -- [x] Documentation in PR description -- [x] No breaking changes -- [x] Follows existing patterns (like `auto_router`) - -cc @ishaan-jaff for review diff --git a/litellm/router_strategy/complexity_router/config.py b/litellm/router_strategy/complexity_router/config.py index 07a2589cba0..844791b2853 100644 --- a/litellm/router_strategy/complexity_router/config.py +++ b/litellm/router_strategy/complexity_router/config.py @@ -109,7 +109,6 @@ DEFAULT_TOKEN_THRESHOLDS: Dict[str, int] = { # ─── Default Tier to Model Mapping ─── -# Standard defaults - best cost/performance for most users DEFAULT_TIER_MODELS: Dict[str, str] = { "SIMPLE": "gpt-4o-mini", "MEDIUM": "gpt-4o", @@ -117,68 +116,16 @@ DEFAULT_TIER_MODELS: Dict[str, str] = { "REASONING": "claude-sonnet-4-20250514", } -# Enterprise presets - for teams using specific cloud providers -ENTERPRISE_TIER_PRESETS: Dict[str, Dict[str, str]] = { - # AWS Bedrock - for enterprises on AWS - "bedrock": { - "SIMPLE": "bedrock/anthropic.claude-3-haiku-20240307-v1:0", - "MEDIUM": "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", - "COMPLEX": "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", - "REASONING": "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", - }, - # Google Vertex AI - for enterprises on GCP - "vertex": { - "SIMPLE": "vertex_ai/gemini-2.0-flash", - "MEDIUM": "vertex_ai/gemini-2.0-flash", - "COMPLEX": "vertex_ai/gemini-2.5-pro", - "REASONING": "vertex_ai/gemini-2.5-pro", - }, - # Azure OpenAI - for enterprises on Azure - "azure": { - "SIMPLE": "azure/gpt-4o-mini", - "MEDIUM": "azure/gpt-4o", - "COMPLEX": "azure/gpt-4o", - "REASONING": "azure/o1", - }, - # Direct API (OpenAI + Anthropic) - best quality, recommended for startups - "standard": { - "SIMPLE": "gpt-4o-mini", - "MEDIUM": "gpt-4o", - "COMPLEX": "claude-sonnet-4-20250514", - "REASONING": "claude-sonnet-4-20250514", - }, - # Cost-optimized - maximum savings - "cost_optimized": { - "SIMPLE": "gemini/gemini-2.0-flash", - "MEDIUM": "gpt-4o-mini", - "COMPLEX": "claude-3-5-sonnet-20241022", - "REASONING": "claude-sonnet-4-20250514", - }, -} - class ComplexityRouterConfig(BaseModel): """Configuration for the ComplexityRouter.""" - # Preset name (bedrock, vertex, azure, standard, cost_optimized) - # If set, overrides 'tiers' with the preset values - preset: Optional[str] = Field( - default=None, - description="Preset name: bedrock, vertex, azure, standard, cost_optimized", - ) - # Tier to model mapping tiers: Dict[str, str] = Field( default_factory=lambda: DEFAULT_TIER_MODELS.copy(), description="Mapping of complexity tiers to model names", ) - def model_post_init(self, __context) -> None: - """Apply preset if specified.""" - if self.preset and self.preset in ENTERPRISE_TIER_PRESETS: - # Override tiers with preset values - self.tiers = ENTERPRISE_TIER_PRESETS[self.preset].copy() - # Tier boundaries (normalized scores) tier_boundaries: Dict[str, float] = Field( default_factory=lambda: DEFAULT_TIER_BOUNDARIES.copy(), diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx deleted file mode 100644 index 8a7bba95853..00000000000 --- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx +++ /dev/null @@ -1,119 +0,0 @@ -import React from "react"; -import { Card, Select as AntdSelect, Typography, Tooltip } from "antd"; -import { InfoCircleOutlined } from "@ant-design/icons"; -import { ModelGroup } from "../playground/llm_calls/fetch_models"; - -const { Text } = Typography; - -interface ComplexityTiers { - SIMPLE: string; - MEDIUM: string; - COMPLEX: string; - REASONING: string; -} - -interface ComplexityRouterConfigProps { - modelInfo: ModelGroup[]; - value?: ComplexityTiers; - onChange?: (tiers: ComplexityTiers) => void; -} - -const tierDescriptions = { - SIMPLE: "Quick questions, greetings, simple lookups (e.g., \"What is the capital of France?\")", - MEDIUM: "Moderate complexity, explanations, summaries", - COMPLEX: "Code generation, technical analysis, detailed research", - REASONING: "Multi-step reasoning, complex problem solving, chain-of-thought tasks", -}; - -const tierLabels = { - SIMPLE: "Simple Tasks", - MEDIUM: "Medium Tasks", - COMPLEX: "Complex Tasks", - REASONING: "Reasoning Tasks", -}; - -const ComplexityRouterConfig: React.FC = ({ - modelInfo, - value, - onChange -}) => { - const tiers: ComplexityTiers = value || { - SIMPLE: "", - MEDIUM: "", - COMPLEX: "", - REASONING: "", - }; - - const handleTierChange = (tier: keyof ComplexityTiers, model: string) => { - const updatedTiers = { ...tiers, [tier]: model }; - onChange?.(updatedTiers); - }; - - // Prepare model options for dropdowns - const modelOptions = Array.from( - new Set(modelInfo.map((model) => model.model_group)) - ).map((model_group) => ({ - value: model_group, - label: model_group, - })); - - return ( -
-
- - Configure which model handles each complexity tier. Requests are automatically classified and routed — no training data needed. - -
- - -
- {(["SIMPLE", "MEDIUM", "COMPLEX", "REASONING"] as const).map((tier) => ( -
-
- {tierLabels[tier]} - - - -
- handleTierChange(tier, value)} - placeholder={`Select model for ${tierLabels[tier].toLowerCase()}`} - showSearch - style={{ width: "100%" }} - options={modelOptions} - allowClear - /> - - {tierDescriptions[tier]} - -
- ))} -
- - - {/* Recommendations */} - -
- -
- - Recommendations - - - • Simple: Use fast, cheap models (e.g., GPT-4o-mini, Gemini Flash) -
- • Medium: Balanced models (e.g., GPT-4o, Claude Sonnet) -
- • Complex: Capable models (e.g., Claude Sonnet, GPT-4o) -
- • Reasoning: Best reasoning models (e.g., Claude Opus, o1-preview) - -
-
- -
- ); -}; - -export default ComplexityRouterConfig;