b1b3fe42df
- 4 providers: Groq, OpenRouter, Together AI, Cerebras - Regex-based prompt classifier (code/math/reasoning/creative/general) - Instance-level round-robin state (no shared module globals) - Sliding-window health tracker (latency, error rate, rate-limit rate) - Auto-fallback on 429/5xx with per-attempt latency tracking - Telemetry hook for all routing decisions (auto + explicit) - OpenRouter recommended headers (HTTP-Referer, X-Title) - 47 tests across 5 test files, zero runtime deps
86 lines
3.1 KiB
TypeScript
86 lines
3.1 KiB
TypeScript
import type { ClassificationResult, PromptCategory } from './types.js';
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// ── Keyword patterns for classification ────────────────────────
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const CODE_PATTERNS = [
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/\b(function|const |let |var |class |import |export |return |async |await )\b/,
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/\b(def |print\(|if __name__|lambda )\b/,
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/[{}();]=>/,
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/```[\s\S]*```/,
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/\b(typescript|javascript|python|rust|golang|java|kotlin|swift|sql|html|css|react|node)\b/i,
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/\b(debug|refactor|compile|build|deploy|lint|test|api|endpoint|route|middleware)\b/i,
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/\b(fix|bug|error|exception|stack trace|undefined|null|NaN)\b/i,
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];
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const MATH_PATTERNS = [
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/\b(calculate|compute|solve|equation|formula|integral|derivative|matrix)\b/i,
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/\b(probability|statistics|regression|correlation|variance|median|mean)\b/i,
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/\b(algebra|geometry|calculus|theorem|proof|hypothesis)\b/i,
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/[+\-*/^=]{2,}/,
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/\d+\s*[+\-*/^]\s*\d+/,
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];
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const REASONING_PATTERNS = [
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/\b(explain|analyze|compare|evaluate|reason|logic|argument|conclusion)\b/i,
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/\b(why|how does|what if|pros and cons|trade-?offs|implications)\b/i,
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/\b(step[- ]by[- ]step|chain of thought|think through|break down)\b/i,
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/\b(strategy|approach|methodology|framework|architecture|design)\b/i,
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];
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const CREATIVE_PATTERNS = [
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/\b(write|compose|draft|create|generate|story|poem|essay|blog|article)\b/i,
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/\b(creative|imaginative|brainstorm|ideas|fiction|narrative|dialogue)\b/i,
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/\b(rewrite|rephrase|summarize|translate|tone|style|voice)\b/i,
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];
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// ── Token estimation ───────────────────────────────────────────
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/**
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* Rough token estimate: ~4 chars per token for English text.
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* Good enough for routing decisions.
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*/
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function estimateTokens(text: string): number {
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return Math.ceil(text.length / 4);
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}
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// ── Classifier ─────────────────────────────────────────────────
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function countMatches(text: string, patterns: RegExp[]): number {
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let count = 0;
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for (const pattern of patterns) {
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if (pattern.test(text)) count++;
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}
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return count;
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}
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/**
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* Classify a prompt into a category based on keyword matching.
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* No LLM needed — pure regex heuristics.
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*/
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export function classifyPrompt(
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messages: { role: string; content: string }[]
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): ClassificationResult {
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const fullText = messages.map(m => m.content).join('\n');
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const estimatedTokens = estimateTokens(fullText);
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const scores: Record<PromptCategory, number> = {
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code: countMatches(fullText, CODE_PATTERNS),
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math: countMatches(fullText, MATH_PATTERNS),
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reasoning: countMatches(fullText, REASONING_PATTERNS),
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creative: countMatches(fullText, CREATIVE_PATTERNS),
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general: 1, // baseline
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};
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// Pick highest scoring category
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let best: PromptCategory = 'general';
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let bestScore = 0;
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for (const [cat, score] of Object.entries(scores) as [PromptCategory, number][]) {
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if (score > bestScore) {
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bestScore = score;
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best = cat;
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}
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}
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return { category: best, estimatedTokens };
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}
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