devibe/dist/ai-batch-optimizer.d.ts

Intelligent repository cleanup with auto mode, AI learning, markdown consolidation, auto-consolidate workflow, context-aware classification, and cost optimization

/**
 * AI Batch Optimizer
 *
 * Intelligent batching strategy to maximize files per API call
 * while staying within context window limits.
 */
export interface FileInfo {
    path: string;
    name: string;
    size: number;
    content: string;
}
export interface BatchGroup {
    files: FileWithSample[];
    estimatedInputTokens: number;
    estimatedOutputTokens: number;
    tier: 'micro' | 'small' | 'medium' | 'large';
}
export interface FileWithSample {
    path: string;
    name: string;
    size: number;
    contentSample: string;
    samplingStrategy: string;
}
import type { ModelConfig } from './ai-model-config.js';
declare const DEFAULT_BATCH_CONFIG: {
    CHARS_PER_TOKEN: number;
    BASE_PROMPT_TOKENS: number;
    PER_FILE_OVERHEAD_TOKENS: number;
    PER_FILE_RESPONSE_TOKENS: number;
    SAFETY_MARGIN_PERCENT: number;
    TIERS: {
        micro: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
        small: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
        medium: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
        large: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
    };
};
export interface BatchConfig {
    maxContextTokens: number;
    targetInputTokens: number;
    safetyMargin: number;
    charsPerToken: number;
    basePromptTokens: number;
    perFileOverheadTokens: number;
    perFileResponseTokens: number;
    tiers: typeof DEFAULT_BATCH_CONFIG.TIERS;
}
/**
 * Create model-specific batch configuration
 */
export declare function createBatchConfig(model: ModelConfig): BatchConfig;
export declare const BATCH_CONFIG: {
    CHARS_PER_TOKEN: number;
    BASE_PROMPT_TOKENS: number;
    PER_FILE_OVERHEAD_TOKENS: number;
    PER_FILE_RESPONSE_TOKENS: number;
    SAFETY_MARGIN_PERCENT: number;
    TIERS: {
        micro: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
        small: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
        medium: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
        large: {
            maxSize: number;
            contentChars: number;
            name: string;
        };
    };
};
export declare class AIBatchOptimizer {
    private model?;
    private config;
    constructor(model?: ModelConfig | undefined);
    /**
     * Estimate tokens for a string (conservative estimate)
     */
    private estimateTokens;
    /**
     * Determine optimal content sampling for a file
     */
    private sampleFileContent;
    /**
     * Calculate optimal batches for maximum throughput
     * Now uses model-specific context window limits for intelligent packing
     */
    createOptimalBatches(files: FileInfo[]): BatchGroup[];
    /**
     * Build a compact, token-efficient prompt
     */
    buildCompactPrompt(batch: BatchGroup, repositories: string[]): string;
    /**
     * Get statistics about batching efficiency
     */
    getBatchStats(batches: BatchGroup[]): {
        totalFiles: number;
        totalBatches: number;
        avgFilesPerBatch: number;
        avgInputTokens: number;
        avgOutputTokens: number;
        estimatedCost: number;
        byTier: Record<string, number>;
    };
    /**
     * Validate that batches fit within limits
     */
    validateBatches(batches: BatchGroup[]): {
        valid: boolean;
        issues: string[];
    };
    /**
     * Get model info for reporting
     */
    getModelInfo(): {
        name: string;
        contextWindow: number;
    } | null;
    /**
     * Calculate efficiency metrics
     */
    calculateEfficiency(batches: BatchGroup[]): {
        contextUtilization: number;
        avgBatchSize: number;
        totalApiCalls: number;
        estimatedTimeSeconds: number;
    };
}
export {};
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