@juspay/neurolink
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Universal AI Development Platform with working MCP integration, multi-provider support, voice (TTS/STT/realtime), and professional CLI. 58+ external MCP servers discoverable, multimodal file processing, RAG pipelines. Build, test, and deploy AI applicatio
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TypeScript
/**
* @file Batch Strategy
* Batch processing for evaluation pipelines
*/
import type { ScorerInput, PipelineExecutionOptions, BatchEvaluationConfig, BatchEvaluationResult, BatchItemResult } from "../../../types/index.js";
import type { EvaluationPipeline } from "../evaluationPipeline.js";
/**
* Batch evaluation strategy
*/
export declare class BatchStrategy {
private _pipeline;
private _config;
constructor(pipeline: EvaluationPipeline, config?: BatchEvaluationConfig);
/**
* Evaluate a batch of inputs
*/
evaluate(inputs: ScorerInput[], options?: PipelineExecutionOptions): Promise<BatchEvaluationResult>;
/**
* Evaluate a single item
*/
private _evaluateItem;
/**
* Estimate remaining time based on average duration
*/
private _estimateRemainingTime;
/**
* Delay helper
*/
private _delay;
/**
* Update configuration
*/
configure(config: Partial<BatchEvaluationConfig>): void;
}
/**
* Create a batch strategy for a pipeline
*/
export declare function createBatchStrategy(pipeline: EvaluationPipeline, config?: BatchEvaluationConfig): BatchStrategy;
/**
* Evaluate a batch of inputs using a pipeline
*/
export declare function evaluateBatch(pipeline: EvaluationPipeline, inputs: ScorerInput[], config?: BatchEvaluationConfig): Promise<BatchEvaluationResult>;
/**
* Stream batch evaluation results
*/
export declare function streamBatchEvaluation(pipeline: EvaluationPipeline, inputs: ScorerInput[], config?: Omit<BatchEvaluationConfig, "onResult" | "onProgress">): AsyncGenerator<BatchItemResult, BatchEvaluationResult["summary"], void>;