@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
261 lines • 7 kB
JavaScript
/**
* @file Pipeline Builder
* Fluent builder API for creating evaluation pipelines
*/
import { EvaluationPipeline } from "./evaluationPipeline.js";
/**
* Fluent builder for creating evaluation pipelines
*/
export class PipelineBuilder {
_name;
_description;
_scorers = [];
_aggregation = { method: "average" };
_passThreshold = 0.7;
_executionMode = "parallel";
_stopOnFailure = false;
_timeout;
_requiredScorers = [];
constructor(name) {
this._name = name;
}
/**
* Create a new pipeline builder
*/
static create(name) {
return new PipelineBuilder(name);
}
/**
* Set pipeline name
*/
name(name) {
this._name = name;
return this;
}
/**
* Set pipeline description
*/
description(desc) {
this._description = desc;
return this;
}
/**
* Add a scorer by ID
*/
addScorer(id, config) {
this._scorers.push({ id, config });
return this;
}
/**
* Add multiple scorers
*/
addScorers(...ids) {
for (const id of ids) {
this._scorers.push({ id });
}
return this;
}
/**
* Add a scorer and mark it as required
*/
requireScorer(id, config) {
const existing = this._scorers.find((scorer) => scorer.id === id);
if (existing) {
existing.config = {
...existing.config,
...config,
};
}
else {
this._scorers.push({ id, config });
}
if (!this._requiredScorers.includes(id)) {
this._requiredScorers.push(id);
}
return this;
}
/**
* Set aggregation method
*/
aggregateWith(method) {
this._aggregation.method = method;
return this;
}
/**
* Set weights for weighted aggregation
*/
withWeights(weights) {
this._aggregation.method = "weighted";
this._aggregation.weights = weights;
return this;
}
/**
* Set custom aggregation function
*/
customAggregation(fn) {
this._aggregation.method = "custom";
this._aggregation.customFn = fn;
return this;
}
/**
* Set pass/fail threshold
*/
passThreshold(threshold) {
this._passThreshold = threshold;
return this;
}
/**
* Run scorers in parallel (default)
*/
parallel() {
this._executionMode = "parallel";
return this;
}
/**
* Run scorers sequentially
*/
sequential() {
this._executionMode = "sequential";
return this;
}
/**
* Stop pipeline on first failure
*/
stopOnFailure() {
this._stopOnFailure = true;
return this;
}
/**
* Continue pipeline on failures (default)
*/
continueOnFailure() {
this._stopOnFailure = false;
return this;
}
/**
* Set pipeline timeout
*/
timeout(ms) {
this._timeout = ms;
return this;
}
/**
* Build the pipeline configuration
*/
buildConfig() {
return {
name: this._name,
description: this._description,
scorers: this._scorers.map((scorer) => ({
id: scorer.id,
config: scorer.config ? { ...scorer.config } : undefined,
})),
aggregation: {
...this._aggregation,
weights: this._aggregation.weights
? { ...this._aggregation.weights }
: undefined,
},
passThreshold: this._passThreshold,
executionMode: this._executionMode,
stopOnFailure: this._stopOnFailure,
timeout: this._timeout,
requiredScorers: this._requiredScorers.length > 0
? [...this._requiredScorers]
: undefined,
};
}
/**
* Build the pipeline (not initialized)
*/
build() {
return new EvaluationPipeline(this.buildConfig());
}
/**
* Build and initialize the pipeline
*/
async buildAndInitialize() {
const pipeline = this.build();
await pipeline.initialize();
return pipeline;
}
}
/**
* Quick pipeline builder factory
*/
export const Pipelines = {
/**
* Create a new pipeline builder
*/
create: (name) => PipelineBuilder.create(name),
/**
* Create a safety-focused pipeline
*/
safety: () => PipelineBuilder.create("safety")
.description("Safety evaluation pipeline")
.addScorers("toxicity", "bias-detection")
.requireScorer("toxicity")
.aggregateWith("minimum")
.passThreshold(0.8),
/**
* Create a RAG evaluation pipeline
*/
rag: () => PipelineBuilder.create("rag")
.description("RAG evaluation pipeline")
.addScorers("faithfulness", "context-relevancy", "answer-relevancy", "hallucination")
.withWeights({
faithfulness: 1.5,
"context-relevancy": 1.0,
"answer-relevancy": 1.0,
hallucination: 1.5,
})
.passThreshold(0.7),
/**
* Create a quality-focused pipeline
*/
quality: () => PipelineBuilder.create("quality")
.description("Quality evaluation pipeline")
.addScorers("tone-consistency", "prompt-alignment", "length", "format")
.aggregateWith("average")
.passThreshold(0.7),
/**
* Create a comprehensive pipeline with all scorers
*/
comprehensive: () => PipelineBuilder.create("comprehensive")
.description("Comprehensive evaluation pipeline")
.addScorers("toxicity", "bias-detection", "hallucination", "faithfulness", "context-relevancy", "answer-relevancy", "tone-consistency", "prompt-alignment")
.requireScorer("toxicity")
.withWeights({
toxicity: 2.0,
"bias-detection": 1.5,
hallucination: 1.5,
faithfulness: 1.0,
"context-relevancy": 1.0,
"answer-relevancy": 1.0,
"tone-consistency": 0.8,
"prompt-alignment": 0.8,
})
.passThreshold(0.75),
/**
* Create a minimal fast pipeline
*/
minimal: () => PipelineBuilder.create("minimal")
.description("Minimal fast evaluation pipeline")
.addScorers("toxicity", "hallucination")
.parallel()
.passThreshold(0.8),
/**
* Create a summarization evaluation pipeline
*/
summarization: () => PipelineBuilder.create("summarization")
.description("Summarization quality evaluation pipeline")
.addScorers("summarization", "faithfulness", "content-similarity", "length")
.withWeights({
summarization: 1.5,
faithfulness: 1.2,
"content-similarity": 1.0,
length: 0.8,
})
.passThreshold(0.7),
};
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