agsamantha/node_modules/langchain/dist/evaluation/embedding_distance/base.js

import { OpenAIEmbeddings } from "@langchain/openai";
import { PairwiseStringEvaluator, StringEvaluator, } from "../base.js";
import { cosine } from "../../util/ml-distance/similarities.js";
import { chebyshev, manhattan } from "../../util/ml-distance/distances.js";
import { euclidean } from "../../util/ml-distance-euclidean/euclidean.js";
/**
 * Get the distance function for the given distance type.
 * @param distance The distance type.
 * @return The distance function.
 */
export function getDistanceCalculationFunction(distanceType) {
    const distanceFunctions = {
        cosine: (X, Y) => 1.0 - cosine(X, Y),
        euclidean,
        manhattan,
        chebyshev,
    };
    return distanceFunctions[distanceType];
}
/**
 * Compute the score based on the distance metric.
 * @param vectors The input vectors.
 * @param distanceMetric The distance metric.
 * @return The computed score.
 */
export function computeEvaluationScore(vectors, distanceMetric) {
    const metricFunction = getDistanceCalculationFunction(distanceMetric);
    return metricFunction(vectors[0], vectors[1]);
}
/**
 * Use embedding distances to score semantic difference between
 * a prediction and reference.
 */
export class EmbeddingDistanceEvalChain extends StringEvaluator {
    constructor(fields) {
        super();
        Object.defineProperty(this, "requiresReference", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: true
        });
        Object.defineProperty(this, "requiresInput", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: false
        });
        Object.defineProperty(this, "outputKey", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "score"
        });
        Object.defineProperty(this, "embedding", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "distanceMetric", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "cosine"
        });
        this.embedding = fields?.embedding || new OpenAIEmbeddings();
        this.distanceMetric = fields?.distanceMetric || "cosine";
    }
    _chainType() {
        return `embedding_${this.distanceMetric}_distance`;
    }
    async _evaluateStrings(args, config) {
        const result = await this.call(args, config);
        return { [this.outputKey]: result[this.outputKey] };
    }
    get inputKeys() {
        return ["reference", "prediction"];
    }
    get outputKeys() {
        return [this.outputKey];
    }
    async _call(values, _runManager) {
        const { prediction, reference } = values;
        if (!this.embedding)
            throw new Error("Embedding is undefined");
        const vectors = await this.embedding.embedDocuments([
            prediction,
            reference,
        ]);
        const score = computeEvaluationScore(vectors, this.distanceMetric);
        return { [this.outputKey]: score };
    }
}
/**
 * Use embedding distances to score semantic difference between two predictions.
 */
export class PairwiseEmbeddingDistanceEvalChain extends PairwiseStringEvaluator {
    constructor(fields) {
        super();
        Object.defineProperty(this, "requiresReference", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: false
        });
        Object.defineProperty(this, "requiresInput", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: false
        });
        Object.defineProperty(this, "outputKey", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "score"
        });
        Object.defineProperty(this, "embedding", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "distanceMetric", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "cosine"
        });
        this.embedding = fields?.embedding || new OpenAIEmbeddings();
        this.distanceMetric = fields?.distanceMetric || "cosine";
    }
    _chainType() {
        return `pairwise_embedding_${this.distanceMetric}_distance`;
    }
    async _evaluateStringPairs(args, config) {
        const result = await this.call(args, config);
        return { [this.outputKey]: result[this.outputKey] };
    }
    get inputKeys() {
        return ["prediction", "predictionB"];
    }
    get outputKeys() {
        return [this.outputKey];
    }
    async _call(values, _runManager) {
        const { prediction, predictionB } = values;
        if (!this.embedding)
            throw new Error("Embedding is undefined");
        const vectors = await this.embedding.embedDocuments([
            prediction,
            predictionB,
        ]);
        const score = computeEvaluationScore(vectors, this.distanceMetric);
        return { [this.outputKey]: score };
    }
}