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*   Embedding Distance

Embedding Distance
==================

To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector distance metric between the two embedded representations using the `embedding_distance` evaluator.

**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the prediction is to the reference, according to their embedded representation.

    import { loadEvaluator } from "langchain/evaluation";import { FakeEmbeddings } from "@langchain/core/utils/testing";const chain = await loadEvaluator("embedding_distance");const res = await chain.evaluateStrings({  prediction: "I shall go",  reference: "I shan't go",});console.log({ res });/*{ res: { score: 0.09664669666115833 } } */const res1 = await chain.evaluateStrings({  prediction: "I shall go",  reference: "I will go",});console.log({ res1 });/*{ res1: { score: 0.03761174400183265 } } */// Select the Distance Metric// By default, the evalutor uses cosine distance. You can choose a different distance metric if you'd like.const evaluator = await loadEvaluator("embedding_distance", {  distanceMetric: "euclidean",});// Select Embeddings to Use// The constructor uses OpenAI embeddings by default, but you can configure this however you want.const embedding = new FakeEmbeddings();const customEmbeddingEvaluator = await loadEvaluator("embedding_distance", {  embedding,});const res2 = await customEmbeddingEvaluator.evaluateStrings({  prediction: "I shall go",  reference: "I shan't go",});console.log({ res2 });/*{ res2: { score: 2.220446049250313e-16 } } */const res3 = await customEmbeddingEvaluator.evaluateStrings({  prediction: "I shall go",  reference: "I will go",});console.log({ res3 });/*{ res3: { score: 2.220446049250313e-16 } } */

#### API Reference:

*   [loadEvaluator](https://api.js.langchain.com/functions/langchain_evaluation.loadEvaluator.html) from `langchain/evaluation`
*   [FakeEmbeddings](https://api.js.langchain.com/classes/langchain_core_utils_testing.FakeEmbeddings.html) from `@langchain/core/utils/testing`

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