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*   [Get started](/v0.1/docs/get_started/)
    
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            *   [Pairwise String Comparison](/v0.1/docs/guides/evaluation/comparison/pairwise_string/)
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*   [Comparison Evaluators](/v0.1/docs/guides/evaluation/comparison/)
*   Pairwise Embedding Distance

Pairwise Embedding Distance
===========================

One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.

You can load the `pairwise_embedding_distance` evaluator to do this.

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

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { OpenAIEmbeddings } from "@langchain/openai";import { loadEvaluator } from "langchain/evaluation";const embedding = new OpenAIEmbeddings();const chain = await loadEvaluator("pairwise_embedding_distance", { embedding });const res = await chain.evaluateStringPairs({  prediction: "Seattle is hot in June",  predictionB: "Seattle is cool in June.",});console.log({ res });/*  { res: { score: 0.03633645503883243 } }*/const res1 = await chain.evaluateStringPairs({  prediction: "Seattle is warm in June",  predictionB: "Seattle is cool in June.",});console.log({ res1 });/*  { res1: { score: 0.03657957473761331 } }*/

#### API Reference:

*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [loadEvaluator](https://api.js.langchain.com/functions/langchain_evaluation.loadEvaluator.html) from `langchain/evaluation`

* * *

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