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How to embed text data

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Head to Integrations for documentation on built-in integrations with text embedding providers.

Prerequisites

This guide assumes familiarity with the following concepts:

Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.

The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).

Get started​

Below is an example of how to use the OpenAI embeddings. Embeddings occasionally have different embedding methods for queries versus documents, so the embedding class exposes a embedQuery and embedDocuments method.

tip

See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/core

Get started​

import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings();

Embed queries​

const res = await embeddings.embedQuery("Hello world");
/*
[
   -0.004845875,   0.004899438,  -0.016358767,  -0.024475135, -0.017341806,
    0.012571548,  -0.019156644,   0.009036391,  -0.010227379, -0.026945334,
    0.022861943,   0.010321903,  -0.023479493, -0.0066544134,  0.007977734,
   0.0026371893,   0.025206111,  -0.012048521,   0.012943339,  0.013094575,
   -0.010580265,  -0.003509951,   0.004070787,   0.008639394, -0.020631202,
  ... 1511 more items
]
*/

Embed documents​

const documentRes = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
/*
[
  [
    -0.004845875,   0.004899438,  -0.016358767,  -0.024475135, -0.017341806,
      0.012571548,  -0.019156644,   0.009036391,  -0.010227379, -0.026945334,
      0.022861943,   0.010321903,  -0.023479493, -0.0066544134,  0.007977734,
    0.0026371893,   0.025206111,  -0.012048521,   0.012943339,  0.013094575,
    -0.010580265,  -0.003509951,   0.004070787,   0.008639394, -0.020631202,
    ... 1511 more items
  ]
  [
      -0.009446913,  -0.013253193,   0.013174579,  0.0057552797,  -0.038993083,
      0.0077763423,    -0.0260478, -0.0114384955, -0.0022683728,  -0.016509168,
      0.041797023,    0.01787183,    0.00552271, -0.0049789557,   0.018146982,
      -0.01542166,   0.033752076,   0.006112323,   0.023872782,  -0.016535373,
      -0.006623321,   0.016116094, -0.0061090477, -0.0044155475,  -0.016627092,
    ... 1511 more items
  ]
]
*/

Next steps​

You've now learned how to use embeddings models with queries and text.

Next, check out how to avoid excessively recomputing embeddings with caching, or the full tutorial on retrieval-augmented generation.

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