How to embed text data

info

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

The Embeddings class is a class designed for interfacing with text embedding models. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.

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

Embeddings can be used to create a numerical representation of textual data. This numerical representation is useful because it can be used to find similar documents.

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

npm install @langchain/openai

Yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

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
  ]
]
*/