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Availble models

Cohere has multiple models with various sizes and languages, here’s the one we use in our example. You can replace it with any embedding model supported by Cohere:
ModelDimensionsMax TokensCostMTEB Avg ScoreSimilarity Metrics
embed-english-v3.01024512$0.10 / 1M Tokens64.5cosine

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Usage

Cohere’s API includes an inputType that allows you to specify the type of input you are embedding. For example, is it a document that you will later retrieve? a question that you use for searching documents? Texts for classification or clustering?

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Installing dependencies

npm install @niledatabase/server cohere-ai

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Generating embeddings with Cohere

const { CohereClient } = require("cohere-ai");
const COHERE_API_KEY = "your cohere api key";

const cohere = new CohereClient({
  token: COHERE_API_KEY,
});

const model = "embed-english-v3.0"; // replace with your favorite cohere model

const input_text = `The future belongs to those who believe in the beauty of their 
                    dreams.`;

const doc_vec_resp = await cohere.embed({
  texts: [input_text], // you can pass multiple texts to embed in one batch call
  model: model,
  inputType: "search_document", // for embedding of documents stored in pg_vector
});

const question = "Who does the future belong to?";

const question_vec_resp = await cohere.embed({
  texts: [question],
  model: model,
  inputType: "search_query", // for embedding of questions used to search documents
});

// Cohere's response is an object with an array of vector embeddings
// The object has other useful info like the model used, input text, billing, etc.
const doc_vec = doc_vec_resp.embeddings[0];
const question_vec = question_vec_resp.embeddings[0];

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Storing and retrieving the embeddings

// set up Nile as the vector store
const { Nile } = await import("@niledatabase/server");
const NILEDB_USER = "you need a nile user with access to a nile database";
const NILEDB_PASSWORD = "and a password for that user";
const nile = Nile({
  user: NILEDB_USER,
  password: NILEDB_PASSWORD,
});

// store vector in a table
await nile.db.query("INSERT INTO embeddings (embedding) values ($1)", [
  JSON.stringify(doc_vec.map((v) => Number(v))),
]);

// search for similar vectors
let db_resp = await nile.db.query(
  "select embedding from embeddings order by embedding<=>$1 limit 1",
  [JSON.stringify(question_vec.map((v) => Number(v)))]
);

// Postgres returns an object, with array of rows each column is a
// property of the row the vector is represented as a string
let similar_str = db_resp.rows[0].embedding;
let similar = similar_str
  .substring(1, similar_str.length - 1)
  .split(",")
  .map((v) => parseFloat(v));

// check that we got the same vector back
let same = true;

for (let i = 0; i < similar.length; i++) {
  if (Math.abs(similar[i] - doc_vec[i]) > 0.000001) {
    same = false;
  }
}

console.log("got same vector? " + same);