Cohere Embedding Models

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:

Model	Dimensions	Max Tokens	Cost	MTEB Avg Score	Similarity Metrics
embed-english-v3.0	1024	512	$0.10 / 1M Tokens	64.5	cosine

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?

Installing dependencies

npm install @niledatabase/server cohere-ai

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];

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);