Voyage Embedding Models

Availble models

Voyage has a collection of specialized models for embedding text from different domains: financial, legal (and large documents), code and medical. It also has highly ranked general embedding model that can be used for a variety of tasks, a general model that is optimized for retrieval, and a smaller cost-efficient retrieval model.

We included a subset here for reference:

Model	Dimensions	Max Tokens	Cost	MTEB Avg Score	Similarity Metric
voyage-large-2-instruct	1024	16000	$0.12 / 1M tokens	68.28	cosine, dot product, L2
voyage-2	1024	4000	$0.1 / 1M tokens		cosine, dot product, L2
voyage-code-2	1536	16000	$0.12 / 1M tokens		cosine, dot product, L2
voyage-law-2	1024	16000	$0.12 / 1M tokens		cosine, dot product, L2

Usage

Voyage has a Python, but not a Javascript, SDK. Their REST API is almost compatible with OpenAI’s API, but unfortunately, their powerful general purpose model, voyage-large-2-instruct requires inputType parameter, which is not supported by OpenAI’s SDK. Fortunately, LangChain has a nice community-contributed JS library for Voyage, which supports the inputType parameter. So we are going to use the LangChain community library in the example below.

Installing dependencies

npm install @niledatabase/server @langchain/community

Generating embeddings with Voyage

const VOYAGE_API_KEY = "your voyage api key";
const { VoyageEmbeddings } = await import(
  "@langchain/community/embeddings/voyage"
);

// we need a separate model object for documents and queries
// because the inputType is different and is in this object
const embedDocs = new VoyageEmbeddings({
  apiKey: VOYAGE_API_KEY, // In Node.js defaults to process.env.VOYAGEAI_API_KEY
  inputType: "document", // for the embedding of documents stored in pg_vector
});

const embedQueries = new VoyageEmbeddings({
  apiKey: VOYAGE_API_KEY,
  inputType: "query", // for the embedding of questions used to search documents
});

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

// embedDocuments is the batch API and can take multiple documents in one call
const doc_vec_resp = await embedDocs.embedDocuments([input_text]);
// it returns an array of embeddings, we have just one
const doc_vec = doc_vec_resp[0];

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

// embedQuery takes a single query string and returns a single vector
const question_vec = await embedQueries.embedQuery(question);

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);

Additional information

Distance metrics

Voyage embeddings are normalized to length 1, which means that you can use L2, cosine, and dot product similarity metrics interchangeably. Dot product is the fastest to compute.

API Rate limits

Note that on the free plan, the rate limits are quite strict. Voyage gives you only 3 API calls a minute. Which means that after you embedded some documents, you can only generate embeddings for 2 queries before running out of the minute and having to wait.