Voyage Embedding Models

npm install @niledatabase/server @langchain/community

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);

// 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 = await 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);