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 = 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);