Embedding Models served by Fireworks

npm install @niledatabase/server openai

// set up OpenAI SDK with Fireworks configuration
const { OpenAI } = await import("openai");
const FIREWORKS_API_KEY = "your fireworks api key";

const openai = new OpenAI({
  apiKey: FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const MODEL = "thenlper/gte-large"; // or nomic-ai/nomic-embed-text-v1.5
const input_text = `The future belongs to those who believe in the beauty of their 
                    dreams.`;

// generate embeddings
let resp = await openai.embeddings.create({
  model: MODEL,
  input: input_text,
  // nomic-ai/nomic-embed-text-v1.5 can scale down, thenlper/gte-large can't
  // dimensions: 512,
});

// OpenAI's response is an object with an array of
// objects that contain the vector embeddings
let embedding = resp.data[0].embedding;

// 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(embedding.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(embedding.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] - embedding[i]) > 0.000001) {
    same = false;
  }
}

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