Embeddings

import math
import os

from openai import OpenAI

client = OpenAI(base_url="https://api.naga.ac/v1", api_key=os.environ["NAGA_API_KEY"])

query = "I forgot my login"
articles = [
    "How do I reset my password?",
    "Where can I download my invoice?",
    "How do I delete my account?",
]

result = client.embeddings.create(model="text-embedding-3-small", input=[query, *articles])
vectors = [item.embedding for item in sorted(result.data, key=lambda item: item.index)]


def cosine(a, b):
    dot = sum(x * y for x, y in zip(a, b))
    return dot / (math.sqrt(sum(x * x for x in a)) * math.sqrt(sum(y * y for y in b)))


ranked = sorted(zip(articles, vectors[1:]), key=lambda pair: cosine(vectors[0], pair[1]), reverse=True)
for article, vector in ranked:
    print(f"{cosine(vectors[0], vector):.3f}  {article}")

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.naga.ac/v1", apiKey: process.env.NAGA_API_KEY });

const query = "I forgot my login";
const articles = [
  "How do I reset my password?",
  "Where can I download my invoice?",
  "How do I delete my account?",
];

const result = await client.embeddings.create({
  model: "text-embedding-3-small",
  input: [query, ...articles],
});
const vectors = [...result.data].sort((a, b) => a.index - b.index).map((item) => item.embedding);

function cosine(a, b) {
  const dot = a.reduce((sum, x, i) => sum + x * b[i], 0);
  return dot / (Math.hypot(...a) * Math.hypot(...b));
}

const ranked = articles
  .map((article, i) => ({ article, score: cosine(vectors[0], vectors[i + 1]) }))
  .sort((a, b) => b.score - a.score);

for (const { article, score } of ranked) console.log(score.toFixed(3), article);

curl https://api.naga.ac/v1/embeddings \
  -H "Authorization: Bearer $NAGA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-3-small", "input": ["I forgot my login", "How do I reset my password?", "Where can I download my invoice?", "How do I delete my account?"]}'