Embeddings API

curl https://open.tutujin.com/v1/embeddings \
  -H "Authorization: Bearer $TUTUJIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": ["What is the return policy?", "How soon do orders ship?"]
  }'

import os
from openai import OpenAI

client = OpenAI(base_url="https://open.tutujin.com/v1", api_key=os.environ["TUTUJIN_API_KEY"])

resp = client.embeddings.create(
    model="text-embedding-3-small",
    input=["What is the return policy?", "How soon do orders ship?"],
)
vectors = [d.embedding for d in sorted(resp.data, key=lambda d: d.index)]
print(len(vectors), len(vectors[0]), resp.usage)

import OpenAI from 'openai'

const client = new OpenAI({ baseURL: 'https://open.tutujin.com/v1', apiKey: process.env.TUTUJIN_API_KEY })

const resp = await client.embeddings.create({
  model: 'text-embedding-3-small',
  input: ['What is the return policy?', 'How soon do orders ship?'],
})
const vectors = [...resp.data].sort((a, b) => a.index - b.index).map((d) => d.embedding)
console.log(vectors.length, vectors[0].length, resp.usage)

import math


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)))


print(cosine(vectors[0], vectors[1]))

{
  "object": "list",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.0456, 0.0789] },
    { "object": "embedding", "index": 1, "embedding": [0.0234, -0.0112, 0.0567] }
  ],
  "model": "text-embedding-3-small",
  "usage": { "prompt_tokens": 18, "total_tokens": 18 }
}