curl https://www.samuraiapi.in/v1/embeddings \
  -H "Authorization: Bearer $SAMURAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The way of the samurai"
  }'

curl https://www.samuraiapi.in/v1/embeddings \
  -H "Authorization: Bearer $SAMURAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": [
      "The way of the samurai",
      "Machine learning is transforming the world",
      "Tokyo is the capital of Japan"
    ]
  }'

import numpy as np
from openai import OpenAI

client = OpenAI(
    api_key="sk-samurai-YOUR_KEY",
    base_url="https://www.samuraiapi.in/v1"
)

def embed(texts):
    res = client.embeddings.create(model="text-embedding-3-small", input=texts)
    return [d.embedding for d in res.data]

def cosine_sim(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

docs = ["Tokyo is the capital of Japan", "Paris is the capital of France"]
doc_vecs = embed(docs)

query_vec = embed(["What is the capital of Japan?"])[0]
scores = [cosine_sim(query_vec, v) for v in doc_vecs]
print(docs[np.argmax(scores)])
# => "Tokyo is the capital of Japan"

{
  "object": "list",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [0.0023064255, -0.009327292, 0.015797656, "...1536 total floats..."]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}