POST https://api.assisters.dev/v1/embeddings

response = client.embeddings.create(
    model="assisters-embed-v1",
    input=[
        "First document to embed",
        "Second document to embed",
        "Third document to embed"
    ]
)

for i, data in enumerate(response.data):
    print(f"Document {i}: {len(data.embedding)} dimensions")

import numpy as np
from openai import OpenAI

client = OpenAI(
    api_key="ask_your_api_key",
    base_url="https://api.assisters.dev/v1"
)

# Your documents
documents = [
    "Python is a programming language",
    "JavaScript runs in the browser",
    "Machine learning uses algorithms",
    "Cats are furry pets"
]

# Embed all documents
doc_response = client.embeddings.create(
    model="assisters-embed-v1",
    input=documents
)
doc_embeddings = [d.embedding for d in doc_response.data]

# Embed the query
query = "What programming languages are there?"
query_response = client.embeddings.create(
    model="assisters-embed-v1",
    input=query
)
query_embedding = query_response.data[0].embedding

# Calculate cosine similarity
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Find most similar documents
similarities = [cosine_similarity(query_embedding, doc) for doc in doc_embeddings]
ranked = sorted(zip(documents, similarities), key=lambda x: x[1], reverse=True)

for doc, score in ranked:
    print(f"{score:.4f}: {doc}")

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.0023064255,
        -0.009327292,
        0.015797347,
        ...
      ]
    }
  ],
  "model": "assisters-embed-v1",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  },
  "cost": {
    "total_usd": 0.000001,
    "price_per_million_tokens": 0.1
  }
}