Embeddings API Guide

Overview

New Model Release

text-embedding-3-small

text-embedding-3-large
Features: Lower cost, better multilingual performance, controllable dimensions

Primary Use Cases

🔍 Search (relevance ranking)

📊 Clustering (similarity grouping)

👍 Recommendation systems

⚠️ Anomaly detection

📈 Diversity analysis

🏷️ Text classification

Basic Usage

Getting Embeddings

from openai import OpenAI
client = OpenAI(
    base_url="https://www.kkiai.com/v1",
    api_key=key
)

response = client.embeddings.create(
    input="Your text string goes here",
    model="text-embedding-3-small"
)

print(response.data[0].embedding)

Response Format

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        // ... more values
      ],
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

Model Comparison

Model	Pages per Dollar	MTEB Score	Max Input
text-embedding-3-small	62,500	62.3%	8191
text-embedding-3-large	9,615	64.6%	8191
text-embedding-ada-002	12,500	61.0%	8191

Practical Examples

Processing Review Data

from openai import OpenAI
client = OpenAI(
    base_url="https://www.kkiai.com/v1",
    api_key=key
)

def get_embedding(text, model="text-embedding-3-small"):
   text = text.replace("\n", " ")
   return client.embeddings.create(input = [text], model=model).data[0].embedding

# Process dataframe
df['ada_embedding'] = df.combined.apply(lambda x: get_embedding(x, model='text-embedding-3-small'))
df.to_csv('output/embedded_1k_reviews.csv', index=False)

# Load saved embeddings
import pandas as pd
import numpy as np

df = pd.read_csv('output/embedded_1k_reviews.csv')
df['ada_embedding'] = df.ada_embedding.apply(eval).apply(np.array)

Technical Details

Dimension Specifications

text-embedding-3-small: Default 1536 dimensions

text-embedding-3-large: Default 3072 dimensions

Dimensions can be adjusted via the Dimensions parameter

Important Notes

Billing is based on input token count

Approximately 800 tokens per page

All models have a maximum input of 8191 tokens