Python Configuration Methods
Python Using Embeddings for Vectorization Embeddings API Guide# Overview# New Model Release# text-embedding-3-large Features: Lower cost, better multilingual performance, controllable dimensions
Primary Use Cases# 🔍 Search (relevance ranking)
📊 Clustering (similarity grouping)
Basic Usage# Getting Embeddings# {
"object" : "list" ,
"data" : [
{
"object" : "embedding" ,
"index" : 0 ,
"embedding" : [
-0.006929283495992422 ,
-0.005336422007530928 ,
] ,
}
] ,
"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 Application Examples# Processing Review Data# 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
Modified at 2026-03-06 15:34:52