Text Embedding 3 Small

Text-embedding-3-small is a compact and efficient model developed for generating high-quality text embeddings. These embeddings are numerical representations of text data, enabling a variety of natural language processing (NLP) tasks such as semantic search, clustering, and text classification

~1.26s
~$0
 1import requests
 2import json
 3
 4url = "https://api.segmind.com/v1/text-embedding-3-small"
 5headers = {
 6    "x-api-key": "YOUR_API_KEY",
 7    "Content-Type": "application/json"
 8}
 9
10data = {
11    "prompt": "You are beautiful"
12}
13
14response = requests.post(url, headers=headers, json=data)
15
16if response.status_code == 200:
17    result = response.json()
18    print(json.dumps(result, indent=2))
19else:
20    print(f"Error: {response.status_code}")
21    print(response.text)

API Endpoint

POSThttps://api.segmind.com/v1/text-embedding-3-small

Parameters

promptrequired
string

Prompt to render

Response Type

Returns: Text/JSON

Common Error Codes

The API returns standard HTTP status codes. Detailed error messages are provided in the response body.

400

Bad Request

Invalid parameters or request format

401

Unauthorized

Missing or invalid API key

403

Forbidden

Insufficient permissions

404

Not Found

Model or endpoint not found

406

Insufficient Credits

Not enough credits to process request

429

Rate Limited

Too many requests

500

Server Error

Internal server error

502

Bad Gateway

Service temporarily unavailable

504

Timeout

Request timed out