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POST
/
v1
/
embeddings
Native OpenAI format
curl --request POST \
  --url https://api.gravitex.ai/v1/embeddings \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "<string>",
  "input": {},
  "encoding_format": "<string>",
  "dimensions": 123
}
'

Documentation Index

Fetch the complete documentation index at: https://docs.gravitex.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
Introduction

Convert text to vector embeddings for semantic search, similarity, and clustering. Compatible with the OpenAI Embeddings API.

​
Authentication

​
Authorization
string
required
Bearer Token, e.g. Bearer sk-xxxxxxxxxx

​
Request body

​
model
string
required
Model name, e.g. text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
​
input
string | array
required
Text to embed (string or array of strings)
​
encoding_format
string
default:"float"
float or base64
​
dimensions
integer
Output dimensions (some models only)

​
Example

curl https://api.gravitex.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Hello, world"
  }'

​
Python example

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello, world"
)

print(response.data[0].embedding)

​
Supported models

ModelDimensionsNotes
text-embedding-3-small1536Cost-effective
text-embedding-3-large3072High precision
text-embedding-ada-0021536Legacy
  • Pass an array to input for batch embedding
  • Some models support custom dimensions