Embeddings - 302.AI API Document

OpenAI's text embeddings are used to measure the relevance between text strings. Embeddings are typically used for:

Search (where results are ranked based on relevance to the query string)

Clustering (where text strings are grouped by similarity)

Recommendations (suggesting items related to relevant text strings)

Anomaly Detection(identifying outliers that are different or less relevant)

Diversity Measurement (analyzing the distribution of similarities)

Classification (categorizing text strings by the most similar label)

An embedding is a list of floating-point numbers (a vector). The distance between two vectors measures their relevance. A small distance indicates high relevance, while a large distance indicates low relevance.

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Request

Authorization

Provide your bearer token in the

Authorization

header when making requests to protected resources.

Example:

Authorization: Bearer ********************

Header Params

Authorization

string

optional

API Key from 302.AI backend

Example:

Bearer {{YOUR_API_KEY}}

Body Params application/json

model

string

required

ID of the model to be used. You can use the List models API to view all available models, or refer to our model overview to understand their descriptions.

input

string

required

Enter text to get the embedding, which can be encoded as a string or an array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or tokens. Each input must not exceed 8192 tokens.

Example

{
  "model": "text-embedding-ada-002",
  "input": "The food was delicious and the waiter..."
}

Request samples

Shell

JavaScript

Java

Swift

PHP

Python

HTTP

Objective-C

Ruby

OCaml

Dart

Responses

🟢200Create embeddings

application/json

Body

object

string

required

data

array [object {3}]

required

object

string

optional

embedding

array[number]

optional

index

integer

optional

model

string

required

usage

object

required

prompt_tokens

integer

required

total_tokens

integer

required

Example

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}