Skip to content
QuantCDN

Generate text embeddings for semantic search and RAG applications

POST
/api/v3/organizations/{organisation}/ai/embeddings

Generates vector embeddings for text content using embedding models. Used for semantic search, document similarity, and RAG applications. * * Features: * - Single text or batch processing (up to 100 texts) * - Configurable dimensions (256, 512, 1024, 8192 for Titan v2) * - Optional normalization to unit length * - Usage tracking for billing * * Use Cases: * - Semantic search across documents * - Similarity matching for content recommendations * - RAG (Retrieval-Augmented Generation) pipelines * - Clustering and classification * * Available Embedding Models: * - amazon.titan-embed-text-v2:0 (default, supports 256-8192 dimensions) * - amazon.titan-embed-text-v1:0 (1536 dimensions fixed)

Authorizations

Parameters

Path Parameters

organisation
required
string

The organisation ID

Request Body required

Embedding request with single or multiple texts

object
input
required
One of:
string
The Australian government announced new climate policy
modelId

Embedding model to use

string
default: amazon.titan-embed-text-v2:0 
amazon.titan-embed-text-v2:0
dimensions

Output embedding dimensions. Titan v2 supports: 256, 512, 1024, 8192

integer
default: 1024 
1024
normalize

Normalize embeddings to unit length (magnitude = 1.0)

boolean
default: true 
true
Example
{
  "input": "The Australian government announced new climate policy",
  "modelId": "amazon.titan-embed-text-v2:0",
  "dimensions": 1024,
  "normalize": true
}

Responses

200

Embeddings generated successfully

object
embeddings
required
One of:

Single embedding vector if input was a string

Array<number>
model
required

Model used to generate embeddings

string
amazon.titan-embed-text-v2:0
dimension
required

Dimensionality of each embedding vector

integer
1024
usage
required
object
inputTokens
required

Number of tokens in input text(s)

integer
totalTokens
required

Total tokens (same as inputTokens for embeddings)

integer
Example
{
  "embeddings": [
    0.0215,
    0.0008,
    0.0312,
    -0.0087,
    0.0273
  ],
  "model": "amazon.titan-embed-text-v2:0",
  "dimension": 1024,
  "usage": {
    "inputTokens": 8,
    "totalTokens": 8
  }
}

400

Invalid request parameters

403

Access denied

500

Failed to generate embeddings