voyage-4 Embedding Model

Version: 1

Voyage AI•Last updated April 2026

Text embedding model optimized for general-purpose (including multilingual) retrieval/search and AI applications. 32K context length.

RAG

Multilingual

Low latency

Key capabilities

About this model

Text embedding models are neural networks that transform texts into numerical vectors. They are a crucial building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and are responsible for the retrieval quality. voyage-4 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options. Learn more about voyage-4 here: https://blog.voyageai.com/2026/01/15/voyage-4

Key model capabilities

General-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications.
Supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization, including float (32-bit floating point), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8).
32K token context length.

Usage

The deployed Azure AI Foundry endpoint exposes the Voyage inference API. Authenticate with your Azure ML endpoint key or a bearer token issued for the workspace.

Generate Embeddings

curl <AZUREML_ENDPOINT_URL>/embeddings \
  -X POST \
  -H "Authorization: Bearer <AZUREML_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"input":["Sample text to embed"],"model":"voyage-4"}'

Supported Parameters

input (string or array of strings, required): Text(s) to embed.
model (string, required): voyage-4.
input_type (string, optional): query or document. Tunes embeddings for retrieval.
output_dimension (int, optional): One of 2048, 1024, 512, 256. Defaults to 1024.
output_dtype (string, optional): float, int8, uint8, binary, or ubinary. Defaults to float.
truncation (bool, optional): Truncate inputs longer than the 32K-token context. Defaults to true.
encoding_format (string, optional): Set to base64 to receive embeddings as base64-encoded strings instead of float arrays.

See the full API reference at https://docs.voyageai.com/reference/embeddings-api .

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.00068755, 0.03410244, -0.02404458, 0.04494607]
    }
  ],
  "model": "voyage-4",
  "usage": { "total_tokens": 4 }
}

The embedding array contains the full vector at the requested output_dimension (shown truncated above). When encoding_format is base64, each embedding is returned as a base64 string instead of a float array.

Model Specifications

Context Length32000

LicenseCustom

Last UpdatedApril 2026

Input TypeText

Output TypeText

ProviderVoyage AI

Languages27 Languages

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