Text embedding models
The Voyage text embedding endpoint receives as input a string (or a list of strings) and other arguments such as the preferred model name, and returns a response containing a list of embeddings.
Request body
Name of the model. Recommended options: voyage-4-large, voyage-4, voyage-4-lite, voyage-3-large, voyage-3.5, voyage-3.5-lite, voyage-code-3, voyage-finance-2, voyage-law-2.
Type of the input text. Defaults to null. Other options: query, document. <ul> <li> When input_type is null, the embedding model directly converts the inputs into numerical vectors. For retrieval/search purposes, where a "query" is used to search for relevant information among a collection of data referred to as "documents," we recommend specifying whether your inputs are intended as queries or documents by setting input_type to query or document, respectively. In these cases, Voyage automatically prepends a prompt to your inputs before vectorizing them, creating vectors more tailored for retrieval/search tasks. Embeddings generated with and without the input_type argument are compatible. </li> <li> For transparency, the following prompts are prepended to your input. </li>
<ul> <li> For <code>query</code>, the prompt is <i>"Represent the query for retrieving supporting documents: ".</i> </li> <li> For <code>document</code>, the prompt is <i>"Represent the document for retrieval: ".</i> </li> </ul> <ul> <ul>Whether to truncate the input texts to fit within the context length. Defaults to true. <ul> <li> If true, an over-length input texts will be truncated to fit within the context length, before vectorized by the embedding model. </li> <li> If false, an error will be raised if any given text exceeds the context length. </li> </ul>
The number of dimensions for resulting output embeddings. Defaults to null. <ul> <li> Most models only support a single default dimension, used when output_dimension is set to null (see output embedding dimensions <a href="https://docs.voyageai.com/docs/embeddings" target="_blank">here</a>). </li> <li> voyage-4-large, voyage-4, voyage-4-lite, voyage-3-large, voyage-3.5, and voyage-3.5-lite, and voyage-code-3 support the following output_dimension values: 2048, 1024 (default), 512, and 256. </li> </ul>
The data type for the embeddings to be returned. Defaults to float. Other options: int8, uint8, binary, ubinary. float is supported for all models. int8, uint8, binary, and ubinary are supported by voyage-4-large, voyage-4, voyage-4-lite, voyage-3-large, voyage-3.5, and voyage-3.5-lite, and voyage-code-3. Please see our <a href="https://docs.voyageai.com/docs/flexible-dimensions-and-quantization#quantization" target="_blank">guide</a> for more details about output data types. <ul> <li> float: Each returned embedding is a list of 32-bit (4-byte) <a href="https://en.wikipedia.org/wiki/Single-precision_floating-point_format" target="_blank">single-precision floating-point</a> numbers. This is the default and provides the highest precision / retrieval accuracy. </li> <li> int8 and uint8: Each returned embedding is a list of 8-bit (1-byte) integers ranging from -128 to 127 and 0 to 255, respectively. </li> <li> binary and ubinary: Each returned embedding is a list of 8-bit integers that represent bit-packed, quantized single-bit embedding values: int8 for binary and uint8 for ubinary. The length of the returned list of integers is 1/8 of output_dimension (which is the actual dimension of the embedding). The binary type uses the offset binary method. Please refer to our guide for details on <a href="https://docs.voyageai.com/docs/flexible-dimensions-and-quantization#offset-binary" target="_blank">offset binary</a> and <a href="https://docs.voyageai.com/docs/flexible-dimensions-and-quantization#quantization" target="_blank">binary embeddings</a>. </ul>
Format in which the embeddings are encoded. Defaults to null. Other options: base64. <ul> <li> If null, each embedding is an array of float numbers when output_dtype is set to float and as an array of integers for all other values of output_dtype (int8, uint8, binary, and ubinary). <li> If base64, the embeddings are represented as a <a href="https://docs.python.org/3/library/base64.html" target="_blank">Base64-encoded</a> NumPy array of: </li>
<ul> <li> Floating-point numbers (<a href="https:/umpy.org/doc/2.1/user/basics.types.html#numerical-data-types" target="_blank">numpy.float32</a>) for <code>output_dtype</code> set to <code>float</code>. </li> <li> Signed integers (<a href="https:/umpy.org/doc/2.1/user/basics.types.html#numerical-data-types" target="_blank">numpy.int8</a>) for <code>output_dtype</code> set to <code>int8</code> or <code>binary</code>. </li> <li> Unsigned integers (<a href="https:/umpy.org/doc/2.1/user/basics.types.html#numerical-data-types" target="_blank">numpy.uint8</a>) for <code>output_dtype</code> set to <code>uint8</code> or <code>ubinary</code>. </li> </ul> </ul>Response
Success
The object type, which is always list.
Name of the model.