Generate embeddings for text input | PrivateGPT

Generate vector embeddings from input text.

This endpoint converts text into high-dimensional vector representations that capture semantic meaning. These embeddings preserve semantic relationships between texts and can be used for various machine learning tasks.

Notes:

Empty strings or arrays are not accepted
Results include index numbers for mapping back to original inputs
All embeddings are generated as query vectors. If the embeddings have two modes (e.g., query and document), this endpoint only returns query vectors.

Generate vector embeddings from input text. This endpoint converts text into high-dimensional vector representations that capture semantic meaning. These embeddings preserve semantic relationships between texts and can be used for various machine learning tasks. Notes: * Empty strings or arrays are not accepted * Results include index numbers for mapping back to original inputs * All embeddings are generated as query vectors. If the embeddings have two modes (e.g., query and document), this endpoint only returns query vectors.

Request

Request body for generating vector embeddings from text.

Contains input text(s) to be converted into high-dimensional vector representations. Supports both single string input and batch processing with arrays of strings for efficient embedding generation.

The request body defines text input and processing options for generating semantic embeddings that capture meaning and relationships between texts.

Request body for generating vector embeddings from text. Contains input text(s) to be converted into high-dimensional vector representations. Supports both single string input and batch processing with arrays of strings for efficient embedding generation. The request body defines text input and processing options for generating semantic embeddings that capture meaning and relationships between texts.

inputstring or list of stringsRequired

The text(s) to generate embeddings for. Can be a single string or an array of strings. Each text should be a meaningful unit of text (sentence, paragraph, etc).

modelstringOptionalDefaults to default

Model identifier or alias.

Response

Successfully generated embeddings

datalist of objects

List of embeddings, one for each input text

object"list"Defaults to list

The type of object returned

model"private-gpt"Defaults to private-gpt

The model used to generate embeddings

Errors

401

Unauthorized Error

422

Unprocessable Entity Error

$	curl -X POST /v1/embeddings \
>	-H "Content-Type: application/json" \
>	-d '{
>	"input": "The quick brown fox jumps over the lazy dog"
>	}'

1	{
2	"data": [
3	{
4	"index": 0,
5	"embedding": [
6	0.123,
7	-0.456,
8	0.789,
9	0.234,
10	-0.567,
11	0.891,
12	-0.123,
13	0.445,
14	-0.678,
15	0.901
16	],
17	"object": "embedding"
18	}
19	],
20	"object": "list",
21	"model": "private-gpt"
22	}