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The /v1/embeddings endpoint converts text into numerical vector representations (embeddings). You can pass a single string or a batch of strings in one request. The resulting vectors can be stored in a vector database and used for semantic search, document clustering, classification, or as input to a retrieval-augmented generation (RAG) pipeline. This endpoint is compatible with the OpenAI embeddings format.

​
Request parameters

​
model
string
required
The embedding model to use. Use the list models endpoint to find available embedding model IDs.
​
input
string | string[]
required
The text to embed. Pass a single string or an array of strings to embed multiple inputs in one request. Token limits vary by model.
​
encoding_format
string
default:"float"
The format in which to return the embedding vectors. Use "float" for an array of floating-point numbers, or "base64" for a Base64-encoded string.

​
Response fields

​
object
string
Always "list".
​
data
array
An array of embedding objects, one for each input string. Objects are returned in the same order as the input.
​
model
string
The model used to generate the embeddings.
​
usage
object
Token usage for the request.

​
Example

​
Request

curl https://tokmodel.com/v1/embeddings \
  --request POST \
  --header "Authorization: Bearer YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "openai/text-embedding-3-small",
    "input": ["TokModel is a unified LLM gateway.", "It supports 30+ AI providers."],
    "encoding_format": "float"
  }'

​
Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0412, -0.0188, "..."]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.0071, -0.0033, 0.0299, -0.0144, "..."]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 16,
    "total_tokens": 16
  }
}