---
title: "Create embeddings"
method: POST
path: "/v1/embeddings"
tags: ["Embeddings"]
---

# Create embeddings

`POST /v1/embeddings`

Creates an embedding vector for the provided input text. OpenAI-compatible endpoint.

## Request body

- EmbeddingRequest
  - `model` string, required — Model identifier to use for embedding
  - `input` string, required — Text to embed
  - `user` string — Optional user identifier

## Response `200`

Embedding created successfully

- EmbeddingResponse
  - `object` 'list'
  - `data` object[]
    - `object` 'embedding'
    - `index` integer
    - `embedding` number[] — Embedding vector
  - `model` string
  - `usage` object
    - `prompt_tokens` integer
    - `total_tokens` integer

## Other responses

- `400` — Invalid request
- `503` — Model not ready

---

[API](https://skmtc.net/tenstorrent/apis/tt-media-server-api-c-drogon.md) · [All operations](https://skmtc.net/tenstorrent/apis/tt-media-server-api-c-drogon/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/tenstorrent/tt-media-server-api-c-drogon/revisions/f1176db3fd15/schema)