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

# Create embedding

`POST /embeddings`

Generate vector embeddings for one or more text inputs. Returns numerical arrays representing semantic meaning, useful for search, classification, and retrieval.

## Request body

- EmbeddingsRequest
  - `model` union, required — The name of the embedding model to use.<br> <br> [See all of Together AI's embedding models](https://docs.together.ai/docs/serverless-models#embedding-models)
    - 'WhereIsAI/UAE-Large-V1' | 'BAAI/bge-large-en-v1.5' | 'BAAI/bge-base-en-v1.5' | 'togethercomputer/m2-bert-80M-8k-retrieval'
    - string
  - `input` union, required
    - string — A string providing the text for the model to embed.
    - string[]

## Response `200`

200

- EmbeddingsResponse
  - `object` 'list', required — The object type, which is always `list`.
  - `model` string, required
  - `data` object[], required
    - `object` 'embedding', required — The object type, which is always `embedding`.
    - `embedding` number[], required
    - `index` integer, required

## Other responses

- `400` — BadRequest
- `401` — Unauthorized
- `404` — NotFound
- `429` — RateLimit
- `503` — Overloaded
- `504` — Timeout

---

[API](https://skmtc.net/together/apis/together-apis.md) · [All operations](https://skmtc.net/together/apis/together-apis/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/together/together-apis/revisions/468edbdc879c/schema)