---
title: "Create an embedding"
method: POST
path: "/genai/{connection_id}/embedding"
tags: ["genai", "embedding"]
---

# Create an embedding

`POST /genai/{connection_id}/embedding`

## Request

- Base URL: `http://api.unified.to`
- URL: `http://api.unified.to/genai/{connection_id}/embedding`
- Auth: API key in header `authorization`

## Path parameters

- `connection_id` string, required

## Query parameters

- `fields` string[]
- `raw` string

## Request body

- GenaiEmbedding
  - `model_id` string
  - `content` GenaiEmbeddingContent[]
    - `text` string, required
  - `enconding_format` 'FLOAT' | 'UINT8' | 'INT8' | 'BINARY' | 'UBINARY' | 'BASE64'
  - `type` string: 'SEARCH_DOC' | 'SEARCH_QUERY' | 'CLUSTERING' | 'CLASSIFICATION';
  - `dimension` number: 256, 512, 1024, and 1536
  - `max_tokens` number
  - `embeddings` string: JSON string based off of the format
  - `tokens_used` number
  - `raw` unknown
  - `id` string

## Response `200`

Successful

- GenaiEmbedding
  - `model_id` string
  - `content` GenaiEmbeddingContent[]
    - `text` string, required
  - `enconding_format` 'FLOAT' | 'UINT8' | 'INT8' | 'BINARY' | 'UBINARY' | 'BASE64'
  - `type` string: 'SEARCH_DOC' | 'SEARCH_QUERY' | 'CLUSTERING' | 'CLASSIFICATION';
  - `dimension` number: 256, 512, 1024, and 1536
  - `max_tokens` number
  - `embeddings` string: JSON string based off of the format
  - `tokens_used` number
  - `raw` unknown
  - `id` string

---

[API](https://skmtc.dev/unified/apis/unified-to-api-2.md) · [All operations](https://skmtc.dev/unified/apis/unified-to-api-2/llms.txt) · [OpenAPI document](https://skmtc.dev/unified/apis/unified-to-api-2/revisions/7a38bdd7f089?raw)