> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mixroute.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Embed English v3.0

> Cohere text embedding model optimized for English retrieval.

Cohere text embedding model optimized for English retrieval.

## Model Information

| Field | Description |
| - | - |
| `model` | embed-english-v3.0 |
| `context` | 512 tokens |
| `default dimensions` | 1024 |
| `endpoint` | POST /v1/embeddings |

Context and output sizes describe the model specification. Effective request limits, availability, and billing depend on the MixRoute route.

## Capabilities and Usage

* Set input\_type=search\_document for indexed documents and search\_query for user queries. classification and clustering select other task types.
* This endpoint example embeds text. Do not assume native Cohere image/PDF input objects work in the OpenAI-compatible input field.
* Use the same model and vector dimensions for documents and queries. Output vectors are returned in data\[].embedding.

## Request Example

Set `MIXROUTE_API_KEY` before making the request.

```bash theme={null}
curl --request POST "https://api.mixroute.ai/v1/embeddings" \
  --header "Authorization: Bearer $MIXROUTE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "embed-english-v3.0",
  "input": [
    "An API reference for developers."
  ],
  "input_type": "search_document",
  "encoding_format": "float"
}'
```

## Request Fields

| Field | Description |
| - | - |
| `model` | Required. Use the exact model ID above. |
| `input` | Required text string or array of strings. |
| `input_type` | Required for Cohere embeddings; select the task type. |
| `encoding_format` | Use float for a JSON vector array. |

Complete request format: [embeddings](/api-reference/endpoint/embeddings).


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