# Source: https://www.promptfoo.dev/docs/providers/cohere/

# Cohere

The `cohere` provider is an interface to Cohere AI's [chat inference API](https://docs.cohere.com/reference/chat), with models such as Command R that are optimized for RAG and tool usage.

## Setup

First, set the `COHERE_API_KEY` environment variable with your Cohere API key.

Next, edit the promptfoo configuration file to point to the Cohere provider.

- `cohere:<model name>` - uses the specified Cohere model (e.g., `command`, `command-light`).

The following models are confirmed supported. For an up-to-date list of supported models, see [Cohere Models](https://docs.cohere.com/docs/models).

- command-light
- command-light-nightly
- command
- command-nightly
- command-r
- command-r-plus

Here's an example configuration:

```yaml
providers:
  - id: cohere:command
    config:
      temperature: 0.5
      max_tokens: 256
      prompt_truncation: 'AUTO'
      connectors:
        - id: web-search
```

## Control over prompting

By default, a regular string prompt will be automatically wrapped in the appropriate chat format and sent to the Cohere API via the `message` field:

```yaml
prompts:
  - 'Write a tweet about {{topic}}'

providers:
  - cohere:command

tests:
  - vars:
      topic: bananas
```

If desired, your prompt can reference a YAML or JSON file that has a more complex set of API parameters. For example:

```yaml
prompts:
  - file://prompt1.yaml

providers:
  - cohere:command

tests:
  - vars:
      question: What year was he born?
  - vars:
      question: What did he like eating for breakfast
```

And in `prompt1.yaml`:

```yaml
chat_history:
  - role: USER
    message: 'Who discovered gravity?'
  - role: CHATBOT
    message: 'Isaac Newton'
    message: '{{question}}'
  connectors:
    - id: web-search
```

## Embedding Configuration

Cohere provides embedding capabilities that can be used for various natural language processing tasks, including similarity comparisons. To use Cohere's embedding model in your evaluations, you can configure it as follows:

1. In your `promptfooconfig.yaml` file, add the embedding configuration under the `defaultTest` section:

```yaml
defaultTest:
  options:
    provider:
      embedding:
        id: cohere:embedding:embed-english-v3.0
```

This configuration sets the default embedding provider for all tests that require embeddings (such as similarity assertions) to use Cohere's `embed-english-v3.0` model.

2. You can also specify the embedding provider for individual assertions:

```yaml
assert:
  - type: similar
    value: Some reference text
    provider:
      embedding:
        id: cohere:embedding:embed-english-v3.0
```

3. Additional configuration options can be passed to the embedding provider:

```yaml
defaultTest:
  options:
    provider:
      embedding:
        id: cohere:embedding:embed-english-v3.0
        config:
          apiKey: your_api_key_here  # If not set via environment variable
          truncate: NONE  # Options: NONE, START, END
```

## Displaying searches and documents

When the Cohere API is called, the provider can optionally include the search queries and documents in the output. This is controlled by the `showSearchQueries` and `showDocuments` config parameters. If true, the content will be appending to the output.

## Configuration

### Cohere parameters

| Parameter | Description |
| --- | --- |
| `apiKey` | Your Cohere API key if not using an environment variable. |
| `chatHistory` | An array of chat history objects with role, message, and optionally user_name and conversation_id. |
| `connectors` | An array of connector objects for integrating with external systems. |
| `documents` | An array of document objects for providing reference material to the model. |
| `frequency_penalty` | Penalizes new tokens based on their frequency in the text so far. |
| `k` | Controls the diversity of the output via top-k sampling. |
| `max_tokens` | The maximum length of the generated text. |
| `modelName` | The model name to use for the chat completion. |
| `p` | Controls the diversity of the output via nucleus (top-p) sampling. |
| `preamble_override` | A string to override the default preamble used by the model. |
| `presence_penalty` | Penalizes new tokens based on their presence in the text so far. |
| `prompt_truncation` | Controls how prompts are truncated ('AUTO' or 'OFF'). |
| `search_queries_only` | If true, only search queries are processed. |
| `temperature` | Controls the randomness of the output. |

### Special parameters

| Parameter | Description |
| --- | --- |
| `showSearchQueries` | If true, includes the search queries used in the output. |
| `showDocuments` | If true, includes the documents used in the output. |