---
title: "CohereChatGenerator"
id: coherechatgenerator
slug: "/coherechatgenerator"
description: "CohereChatGenerator enables chat completions using Cohere's large language models (LLMs)."
---

# CohereChatGenerator

CohereChatGenerator enables chat completions using Cohere's large language models (LLMs).

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)  |
| **Mandatory init variables** | "api_key": The Cohere API key. Can be set with `COHERE_API_KEY` or `CO_API_KEY` env var. |
| **Mandatory run variables** | “messages” A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects |
| **Output variables** | "replies": A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects  <br /> <br />”meta”: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Cohere](/reference/integrations-cohere) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere |

This integration supports Cohere `chat` models such as `command`,`command-r` and `comman-r-plus`. Check out the most recent full list in [Cohere documentation](https://docs.cohere.com/reference/chat).

## Overview

`CohereChatGenerator` needs a Cohere API key to work. You can set this key in:

- The `api_key` init parameter using [Secret API](../../concepts/secret-management.mdx)
- The `COHERE_API_KEY` environment variable (recommended)

Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the `Co.chat` method directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the Cohere API, refer to the [Cohere documentation](https://docs.cohere.com/reference/chat).

Finally, the component needs a list of `ChatMessage` objects to operate. `ChatMessage` is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

You need to install `cohere-haystack` package to use the  `CohereChatGenerator`:

```shell
pip install cohere-haystack
```

#### On its own

```python
from haystack_integrations.components.generators.cohere import CohereChatGenerator
from haystack.dataclasses import ChatMessage

generator = CohereChatGenerator()
message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
print(generator.run([message]))
```

#### In a Pipeline

You can also use `CohereChatGenerator` to use cohere chat models in your pipeline.

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.cohere import CohereChatGenerator
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", CohereChatGenerator())
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system(
    "You are an assistant giving out valuable information to language learners.",
)
messages = [
    system_message,
    ChatMessage.from_user("What's the official language of {{ country }}?"),
]

res = pipe.run(
    data={
        "prompt_builder": {
            "template_variables": {"country": country},
            "template": messages,
        },
    },
)
print(res)
```
