chore: import upstream snapshot with attribution

docs-website/versioned_docs/version-2.18/pipeline-components/generators/coherechatgenerator.mdx

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+---
+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)
+```