chore: import upstream snapshot with attribution

docs-website/docs/pipeline-components/generators/transformerschatgenerator.mdx

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+---
+title: "TransformersChatGenerator"
+id: transformerschatgenerator
+slug: "/transformerschatgenerator"
+description: "Provides an interface for chat completion using a Hugging Face model that runs locally."
+---
+
+# TransformersChatGenerator
+
+Provides an interface for chat completion using a Hugging Face model that runs locally.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                                         |
+| **Mandatory init variables**           | None                                                                                                          |
+| **Mandatory run variables**            | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects representing the chat or a plain string |
+| **Output variables**                   | `replies`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects generated by the LLM |
+| **API reference**                      | [Transformers](/reference/integrations-transformers)                                                                |
+| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/transformers |
+| **Package name** | `transformers-haystack` |
+
+</div>
+
+## Overview
+
+Keep in mind that if LLMs run locally, you may need a powerful machine to run them. This depends strongly on the model you select and its parameter count.
+
+If a string is passed to `messages`, it is converted into a list containing a single `ChatMessage` with the `user` role.
+
+Authentication with a Hugging Face API token is only required to access private or gated models. You can pass the token at initialization with `token`, or set the `HF_API_TOKEN` or `HF_TOKEN` environment variable:
+
+```python
+generator = TransformersChatGenerator(
+    token=Secret.from_token("<your-api-key>"),
+)
+```
+
+### 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
+
+Install the `transformers-haystack` package to use the `TransformersChatGenerator`:
+
+```shell
+pip install transformers-haystack
+```
+
+### On its own
+
+```python
+from haystack_integrations.components.generators.transformers import (
+    TransformersChatGenerator,
+)
+from haystack.dataclasses import ChatMessage
+
+generator = TransformersChatGenerator(model="Qwen/Qwen3-0.6B")
+messages = [ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
+print(generator.run(messages))
+```
+
+### In a Pipeline
+
+```python
+from haystack import Pipeline
+from haystack.components.builders.prompt_builder import ChatPromptBuilder
+from haystack_integrations.components.generators.transformers import (
+    TransformersChatGenerator,
+)
+from haystack.dataclasses import ChatMessage
+from haystack.utils import Secret
+
+prompt_builder = ChatPromptBuilder()
+llm = TransformersChatGenerator(
+    model="Qwen/Qwen3-0.6B",
+    token=Secret.from_env_var("HF_API_TOKEN"),
+)
+
+pipe = Pipeline()
+pipe.add_component("prompt_builder", prompt_builder)
+pipe.add_component("llm", llm)
+pipe.connect("prompt_builder.prompt", "llm.messages")
+location = "Berlin"
+messages = [
+    ChatMessage.from_system(
+        "Always respond in German even if some input data is in other languages.",
+    ),
+    ChatMessage.from_user("Tell me about {{location}}"),
+]
+pipe.run(
+    data={
+        "prompt_builder": {
+            "template_variables": {"location": location},
+            "template": messages,
+        },
+    },
+)
+```