chore: import upstream snapshot with attribution

docs-website/reference_versioned_docs/version-2.30/integrations-api/meta_llama.md

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+---
+title: "Meta Llama API"
+id: integrations-meta-llama
+description: "Meta Llama API integration for Haystack"
+slug: "/integrations-meta-llama"
+---
+
+
+## haystack_integrations.components.generators.meta_llama.chat.chat_generator
+
+### MetaLlamaChatGenerator
+
+Bases: <code>OpenAIChatGenerator</code>
+
+Enables text generation using Llama generative models.
+For supported models, see [Llama API Docs](https://llama.developer.meta.com/docs/).
+
+Users can pass any text generation parameters valid for the Llama Chat Completion API
+directly to this component via the `generation_kwargs` parameter in `__init__` or the `generation_kwargs`
+parameter in `run` method.
+
+Key Features and Compatibility:
+
+- **Primary Compatibility**: Designed to work seamlessly with the Llama API Chat Completion endpoint.
+- **Streaming Support**: Supports streaming responses from the Llama API Chat Completion endpoint.
+- **Customizability**: Supports parameters supported by the Llama API Chat Completion endpoint.
+- **Response Format**: Currently only supports json_schema response format.
+
+This component uses the ChatMessage format for structuring both input and output,
+ensuring coherent and contextually relevant responses in chat-based text generation scenarios.
+Details on the ChatMessage format can be found in the
+[Haystack docs](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage)
+
+For more details on the parameters supported by the Llama API, refer to the
+[Llama API Docs](https://llama.developer.meta.com/docs/).
+
+Usage example:
+
+```python
+from haystack_integrations.components.generators.llama import LlamaChatGenerator
+from haystack.dataclasses import ChatMessage
+
+messages = [ChatMessage.from_user("What's Natural Language Processing?")]
+
+client = LlamaChatGenerator()
+response = client.run(messages)
+print(response)
+```
+
+#### SUPPORTED_MODELS
+
+```python
+SUPPORTED_MODELS: list[str] = [
+    "Llama-4-Maverick-17B-128E-Instruct-FP8",
+    "Llama-4-Scout-17B-16E-Instruct-FP8",
+    "Llama-3.3-70B-Instruct",
+    "Llama-3.3-8B-Instruct",
+]
+
+```
+
+A non-exhaustive list of chat models supported by this component.
+See https://llama.developer.meta.com/docs/models for the full list.
+
+#### __init__
+
+```python
+__init__(
+    *,
+    api_key: Secret = Secret.from_env_var("LLAMA_API_KEY"),
+    model: str = "Llama-4-Scout-17B-16E-Instruct-FP8",
+    streaming_callback: StreamingCallbackT | None = None,
+    api_base_url: str | None = "https://api.llama.com/compat/v1/",
+    generation_kwargs: dict[str, Any] | None = None,
+    timeout: float | None = None,
+    max_retries: int | None = None,
+    tools: ToolsType | None = None
+)
+```
+
+Creates an instance of LlamaChatGenerator. Unless specified otherwise in the `model`, this is for Llama's
+`Llama-4-Scout-17B-16E-Instruct-FP8` model.
+
+**Parameters:**
+
+- **api_key** (<code>Secret</code>) – The Llama API key.
+- **model** (<code>str</code>) – The name of the Llama chat completion model to use.
+- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is received from the stream.
+  The callback function accepts StreamingChunk as an argument.
+- **api_base_url** (<code>str | None</code>) – The Llama API Base url.
+  For more details, see LlamaAPI [docs](https://llama.developer.meta.com/docs/features/compatibility/).
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – Other parameters to use for the model. These parameters are all sent directly to
+  the Llama API endpoint. See [Llama API docs](https://llama.developer.meta.com/docs/features/compatibility/)
+  for more details.
+  Some of the supported parameters:
+- `max_tokens`: The maximum number of tokens the output text can have.
+- `temperature`: What sampling temperature to use. Higher values mean the model will take more risks.
+  Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer.
+- `top_p`: An alternative to sampling with temperature, called nucleus sampling, where the model
+  considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens
+  comprising the top 10% probability mass are considered.
+- `stream`: Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent
+  events as they become available, with the stream terminated by a data: [DONE] message.
+- `safe_prompt`: Whether to inject a safety prompt before all conversations.
+- `random_seed`: The seed to use for random sampling.
+- `response_format`: A JSON schema or a Pydantic model that enforces the structure of the model's response.
+  If provided, the output will always be validated against this
+  format (unless the model returns a tool call).
+  For details, see the [OpenAI Structured Outputs documentation](https://platform.openai.com/docs/guides/structured-outputs).
+  For structured outputs with streaming, the `response_format` must be a JSON
+  schema and not a Pydantic model.
+- **timeout** (<code>float | None</code>) – Timeout for Llama API client calls.
+- **max_retries** (<code>int | None</code>) – Maximum number of retries to attempt for failed requests.
+- **tools** (<code>ToolsType | None</code>) – A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+  Each tool should have a unique name.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialize this component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – The serialized component as a dictionary.