chore: import upstream snapshot with attribution

docs-website/reference_versioned_docs/version-2.26/integrations-api/llama_cpp.md

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+---
+title: "Llama.cpp"
+id: integrations-llama-cpp
+description: "Llama.cpp integration for Haystack"
+slug: "/integrations-llama-cpp"
+---
+
+
+## haystack_integrations.components.generators.llama_cpp.chat.chat_generator
+
+### LlamaCppChatGenerator
+
+Provides an interface to generate text using LLM via llama.cpp.
+
+[llama.cpp](https://github.com/ggml-org/llama.cpp) is a project written in C/C++ for efficient inference of LLMs.
+It employs the quantized GGUF format, suitable for running these models on standard machines (even without GPUs).
+Supports both text-only and multimodal (text + image) models like LLaVA.
+
+Usage example:
+
+```python
+from haystack_integrations.components.generators.llama_cpp import LlamaCppChatGenerator
+user_message = [ChatMessage.from_user("Who is the best American actor?")]
+generator = LlamaCppGenerator(model="zephyr-7b-beta.Q4_0.gguf", n_ctx=2048, n_batch=512)
+
+print(generator.run(user_message, generation_kwargs={"max_tokens": 128}))
+# {"replies": [ChatMessage(content="John Cusack", role=<ChatRole.ASSISTANT: "assistant">, name=None, meta={...})}
+```
+
+Usage example with multimodal (image + text):
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+
+# Create an image from file path or base64
+image_content = ImageContent.from_file_path("path/to/your/image.jpg")
+
+# Create a multimodal message with both text and image
+messages = [ChatMessage.from_user(content_parts=["What's in this image?", image_content])]
+
+# Initialize with multimodal support
+generator = LlamaCppChatGenerator(
+    model="llava-v1.5-7b-q4_0.gguf",
+    chat_handler_name="Llava15ChatHandler",  # Use llava-1-5 handler
+    model_clip_path="mmproj-model-f16.gguf",  # CLIP model
+    n_ctx=4096  # Larger context for image processing
+)
+
+result = generator.run(messages)
+print(result)
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str,
+    n_ctx: int | None = 0,
+    n_batch: int | None = 512,
+    model_kwargs: dict[str, Any] | None = None,
+    generation_kwargs: dict[str, Any] | None = None,
+    *,
+    tools: ToolsType | None = None,
+    streaming_callback: StreamingCallbackT | None = None,
+    chat_handler_name: str | None = None,
+    model_clip_path: str | None = None
+) -> None
+```
+
+Initialize LlamaCppChatGenerator.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – The path of a quantized model for text generation, for example, "zephyr-7b-beta.Q4_0.gguf".
+  If the model path is also specified in the `model_kwargs`, this parameter will be ignored.
+- **n_ctx** (<code>int | None</code>) – The number of tokens in the context. When set to 0, the context will be taken from the model.
+- **n_batch** (<code>int | None</code>) – Prompt processing maximum batch size.
+- **model_kwargs** (<code>dict\[str, Any\] | None</code>) – Dictionary containing keyword arguments used to initialize the LLM for text generation.
+  These keyword arguments provide fine-grained control over the model loading.
+  In case of duplication, these kwargs override `model`, `n_ctx`, and `n_batch` init parameters.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__init__).
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – A dictionary containing keyword arguments to customize text generation.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_chat_completion).
+- **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.
+- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is received from the stream.
+- **chat_handler_name** (<code>str | None</code>) – Name of the chat handler for multimodal models.
+  Common options include: "Llava16ChatHandler", "MoondreamChatHandler", "Qwen25VLChatHandler".
+  For other handlers, check
+  [llama-cpp-python documentation](https://llama-cpp-python.readthedocs.io/en/latest/#multi-modal-models).
+- **model_clip_path** (<code>str | None</code>) – Path to the CLIP model for vision processing (e.g., "mmproj.bin").
+  Required when chat_handler_name is provided for multimodal models.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Load and initialize the llama.cpp model.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> LlamaCppChatGenerator
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>LlamaCppChatGenerator</code> – Deserialized component.
+
+#### run
+
+```python
+run(
+    messages: list[ChatMessage] | str,
+    generation_kwargs: dict[str, Any] | None = None,
+    *,
+    tools: ToolsType | None = None,
+    streaming_callback: StreamingCallbackT | None = None
+) -> dict[str, list[ChatMessage]]
+```
+
+Run the text generation model on the given list of ChatMessages.
+
+**Parameters:**
+
+- **messages** (<code>list\[ChatMessage\] | str</code>) – A list of ChatMessage instances representing the input messages.
+  If a string is provided, it is converted to a list containing a ChatMessage with user role.
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – A dictionary containing keyword arguments to customize text generation.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_chat_completion).
+- **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. If set, it will override the `tools` parameter set during
+  component initialization.
+- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is received from the stream.
+  If set, it will override the `streaming_callback` parameter set during component initialization.
+
+**Returns:**
+
+- <code>dict\[str, list\[ChatMessage\]\]</code> – A dictionary with the following keys:
+- `replies`: The responses from the model
+
+#### run_async
+
+```python
+run_async(
+    messages: list[ChatMessage] | str,
+    generation_kwargs: dict[str, Any] | None = None,
+    *,
+    tools: ToolsType | None = None,
+    streaming_callback: StreamingCallbackT | None = None
+) -> dict[str, list[ChatMessage]]
+```
+
+Async version of run. Runs the text generation model on the given list of ChatMessages.
+
+Uses a thread pool to avoid blocking the event loop, since llama-cpp-python provides
+only synchronous inference.
+
+**Parameters:**
+
+- **messages** (<code>list\[ChatMessage\] | str</code>) – A list of ChatMessage instances representing the input messages.
+  If a string is provided, it is converted to a list containing a ChatMessage with user role.
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – A dictionary containing keyword arguments to customize text generation.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_chat_completion).
+- **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. If set, it will override the `tools` parameter set during
+  component initialization.
+- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is received from the stream.
+  If set, it will override the `streaming_callback` parameter set during component initialization.
+
+**Returns:**
+
+- <code>dict\[str, list\[ChatMessage\]\]</code> – A dictionary with the following keys:
+- `replies`: The responses from the model
+
+## haystack_integrations.components.generators.llama_cpp.generator
+
+### LlamaCppGenerator
+
+Provides an interface to generate text using LLM via llama.cpp.
+
+[llama.cpp](https://github.com/ggml-org/llama.cpp) is a project written in C/C++ for efficient inference of LLMs.
+It employs the quantized GGUF format, suitable for running these models on standard machines (even without GPUs).
+
+Usage example:
+
+```python
+from haystack_integrations.components.generators.llama_cpp import LlamaCppGenerator
+generator = LlamaCppGenerator(model="zephyr-7b-beta.Q4_0.gguf", n_ctx=2048, n_batch=512)
+
+print(generator.run("Who is the best American actor?", generation_kwargs={"max_tokens": 128}))
+# {'replies': ['John Cusack'], 'meta': [{"object": "text_completion", ...}]}
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str,
+    n_ctx: int | None = 0,
+    n_batch: int | None = 512,
+    model_kwargs: dict[str, Any] | None = None,
+    generation_kwargs: dict[str, Any] | None = None,
+) -> None
+```
+
+Initialize LlamaCppGenerator.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – The path of a quantized model for text generation, for example, "zephyr-7b-beta.Q4_0.gguf".
+  If the model path is also specified in the `model_kwargs`, this parameter will be ignored.
+- **n_ctx** (<code>int | None</code>) – The number of tokens in the context. When set to 0, the context will be taken from the model.
+- **n_batch** (<code>int | None</code>) – Prompt processing maximum batch size.
+- **model_kwargs** (<code>dict\[str, Any\] | None</code>) – Dictionary containing keyword arguments used to initialize the LLM for text generation.
+  These keyword arguments provide fine-grained control over the model loading.
+  In case of duplication, these kwargs override `model`, `n_ctx`, and `n_batch` init parameters.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__init__).
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – A dictionary containing keyword arguments to customize text generation.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_completion).
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Load and initialize the llama.cpp model.
+
+#### run
+
+```python
+run(
+    prompt: str, generation_kwargs: dict[str, Any] | None = None
+) -> dict[str, list[str] | list[dict[str, Any]]]
+```
+
+Run the text generation model on the given prompt.
+
+**Parameters:**
+
+- **prompt** (<code>str</code>) – the prompt to be sent to the generative model.
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – A dictionary containing keyword arguments to customize text generation.
+  For more information on the available kwargs, see
+  [llama.cpp documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_completion).
+
+**Returns:**
+
+- <code>dict\[str, list\[str\] | list\[dict\[str, Any\]\]\]</code> – A dictionary with the following keys:
+- `replies`: the list of replies generated by the model.
+- `meta`: metadata about the request.