introduce utility function (#939)

Co-authored-by: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>

Author: anakin87

integrations/llama_cpp/src/haystack_integrations/components/generators/llama_cpp/chat/chat_generator.py

@@ -9,6 +9,21 @@
 logger = logging.getLogger(__name__)
 
 
+def _convert_message_to_llamacpp_format(message: ChatMessage) -> Dict[str, str]:
+    """
+    Convert a message to the format expected by Llama.cpp.
+    :returns: A dictionary with the following keys:
+        - `role`
+        - `content`
+        - `name` (optional)
+    """
+    formatted_msg = {"role": message.role.value, "content": message.content}
+    if message.name:
+        formatted_msg["name"] = message.name
+
+    return formatted_msg
+
+
 @component
 class LlamaCppChatGenerator:
     """
@@ -96,7 +111,7 @@ def run(self, messages: List[ChatMessage], generation_kwargs: Optional[Dict[str,
             return {"replies": []}
 
         updated_generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
-        formatted_messages = [msg.to_openai_format() for msg in messages]
+        formatted_messages = [_convert_message_to_llamacpp_format(msg) for msg in messages]
 
         response = self.model.create_chat_completion(messages=formatted_messages, **updated_generation_kwargs)
         replies = [

integrations/llama_cpp/tests/test_chat_generator.py

@@ -10,7 +10,10 @@
 from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.dataclasses import ChatMessage, ChatRole
 from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack_integrations.components.generators.llama_cpp import LlamaCppChatGenerator
+from haystack_integrations.components.generators.llama_cpp.chat.chat_generator import (
+    LlamaCppChatGenerator,
+    _convert_message_to_llamacpp_format,
+)
 
 
 @pytest.fixture
@@ -29,6 +32,21 @@ def download_file(file_link, filename, capsys):
             print("\nModel file already exists.")
 
 
+def test_convert_message_to_llamacpp_format():
+    message = ChatMessage.from_system("You are good assistant")
+    assert _convert_message_to_llamacpp_format(message) == {"role": "system", "content": "You are good assistant"}
+
+    message = ChatMessage.from_user("I have a question")
+    assert _convert_message_to_llamacpp_format(message) == {"role": "user", "content": "I have a question"}
+
+    message = ChatMessage.from_function("Function call", "function_name")
+    assert _convert_message_to_llamacpp_format(message) == {
+        "role": "function",
+        "content": "Function call",
+        "name": "function_name",
+    }
+
+
 class TestLlamaCppChatGenerator:
     @pytest.fixture
     def generator(self, model_path, capsys):