fix: Make the HuggingFaceLocalChatGenerator compatible with the new ChatMessage; serialize chat_template (#8663)

* message conversion function

* hfapi w tools

* right test file + hf_hub version

* release note

* fix for new chatmessage; serialize chat_template

* feedback

haystack/components/generators/chat/hugging_face_local.py

@@ -25,6 +25,7 @@ with LazyImport(message="Run 'pip install \"transformers[torch]\"'") as torch_an
     from haystack.utils.hf import (  # pylint: disable=ungrouped-imports
         HFTokenStreamingHandler,
         StopWordsCriteria,
+        convert_message_to_hf_format,
         deserialize_hf_model_kwargs,
         serialize_hf_model_kwargs,
     )
@@ -201,6 +202,7 @@ class HuggingFaceLocalChatGenerator:
             generation_kwargs=self.generation_kwargs,
             streaming_callback=callback_name,
             token=self.token.to_dict() if self.token else None,
+            chat_template=self.chat_template,
         )
 
         huggingface_pipeline_kwargs = serialization_dict["init_parameters"]["huggingface_pipeline_kwargs"]
@@ -270,9 +272,11 @@ class HuggingFaceLocalChatGenerator:
             # streamer parameter hooks into HF streaming, HFTokenStreamingHandler is an adapter to our streaming
             generation_kwargs["streamer"] = HFTokenStreamingHandler(tokenizer, self.streaming_callback, stop_words)
 
+        hf_messages = [convert_message_to_hf_format(message) for message in messages]
+
         # Prepare the prompt for the model
         prepared_prompt = tokenizer.apply_chat_template(
-            messages, tokenize=False, chat_template=self.chat_template, add_generation_prompt=True
+            hf_messages, tokenize=False, chat_template=self.chat_template, add_generation_prompt=True
         )
 
         # Avoid some unnecessary warnings in the generation pipeline call

releasenotes/notes/hflocalchat-fixes-ddf71e8c4c73e566.yaml

@@ -0,0 +1,7 @@
+---
+fixes:
+  - |
+    Make the HuggingFaceLocalChatGenerator compatible with the new ChatMessage format, by converting the messages to
+    the format expected by Hugging Face.
+
+    Serialize the chat_template parameter.

test/components/generators/chat/test_hugging_face_local.py

@@ -135,6 +135,7 @@ class TestHuggingFaceLocalChatGenerator:
             generation_kwargs={"n": 5},
             stop_words=["stop", "words"],
             streaming_callback=lambda x: x,
+            chat_template="irrelevant",
         )
 
         # Call the to_dict method
@@ -146,6 +147,7 @@ class TestHuggingFaceLocalChatGenerator:
         assert init_params["huggingface_pipeline_kwargs"]["model"] == "NousResearch/Llama-2-7b-chat-hf"
         assert "token" not in init_params["huggingface_pipeline_kwargs"]
         assert init_params["generation_kwargs"] == {"max_new_tokens": 512, "n": 5, "stop_sequences": ["stop", "words"]}
+        assert init_params["chat_template"] == "irrelevant"
 
     def test_from_dict(self, model_info_mock):
         generator = HuggingFaceLocalChatGenerator(
@@ -153,6 +155,7 @@ class TestHuggingFaceLocalChatGenerator:
             generation_kwargs={"n": 5},
             stop_words=["stop", "words"],
             streaming_callback=streaming_callback_handler,
+            chat_template="irrelevant",
         )
         # Call the to_dict method
         result = generator.to_dict()
@@ -162,6 +165,7 @@ class TestHuggingFaceLocalChatGenerator:
         assert generator_2.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
         assert generator_2.generation_kwargs == {"max_new_tokens": 512, "n": 5, "stop_sequences": ["stop", "words"]}
         assert generator_2.streaming_callback is streaming_callback_handler
+        assert generator_2.chat_template == "irrelevant"
 
     @patch("haystack.components.generators.chat.hugging_face_local.pipeline")
     def test_warm_up(self, pipeline_mock, monkeypatch):
@@ -218,3 +222,36 @@ class TestHuggingFaceLocalChatGenerator:
         chat_message = results["replies"][0]
         assert chat_message.is_from(ChatRole.ASSISTANT)
         assert chat_message.text == "Berlin is cool"
+
+    @patch("haystack.components.generators.chat.hugging_face_local.convert_message_to_hf_format")
+    def test_messages_conversion_is_called(self, mock_convert, model_info_mock):
+        generator = HuggingFaceLocalChatGenerator(model="fake-model")
+
+        messages = [ChatMessage.from_user("Hello"), ChatMessage.from_assistant("Hi there")]
+
+        with patch.object(generator, "pipeline") as mock_pipeline:
+            mock_pipeline.tokenizer.apply_chat_template.return_value = "test prompt"
+            mock_pipeline.return_value = [{"generated_text": "test response"}]
+
+            generator.warm_up()
+            generator.run(messages)
+
+        assert mock_convert.call_count == 2
+        mock_convert.assert_any_call(messages[0])
+        mock_convert.assert_any_call(messages[1])
+
+    @pytest.mark.integration
+    @pytest.mark.flaky(reruns=3, reruns_delay=10)
+    def test_live_run(self):
+        messages = [ChatMessage.from_user("Please create a summary about the following topic: Climate change")]
+
+        llm = HuggingFaceLocalChatGenerator(
+            model="Qwen/Qwen2.5-0.5B-Instruct", generation_kwargs={"max_new_tokens": 50}
+        )
+        llm.warm_up()
+
+        result = llm.run(messages)
+
+        assert "replies" in result
+        assert isinstance(result["replies"][0], ChatMessage)
+        assert "climate change" in result["replies"][0].text.lower()