feat: Have components auto run warm_up on first use instead of erroring when not pre-warmed up

haystack/components/agents/agent.py

@@ -554,7 +554,6 @@ def run(  # noqa: PLR0915
             - "messages": List of all messages exchanged during the agent's run.
             - "last_message": The last message exchanged during the agent's run.
             - Any additional keys defined in the `state_schema`.
-        :raises RuntimeError: If the Agent component wasn't warmed up before calling `run()`.
         :raises BreakpointException: If an agent breakpoint is triggered.
         """
         # We pop parent_snapshot from kwargs to avoid passing it into State.
@@ -722,7 +721,6 @@ async def run_async(
             - "messages": List of all messages exchanged during the agent's run.
             - "last_message": The last message exchanged during the agent's run.
             - Any additional keys defined in the `state_schema`.
-        :raises RuntimeError: If the Agent component wasn't warmed up before calling `run_async()`.
         :raises BreakpointException: If an agent breakpoint is triggered.
         """
         # We pop parent_snapshot from kwargs to avoid passing it into State.

haystack/components/audio/whisper_local.py

@@ -127,9 +127,7 @@ def run(self, sources: list[Union[str, Path, ByteStream]], whisper_params: Optio
                 for the transcription.
         """
         if self._model is None:
-            raise RuntimeError(
-                "The component LocalWhisperTranscriber was not warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if whisper_params is None:
             whisper_params = self.whisper_params
@@ -172,7 +170,9 @@ def _raw_transcribe(self, sources: list[Union[str, Path, ByteStream]], **kwargs)
             A dictionary mapping 'file_path' to 'transcription'.
         """
         if self._model is None:
-            raise RuntimeError("Model is not loaded, please run 'warm_up()' before calling 'run()'")
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self._model is not None
 
         return_segments = kwargs.pop("return_segments", False)
         transcriptions = {}

haystack/components/classifiers/zero_shot_document_classifier.py

@@ -203,10 +203,9 @@ def run(self, documents: list[Document], batch_size: int = 1):
         """
 
         if self.pipeline is None:
-            raise RuntimeError(
-                "The component TransformerZeroShotDocumentClassifier wasn't warmed up. "
-                "Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self.pipeline is not None
 
         if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
             raise TypeError(

haystack/components/embedders/image/sentence_transformers_doc_image_embedder.py

@@ -237,7 +237,9 @@ def run(self, documents: list[Document]) -> dict[str, list[Document]]:
                 "In case you want to embed a string, please use the SentenceTransformersTextEmbedder."
             )
         if self._embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self._embedding_backend is not None
 
         images_source_info = _extract_image_sources_info(
             documents=documents, file_path_meta_field=self.file_path_meta_field, root_path=self.root_path

haystack/components/embedders/sentence_transformers_document_embedder.py

@@ -22,7 +22,7 @@ class SentenceTransformersDocumentEmbedder:
     It stores the embeddings in the `embedding` metadata field of each document.
     You can also embed documents' metadata.
     Use this component in indexing pipelines to embed input documents
-    and send them to DocumentWriter to write a into a Document Store.
+    and send them to DocumentWriter to write into a Document Store.
 
     ### Usage example:
 
@@ -244,7 +244,9 @@ def run(self, documents: list[Document]):
                 "In case you want to embed a string, please use the SentenceTransformersTextEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self.embedding_backend is not None
 
         texts_to_embed = []
         for doc in documents:

haystack/components/embedders/sentence_transformers_sparse_document_embedder.py

@@ -216,7 +216,9 @@ def run(self, documents: list[Document]):
                 "In case you want to embed a list of strings, please use the SentenceTransformersSparseTextEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self.embedding_backend is not None
 
         texts_to_embed = []
         for doc in documents:

haystack/components/embedders/sentence_transformers_sparse_text_embedder.py

@@ -192,7 +192,9 @@ def run(self, text: str):
                 "SentenceTransformersSparseDocumentEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self.embedding_backend is not None
 
         text_to_embed = self.prefix + text + self.suffix
 

haystack/components/embedders/sentence_transformers_text_embedder.py

@@ -229,7 +229,9 @@ def run(self, text: str):
                 "In case you want to embed a list of Documents, please use the SentenceTransformersDocumentEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
+        # To make mypy happy even though this is set in warm_up()
+        assert self.embedding_backend is not None
 
         text_to_embed = self.prefix + text + self.suffix
         embedding = self.embedding_backend.embed(

haystack/components/evaluators/llm_evaluator.py

@@ -108,6 +108,17 @@ def __init__(  # pylint: disable=too-many-positional-arguments
             generation_kwargs = {"response_format": {"type": "json_object"}, "seed": 42}
             self._chat_generator = OpenAIChatGenerator(generation_kwargs=generation_kwargs)
 
+        self._is_warmed_up = False
+
+    def warm_up(self):
+        """
+        Warm up the component by warming up the underlying chat generator.
+        """
+        if not self._is_warmed_up:
+            if hasattr(self._chat_generator, "warm_up"):
+                self._chat_generator.warm_up()
+            self._is_warmed_up = True
+
     @staticmethod
     def validate_init_parameters(
         inputs: list[tuple[str, type[list]]], outputs: list[str], examples: list[dict[str, Any]]
@@ -181,6 +192,9 @@ def run(self, **inputs) -> dict[str, Any]:
             Only in the case that  `raise_on_failure` is set to True and the received inputs are not lists or have
             different lengths, or if the output is not a valid JSON or doesn't contain the expected keys.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         self.validate_input_parameters(dict(self.inputs), inputs)
 
         # inputs is a dictionary with keys being input names and values being a list of input values

haystack/components/evaluators/sas_evaluator.py

@@ -164,8 +164,7 @@ def run(self, ground_truth_answers: list[str], predicted_answers: list[str]) ->
             return {"score": 0.0, "individual_scores": [0.0]}
 
         if not self._similarity_model:
-            msg = "The model has not been initialized. Call warm_up() before running the evaluator."
-            raise RuntimeError(msg)
+            self.warm_up()
 
         if isinstance(self._similarity_model, CrossEncoder):
             # For Cross Encoders we create a list of pairs of predictions and labels

haystack/components/extractors/named_entity_extractor.py

@@ -192,8 +192,7 @@ def run(self, documents: list[Document], batch_size: int = 1) -> dict[str, Any]:
             If the backend fails to process a document.
         """
         if not self._warmed_up:
-            msg = "The component NamedEntityExtractor was not warmed up. Call warm_up() before running the component."
-            raise RuntimeError(msg)
+            self.warm_up()
 
         texts = [doc.content if doc.content is not None else "" for doc in documents]
         annotations = self._backend.annotate(texts, batch_size=batch_size)

haystack/components/generators/chat/fallback.py

@@ -47,7 +47,7 @@ class FallbackChatGenerator:
     - Any other exception
     """
 
-    def __init__(self, chat_generators: list[ChatGenerator]):
+    def __init__(self, chat_generators: list[ChatGenerator]) -> None:
         """
         Creates an instance of FallbackChatGenerator.
 
@@ -58,6 +58,7 @@ def __init__(self, chat_generators: list[ChatGenerator]):
             raise ValueError(msg)
 
         self.chat_generators = list(chat_generators)
+        self._is_warmed_up = False
 
     def to_dict(self) -> dict[str, Any]:
         """Serialize the component, including nested chat generators when they support serialization."""
@@ -87,10 +88,15 @@ def warm_up(self) -> None:
 
         This method calls warm_up() on each underlying generator that supports it.
         """
+        if self._is_warmed_up:
+            return
+
         for gen in self.chat_generators:
             if hasattr(gen, "warm_up") and callable(gen.warm_up):
                 gen.warm_up()
 
+        self._is_warmed_up = True
+
     def _run_single_sync(  # pylint: disable=too-many-positional-arguments
         self,
         gen: Any,
@@ -133,7 +139,7 @@ def run(
         generation_kwargs: Union[dict[str, Any], None] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Union[StreamingCallbackT, None] = None,
-    ) -> dict[str, Any]:
+    ) -> dict[str, Union[list[ChatMessage], dict[str, Any]]]:
         """
         Execute chat generators sequentially until one succeeds.
 
@@ -147,6 +153,9 @@ def run(
               total_attempts, failed_chat_generators, plus any metadata from the successful generator.
         :raises RuntimeError: If all chat generators fail.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         failed: list[str] = []
         last_error: Union[BaseException, None] = None
 
@@ -186,7 +195,7 @@ async def run_async(
         generation_kwargs: Union[dict[str, Any], None] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Union[StreamingCallbackT, None] = None,
-    ) -> dict[str, Any]:
+    ) -> dict[str, Union[list[ChatMessage], dict[str, Any]]]:
         """
         Asynchronously execute chat generators sequentially until one succeeds.
 
@@ -200,6 +209,9 @@ async def run_async(
               total_attempts, failed_chat_generators, plus any metadata from the successful generator.
         :raises RuntimeError: If all chat generators fail.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         failed: list[str] = []
         last_error: Union[BaseException, None] = None
 

haystack/components/generators/chat/hugging_face_api.py

@@ -436,7 +436,7 @@ def run(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Invoke the text generation inference based on the provided messages and generation parameters.
 
@@ -454,6 +454,8 @@ def run(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage objects.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
 
         # update generation kwargs by merging with the default ones
         generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
@@ -485,7 +487,7 @@ async def run_async(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Asynchronously invokes the text generation inference based on the provided messages and generation parameters.
 
@@ -506,6 +508,8 @@ async def run_async(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage objects.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
 
         # update generation kwargs by merging with the default ones
         generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}

haystack/components/generators/chat/hugging_face_local.py

@@ -361,6 +361,9 @@ def run(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if self.pipeline is None:
+            self.warm_up()
+
         prepared_inputs = self._prepare_inputs(
             messages=messages, generation_kwargs=generation_kwargs, streaming_callback=streaming_callback, tools=tools
         )
@@ -475,6 +478,9 @@ async def run_async(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if self.pipeline is None:
+            self.warm_up()
+
         prepared_inputs = self._prepare_inputs(
             messages=messages, generation_kwargs=generation_kwargs, streaming_callback=streaming_callback, tools=tools
         )
@@ -542,18 +548,17 @@ def _prepare_inputs(
         :param streaming_callback: An optional callable for handling streaming responses.
         :param tools: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
         :returns: A dictionary containing the prepared prompt, tokenizer, generation kwargs, and tools.
-        :raises RuntimeError: If the generation model has not been loaded.
         :raises ValueError: If both tools and streaming_callback are provided.
         """
-        if self.pipeline is None:
-            raise RuntimeError("The generation model has not been loaded. Please call warm_up() before running.")
-
         tools = tools or self.tools
         if tools and streaming_callback is not None:
             raise ValueError("Using tools and streaming at the same time is not supported. Please choose one.")
         flat_tools = flatten_tools_or_toolsets(tools)
         _check_duplicate_tool_names(flat_tools)
 
+        # To make mypy happy even though this is set in warm_up()
+        assert self.pipeline is not None
+
         tokenizer = self.pipeline.tokenizer
         # initialized text-generation/text2text-generation pipelines always have a non-None tokenizer
         assert tokenizer is not None

haystack/components/generators/chat/openai.py

@@ -286,7 +286,7 @@ def run(
         *,
         tools: Optional[ToolsType] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Invokes chat completion based on the provided messages and generation parameters.
 
@@ -310,6 +310,9 @@ def run(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         if len(messages) == 0:
             return {"replies": []}
 
@@ -358,7 +361,7 @@ async def run_async(
         *,
         tools: Optional[ToolsType] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Asynchronously invokes chat completion based on the provided messages and generation parameters.
 
@@ -385,6 +388,9 @@ async def run_async(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         # validate and select the streaming callback
         streaming_callback = select_streaming_callback(
             init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=True

haystack/components/generators/chat/openai_responses.py

@@ -297,7 +297,7 @@ def run(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[Union[ToolsType, list[dict]]] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Invokes response generation based on the provided messages and generation parameters.
 
@@ -326,6 +326,9 @@ def run(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         if len(messages) == 0:
             return {"replies": []}
 
@@ -364,7 +367,7 @@ async def run_async(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[Union[ToolsType, list[dict]]] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Asynchronously invokes response generation based on the provided messages and generation parameters.
 
@@ -395,6 +398,9 @@ async def run_async(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         # validate and select the streaming callback
         streaming_callback = select_streaming_callback(
             init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=True

haystack/components/generators/hugging_face_local.py

@@ -229,9 +229,7 @@ def run(
             - replies: A list of strings representing the generated replies.
         """
         if not self._warmed_up:
-            raise RuntimeError(
-                "The component HuggingFaceLocalGenerator was not warmed up. Please call warm_up() before running."
-            )
+            self.warm_up()
 
         # at this point, we know that the pipeline has been initialized
         assert self.pipeline is not None