docs: consistently use returns over return

integrations/amazon_bedrock/src/haystack_integrations/common/amazon_bedrock/utils.py

@@ -34,7 +34,7 @@ def get_aws_session(
     :param kwargs: The kwargs passed down to the service client. Supported kwargs depend on the model chosen.
         See https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html.
     :raises AWSConfigurationError: If the provided AWS credentials are invalid.
-    :return: The created AWS session.
+    :returns: The created AWS session.
     """
     try:
         return boto3.Session(
@@ -54,7 +54,7 @@ def aws_configured(**kwargs) -> bool:
     """
     Checks whether AWS configuration is provided.
     :param kwargs: The kwargs passed down to the generator.
-    :return: True if AWS configuration is provided, False otherwise.
+    :returns: True if AWS configuration is provided, False otherwise.
     """
     aws_config_provided = any(key in kwargs for key in AWS_CONFIGURATION_KEYS)
     return aws_config_provided

integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/document_embedder.py

@@ -235,7 +235,7 @@ def run(self, documents: List[Document]):
     def to_dict(self) -> Dict[str, Any]:
         """
         Serialize this component to a dictionary.
-        :return: The serialized component as a dictionary.
+        :returns: The serialized component as a dictionary.
         """
         return default_to_dict(
             self,

integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/text_embedder.py

@@ -154,7 +154,7 @@ def run(self, text: str):
     def to_dict(self) -> Dict[str, Any]:
         """
         Serialize this component to a dictionary.
-        :return: The serialized component as a dictionary.
+        :returns: The serialized component as a dictionary.
         """
         return default_to_dict(
             self,
@@ -172,7 +172,7 @@ def from_dict(cls, data: Dict[str, Any]) -> "AmazonBedrockTextEmbedder":
         """
         Deserialize this component from a dictionary.
         :param data: The dictionary representation of this component.
-        :return: The deserialized component instance.
+        :returns: The deserialized component instance.
         """
         deserialize_secrets_inplace(
             data["init_parameters"],

integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/adapters.py

@@ -25,15 +25,15 @@ def prepare_body(self, prompt: str, **inference_kwargs) -> Dict[str, Any]:
 
         :param prompt: The prompt to be sent to the model.
         :param inference_kwargs: Additional keyword arguments passed to the handler.
-        :return: A dictionary containing the body for the request.
+        :returns: A dictionary containing the body for the request.
         """
 
     def get_responses(self, response_body: Dict[str, Any]) -> List[str]:
         """
         Extracts the responses from the Amazon Bedrock response.
 
         :param response_body: The response body from the Amazon Bedrock request.
-        :return: A list of responses.
+        :returns: A list of responses.
         """
         completions = self._extract_completions_from_response(response_body)
         responses = [completion.lstrip() for completion in completions]
@@ -45,7 +45,7 @@ def get_stream_responses(self, stream, stream_handler: TokenStreamingHandler) ->
 
         :param stream: The streaming response from the Amazon Bedrock request.
         :param stream_handler: The handler for the streaming response.
-        :return: A list of string responses.
+        :returns: A list of string responses.
         """
         tokens: List[str] = []
         for event in stream:
@@ -64,7 +64,7 @@ def _get_params(self, inference_kwargs: Dict[str, Any], default_params: Dict[str
         Includes param if it's in kwargs or its default is not None (i.e. it is actually defined).
         :param inference_kwargs: The inference kwargs.
         :param default_params: The default params.
-        :return: A dictionary containing the merged params.
+        :returns: A dictionary containing the merged params.
         """
         kwargs = self.model_kwargs.copy()
         kwargs.update(inference_kwargs)
@@ -80,7 +80,7 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         Extracts the responses from the Amazon Bedrock response.
 
         :param response_body: The response body from the Amazon Bedrock request.
-        :return: A list of string responses.
+        :returns: A list of string responses.
         """
 
     @abstractmethod
@@ -89,7 +89,7 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: A string token.
+        :returns: A string token.
         """
 
 
@@ -121,7 +121,7 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         Extracts the responses from the Amazon Bedrock response.
 
         :param response_body: The response body from the Amazon Bedrock request.
-        :return: A list of string responses.
+        :returns: A list of string responses.
         """
         return [response_body["completion"]]
 
@@ -130,7 +130,7 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: A string token.
+        :returns: A string token.
         """
         return chunk.get("completion", "")
 
@@ -146,7 +146,7 @@ def prepare_body(self, prompt: str, **inference_kwargs) -> Dict[str, Any]:
 
         :param prompt: The prompt to be sent to the model.
         :param inference_kwargs: Additional keyword arguments passed to the handler.
-        :return: A dictionary containing the body for the request.
+        :returns: A dictionary containing the body for the request.
         """
         default_params = {
             "max_tokens": self.max_length,
@@ -170,7 +170,7 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         Extracts the responses from the Cohere Command model response.
 
         :param response_body: The response body from the Amazon Bedrock request.
-        :return: A list of string responses.
+        :returns: A list of string responses.
         """
         responses = [generation["text"] for generation in response_body["generations"]]
         return responses
@@ -180,7 +180,7 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: A string token.
+        :returns: A string token.
         """
         return chunk.get("text", "")
 
@@ -226,7 +226,7 @@ def prepare_body(self, prompt: str, **inference_kwargs) -> Dict[str, Any]:
 
         :param prompt: The prompt to be sent to the model.
         :param inference_kwargs: Additional keyword arguments passed to the handler.
-        :return: A dictionary containing the body for the request.
+        :returns: A dictionary containing the body for the request.
         """
         default_params = {
             "maxTokenCount": self.max_length,
@@ -244,7 +244,7 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         Extracts the responses from the Titan model response.
 
         :param response_body: The response body for Titan model response.
-        :return: A list of string responses.
+        :returns: A list of string responses.
         """
         responses = [result["outputText"] for result in response_body["results"]]
         return responses
@@ -254,7 +254,7 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: A string token.
+        :returns: A string token.
         """
         return chunk.get("outputText", "")
 
@@ -270,7 +270,7 @@ def prepare_body(self, prompt: str, **inference_kwargs) -> Dict[str, Any]:
 
         :param prompt: The prompt to be sent to the model.
         :param inference_kwargs: Additional keyword arguments passed to the handler.
-        :return: A dictionary containing the body for the request.
+        :returns: A dictionary containing the body for the request.
         """
         default_params = {
             "max_gen_len": self.max_length,
@@ -287,7 +287,7 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         Extracts the responses from the Llama2 model response.
 
         :param response_body: The response body from the Llama2 model request.
-        :return: A list of string responses.
+        :returns: A list of string responses.
         """
         return [response_body["generation"]]
 
@@ -296,6 +296,6 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: A string token.
+        :returns: A string token.
         """
         return chunk.get("generation", "")

integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/adapters.py

@@ -34,15 +34,15 @@ def prepare_body(self, messages: List[ChatMessage], **inference_kwargs) -> Dict[
 
         :param messages: The chat messages to package into the request.
         :param inference_kwargs: Additional inference kwargs to use.
-        :return: The prepared body.
+        :returns: The prepared body.
         """
 
     def get_responses(self, response_body: Dict[str, Any]) -> List[ChatMessage]:
         """
         Extracts the responses from the Amazon Bedrock response.
 
         :param response_body: The response body.
-        :return: The extracted responses.
+        :returns: The extracted responses.
         """
         return self._extract_messages_from_response(self.response_body_message_key(), response_body)
 
@@ -85,7 +85,7 @@ def _get_params(self, inference_kwargs: Dict[str, Any], default_params: Dict[str
 
         :param inference_kwargs: The inference kwargs to merge.
         :param default_params: The default params to start with.
-        :return: The merged params.
+        :returns: The merged params.
         """
         # Start with a copy of default_params
         kwargs = default_params.copy()
@@ -100,7 +100,7 @@ def _ensure_token_limit(self, prompt: str) -> str:
         """
         Ensures that the prompt is within the token limit for the model.
         :param prompt: The prompt to check.
-        :return: The resized prompt.
+        :returns: The resized prompt.
         """
         resize_info = self.check_prompt(prompt)
         if resize_info["prompt_length"] != resize_info["new_prompt_length"]:
@@ -121,7 +121,7 @@ def check_prompt(self, prompt: str) -> Dict[str, Any]:
         Checks the prompt length and resizes it if necessary. If the prompt is too long, it will be truncated.
 
         :param prompt: The prompt to check.
-        :return: A dictionary containing the resized prompt and additional information.
+        :returns: A dictionary containing the resized prompt and additional information.
         """
 
     def _extract_messages_from_response(self, message_tag: str, response_body: Dict[str, Any]) -> List[ChatMessage]:
@@ -130,7 +130,7 @@ def _extract_messages_from_response(self, message_tag: str, response_body: Dict[
 
         :param message_tag: The key for the message in the response body.
         :param response_body: The response body.
-        :return: The extracted ChatMessage list.
+        :returns: The extracted ChatMessage list.
         """
         metadata = {k: v for (k, v) in response_body.items() if k != message_tag}
         return [ChatMessage.from_assistant(response_body[message_tag], meta=metadata)]
@@ -141,7 +141,7 @@ def response_body_message_key(self) -> str:
         Returns the key for the message in the response body.
         Subclasses should override this method to return the correct message key - where the response is located.
 
-        :return: The key for the message in the response body.
+        :returns: The key for the message in the response body.
         """
 
     @abstractmethod
@@ -150,7 +150,7 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: The extracted token.
+        :returns: The extracted token.
         """
 
 
@@ -192,7 +192,7 @@ def prepare_body(self, messages: List[ChatMessage], **inference_kwargs) -> Dict[
 
         :param messages: The chat messages to package into the request.
         :param inference_kwargs: Additional inference kwargs to use.
-        :return: The prepared body.
+        :returns: The prepared body.
         """
         default_params = {
             "max_tokens_to_sample": self.generation_kwargs.get("max_tokens_to_sample") or 512,
@@ -212,7 +212,7 @@ def prepare_chat_messages(self, messages: List[ChatMessage]) -> str:
         Prepares the chat messages for the Anthropic Claude request.
 
         :param messages: The chat messages to prepare.
-        :return: The prepared chat messages as a string.
+        :returns: The prepared chat messages as a string.
         """
         conversation = []
         for index, message in enumerate(messages):
@@ -241,15 +241,15 @@ def check_prompt(self, prompt: str) -> Dict[str, Any]:
         Checks the prompt length and resizes it if necessary. If the prompt is too long, it will be truncated.
 
         :param prompt: The prompt to check.
-        :return: A dictionary containing the resized prompt and additional information.
+        :returns: A dictionary containing the resized prompt and additional information.
         """
         return self.prompt_handler(prompt)
 
     def response_body_message_key(self) -> str:
         """
         Returns the key for the message in the response body for Anthropic Claude i.e. "completion".
 
-        :return: The key for the message in the response body.
+        :returns: The key for the message in the response body.
         """
         return "completion"
 
@@ -258,7 +258,7 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: The extracted token.
+        :returns: The extracted token.
         """
         return chunk.get("completion", "")
 
@@ -340,7 +340,7 @@ def prepare_chat_messages(self, messages: List[ChatMessage]) -> str:
         Prepares the chat messages for the Meta Llama 2 request.
 
         :param messages: The chat messages to prepare.
-        :return: The prepared chat messages as a string ready for the model.
+        :returns: The prepared chat messages as a string ready for the model.
         """
         prepared_prompt: str = self.prompt_handler.tokenizer.apply_chat_template(
             conversation=messages, tokenize=False, chat_template=self.chat_template
@@ -352,7 +352,7 @@ def check_prompt(self, prompt: str) -> Dict[str, Any]:
         Checks the prompt length and resizes it if necessary. If the prompt is too long, it will be truncated.
 
         :param prompt: The prompt to check.
-        :return: A dictionary containing the resized prompt and additional information.
+        :returns: A dictionary containing the resized prompt and additional information.
 
         """
         return self.prompt_handler(prompt)
@@ -361,7 +361,7 @@ def response_body_message_key(self) -> str:
         """
         Returns the key for the message in the response body for Meta Llama 2 i.e. "generation".
 
-        :return: The key for the message in the response body.
+        :returns: The key for the message in the response body.
         """
         return "generation"
 
@@ -370,6 +370,6 @@ def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
         Extracts the token from a streaming chunk.
 
         :param chunk: The streaming chunk.
-        :return: The extracted token.
+        :returns: The extracted token.
         """
         return chunk.get("generation", "")

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

@@ -140,7 +140,7 @@ def invoke(self, *args, **kwargs):
 
         :param args: The positional arguments passed to the generator.
         :param kwargs: The keyword arguments passed to the generator.
-        :return: List of `ChatMessage` generated by LLM.
+        :returns: List of `ChatMessage` generated by LLM.
         """
 
         kwargs = kwargs.copy()
@@ -183,7 +183,7 @@ def run(self, messages: List[ChatMessage], generation_kwargs: Optional[Dict[str,
 
         :param messages: The messages to generate a response to.
         :param generation_kwargs: Additional generation keyword arguments passed to the model.
-        :return: A dictionary with the following keys:
+        :returns: A dictionary with the following keys:
             - `replies`: The generated List of `ChatMessage` objects.
         """
         return {"replies": self.invoke(messages=messages, **(generation_kwargs or {}))}
@@ -194,7 +194,7 @@ def get_model_adapter(cls, model: str) -> Optional[Type[BedrockModelChatAdapter]
         Returns the model adapter for the given model.
 
         :param model: The model to get the adapter for.
-        :return: The model adapter for the given model, or None if the model is not supported.
+        :returns: The model adapter for the given model, or None if the model is not supported.
         """
         for pattern, adapter in cls.SUPPORTED_MODEL_PATTERNS.items():
             if re.fullmatch(pattern, model):