diff --git a/.gitignore b/.gitignore
index 8634bc259..c3a7cf863 100644
--- a/.gitignore
+++ b/.gitignore
@@ -134,3 +134,4 @@ dmypy.json
 
 # Docs generation artifacts
 _readme_*.md
+.idea
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/common/amazon_bedrock/utils.py b/integrations/amazon_bedrock/src/haystack_integrations/common/amazon_bedrock/utils.py
index e1683e3b3..3148818c1 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/common/amazon_bedrock/utils.py
+++ b/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
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/document_embedder.py b/integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/document_embedder.py
index 5a82821e3..8cf98cd45 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/document_embedder.py
+++ b/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,
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/text_embedder.py b/integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/text_embedder.py
index 8804702a0..ed6768737 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/text_embedder.py
+++ b/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"],
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/adapters.py b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/adapters.py
index a1704ef13..f842f0ef5 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/adapters.py
+++ b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/adapters.py
@@ -25,7 +25,7 @@ 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.
         """
 
     def get_responses(self, response_body: Dict[str, Any]) -> List[str]:
@@ -33,7 +33,7 @@ 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", "")
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/adapters.py b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/adapters.py
index d5dc100f9..196a55743 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/adapters.py
+++ b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/adapters.py
@@ -34,7 +34,7 @@ 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.
         """
 
     def get_responses(self, response_body: Dict[str, Any]) -> List[ChatMessage]:
@@ -42,7 +42,7 @@ 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):
@@ -241,7 +241,7 @@ 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)
 
@@ -249,7 +249,7 @@ 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", "")
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/chat_generator.py b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/chat_generator.py
index 3b5a8f6cc..bea6924f6 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/chat_generator.py
+++ b/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):
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/generator.py b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/generator.py
index 706d29c98..f6af48ae1 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/generator.py
+++ b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/generator.py
@@ -142,7 +142,7 @@ def _ensure_token_limit(self, prompt: Union[str, List[Dict[str, str]]]) -> Union
         the initialization of the component.
 
         :param prompt: The prompt to be sent to the model.
-        :return: The resized prompt.
+        :returns: The resized prompt.
         """
         # the prompt for this model will be of the type str
         if isinstance(prompt, List):
@@ -171,7 +171,7 @@ def invoke(self, *args, **kwargs):
 
         :param args: Additional positional arguments passed to the generator.
         :param kwargs: Additional keyword arguments passed to the generator.
-        :return: A list of generated responses (strings).
+        :returns: A list of generated responses (strings).
         """
         kwargs = kwargs.copy()
         prompt: str = kwargs.pop("prompt", None)
@@ -225,7 +225,7 @@ def run(self, prompt: str, generation_kwargs: Optional[Dict[str, Any]] = None):
 
         :param prompt: The prompt to generate a response for.
         :param generation_kwargs: Additional keyword arguments passed to the generator.
-        :return: A dictionary with the following keys:
+        :returns: A dictionary with the following keys:
             - `replies`: A list of generated responses (strings).
         """
         return {"replies": self.invoke(prompt=prompt, **(generation_kwargs or {}))}
@@ -236,7 +236,7 @@ def get_model_adapter(cls, model: str) -> Optional[Type[BedrockModelAdapter]]:
         Gets the model adapter for the given model.
 
         :param model: The model name.
-        :return: The model adapter class, or None if no adapter is found.
+        :returns: The model adapter class, or None if no adapter is found.
         """
         for pattern, adapter in cls.SUPPORTED_MODEL_PATTERNS.items():
             if re.fullmatch(pattern, model):
diff --git a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/handlers.py b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/handlers.py
index b7b555ec0..ddc276264 100644
--- a/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/handlers.py
+++ b/integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/handlers.py
@@ -34,7 +34,7 @@ def __call__(self, prompt: str, **kwargs) -> Dict[str, Union[str, int]]:
 
         :param prompt: the prompt to be sent to the model.
         :param kwargs: Additional keyword arguments passed to the handler.
-        :return: A dictionary containing the resized prompt and additional information.
+        :returns: A dictionary containing the resized prompt and additional information.
         """
         resized_prompt = prompt
         prompt_length = 0
@@ -75,7 +75,7 @@ def __call__(self, token_received: str, **kwargs) -> str:
 
         :param token_received: The token received from the stream.
         :param kwargs: Additional keyword arguments passed to the handler.
-        :return: The token to be sent to the stream.
+        :returns: The token to be sent to the stream.
         """
         pass
 
@@ -87,7 +87,7 @@ def __call__(self, token_received, **kwargs) -> str:
 
         :param token_received: The token received from the stream.
         :param kwargs: Additional keyword arguments passed to the handler.
-        :return: The token to be sent to the stream.
+        :returns: The token to be sent to the stream.
         """
         print(token_received, flush=True, end="")  # noqa: T201
         return token_received
diff --git a/integrations/amazon_sagemaker/src/haystack_integrations/components/generators/amazon_sagemaker/sagemaker.py b/integrations/amazon_sagemaker/src/haystack_integrations/components/generators/amazon_sagemaker/sagemaker.py
index 106698558..c171ccdf6 100644
--- a/integrations/amazon_sagemaker/src/haystack_integrations/components/generators/amazon_sagemaker/sagemaker.py
+++ b/integrations/amazon_sagemaker/src/haystack_integrations/components/generators/amazon_sagemaker/sagemaker.py
@@ -179,7 +179,7 @@ def _get_aws_session(
         :param aws_profile_name: AWS profile name.
 
         :raises AWSConfigurationError: If the provided AWS credentials are invalid.
-        :return: The created AWS session.
+        :returns: The created AWS session.
         """
         try:
             return boto3.Session(
@@ -202,7 +202,7 @@ def run(self, prompt: str, generation_kwargs: Optional[Dict[str, Any]] = None):
         :param generation_kwargs: Additional keyword arguments for text generation. These parameters will
         potentially override the parameters passed in the `__init__` method.
 
-        :return: A dictionary with the following keys:
+        :returns: A dictionary with the following keys:
             - `replies`: A list of strings containing the generated responses
             - `meta`: A list of dictionaries containing the metadata for each response.
         """
diff --git a/integrations/astra/src/haystack_integrations/components/retrievers/astra/retriever.py b/integrations/astra/src/haystack_integrations/components/retrievers/astra/retriever.py
index 2b9ac7d28..80e436e0a 100644
--- a/integrations/astra/src/haystack_integrations/components/retrievers/astra/retriever.py
+++ b/integrations/astra/src/haystack_integrations/components/retrievers/astra/retriever.py
@@ -52,7 +52,7 @@ def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] =
         :param filters: filters to narrow down the search space.
         :param top_k: the maximum number of documents to retrieve.
         :returns: a dictionary with the following keys:
-            - documents: A list of documents retrieved from the AstraDocumentStore.
+            - `documents`: A list of documents retrieved from the AstraDocumentStore.
         """
 
         if not top_k:
diff --git a/integrations/chroma/src/haystack_integrations/components/retrievers/chroma/retriever.py b/integrations/chroma/src/haystack_integrations/components/retrievers/chroma/retriever.py
index 10f97f01f..7138eff88 100644
--- a/integrations/chroma/src/haystack_integrations/components/retrievers/chroma/retriever.py
+++ b/integrations/chroma/src/haystack_integrations/components/retrievers/chroma/retriever.py
@@ -64,8 +64,8 @@ def run(
         :param query: The input data for the retriever. In this case, a plain-text query.
         :param top_k: The maximum number of documents to retrieve.
             If not specified, the default value from the constructor is used.
-        :return: A dictionary with the following keys:
-            - "documents": List of documents returned by the search engine.
+        :returns: A dictionary with the following keys:
+            - `documents`: List of documents returned by the search engine.
 
         :raises ValueError: If the specified document store is not found or is not a MemoryDocumentStore instance.
         """
@@ -119,8 +119,8 @@ def run(
         Run the retriever on the given input data.
 
         :param query_embedding: the query embeddings.
-        :return: a dictionary with the following keys:
-            - "documents": List of documents returned by the search engine.
+        :returns: a dictionary with the following keys:
+            - `documents`: List of documents returned by the search engine.
         """
         top_k = top_k or self.top_k
 
diff --git a/integrations/chroma/src/haystack_integrations/document_stores/chroma/document_store.py b/integrations/chroma/src/haystack_integrations/document_stores/chroma/document_store.py
index 4201de23b..0db9f832a 100644
--- a/integrations/chroma/src/haystack_integrations/document_stores/chroma/document_store.py
+++ b/integrations/chroma/src/haystack_integrations/document_stores/chroma/document_store.py
@@ -206,7 +206,7 @@ def search(self, queries: List[str], top_k: int) -> List[List[Document]]:
 
         :param queries: the list of queries to search for.
         :param top_k: top_k documents to return for each query.
-        :return: matching documents for each query.
+        :returns: matching documents for each query.
         """
         results = self._collection.query(
             query_texts=queries, n_results=top_k, include=["embeddings", "documents", "metadatas", "distances"]
diff --git a/integrations/cohere/src/haystack_integrations/components/embedders/cohere/text_embedder.py b/integrations/cohere/src/haystack_integrations/components/embedders/cohere/text_embedder.py
index 305743126..87b4f8834 100644
--- a/integrations/cohere/src/haystack_integrations/components/embedders/cohere/text_embedder.py
+++ b/integrations/cohere/src/haystack_integrations/components/embedders/cohere/text_embedder.py
@@ -112,8 +112,8 @@ def run(self, text: str):
 
         :param text: the text to embed.
         :returns: A dictionary with the following keys:
-            - "embedding": the embedding of the text.
-            - "meta": metadata about the request.
+            - `embedding`: the embedding of the text.
+            - `meta`: metadata about the request.
         :raises TypeError: If the input is not a string.
         """
         if not isinstance(text, str):
diff --git a/integrations/cohere/src/haystack_integrations/components/generators/cohere/chat/chat_generator.py b/integrations/cohere/src/haystack_integrations/components/generators/cohere/chat/chat_generator.py
index dcee45d10..980441009 100644
--- a/integrations/cohere/src/haystack_integrations/components/generators/cohere/chat/chat_generator.py
+++ b/integrations/cohere/src/haystack_integrations/components/generators/cohere/chat/chat_generator.py
@@ -149,7 +149,7 @@ def run(self, messages: List[ChatMessage], generation_kwargs: Optional[Dict[str,
             For more details on the parameters supported by the Cohere API, refer to the
             Cohere [documentation](https://docs.cohere.com/reference/chat).
         :returns: A dictionary with the following keys:
-            - "replies": a list of `ChatMessage` instances representing the generated responses.
+            - `replies`: a list of `ChatMessage` instances representing the generated responses.
         """
         # update generation kwargs by merging with the generation kwargs passed to the run method
         generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
@@ -186,7 +186,7 @@ def _build_chunk(self, chunk) -> StreamingChunk:
         Converts the response from the Cohere API to a StreamingChunk.
         :param chunk: The chunk returned by the OpenAI API.
         :param choice: The choice returned by the OpenAI API.
-        :return: The StreamingChunk.
+        :returns: The StreamingChunk.
         """
         chat_message = StreamingChunk(content=chunk.text, meta={"index": chunk.index, "event_type": chunk.event_type})
         return chat_message
@@ -195,7 +195,7 @@ def _build_message(self, cohere_response):
         """
         Converts the non-streaming response from the Cohere API to a ChatMessage.
         :param cohere_response: The completion returned by the Cohere API.
-        :return: The ChatMessage.
+        :returns: The ChatMessage.
         """
         content = cohere_response.text
         message = ChatMessage.from_assistant(content=content)
diff --git a/integrations/cohere/src/haystack_integrations/components/generators/cohere/generator.py b/integrations/cohere/src/haystack_integrations/components/generators/cohere/generator.py
index 6a25727dd..2c9a97478 100644
--- a/integrations/cohere/src/haystack_integrations/components/generators/cohere/generator.py
+++ b/integrations/cohere/src/haystack_integrations/components/generators/cohere/generator.py
@@ -127,8 +127,8 @@ def run(self, prompt: str):
 
         :param prompt: the prompt to be sent to the generative model.
         :returns: A dictionary with the following keys:
-            - "replies": the list of replies generated by the model.
-            - "meta": metadata about the request.
+            - `replies`: the list of replies generated by the model.
+            - `meta`: metadata about the request.
         """
         response = self.client.generate(
             model=self.model, prompt=prompt, stream=self.streaming_callback is not None, **self.model_parameters
@@ -153,7 +153,7 @@ def _build_chunk(self, chunk) -> StreamingChunk:
         """
         Converts the response from the Cohere API to a StreamingChunk.
         :param chunk: The chunk returned by the OpenAI API.
-        :return: The StreamingChunk.
+        :returns: The StreamingChunk.
         """
         streaming_chunk = StreamingChunk(content=chunk.text, meta={"index": chunk.index})
         return streaming_chunk
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
index f08ff1adc..4af8e1bbe 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
@@ -144,7 +144,7 @@ def run(self, documents: List[Document]):
         Embeds a list of Documents.
 
         :param documents: List of Documents to embed.
-        :return: A dictionary with the following keys:
+        :returns: A dictionary with the following keys:
             - `documents`: List of Documents with each Document's `embedding` field set to the computed embeddings.
         """
         if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py
index ffba6a902..13a89d1ce 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py
@@ -101,7 +101,7 @@ def run(self, text: str):
         Embeds text using the Fastembed model.
 
         :param text: A string to embed.
-        :return: A dictionary with the following keys:
+        :returns: A dictionary with the following keys:
             - `embedding`: A list of floats representing the embedding of the input text.
         :raises TypeError: If the input is not a string.
         :raises RuntimeError: If the embedding model has not been loaded.
diff --git a/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/image_generator.py b/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/image_generator.py
index 422e1cfe6..ae8c4892f 100644
--- a/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/image_generator.py
+++ b/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/image_generator.py
@@ -85,7 +85,7 @@ def run(self, prompt: str, negative_prompt: Optional[str] = None):
         :param negative_prompt: A description of what you want to omit in
                 the generated images.
         :returns: A dictionary with the following keys:
-            - images: A list of ByteStream objects, each containing an image.
+            - `images`: A list of ByteStream objects, each containing an image.
         """
         negative_prompt = negative_prompt or self._kwargs.get("negative_prompt")
         res = self._model.generate_images(prompt=prompt, negative_prompt=negative_prompt, **self._kwargs)
diff --git a/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/question_answering.py b/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/question_answering.py
index 79c343b02..32cde86ef 100644
--- a/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/question_answering.py
+++ b/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/question_answering.py
@@ -89,7 +89,7 @@ def run(self, image: ByteStream, question: str):
         :param image: The image to ask the question about.
         :param question: The question to ask.
         :returns: A dictionary with the following keys:
-            - answers: A list of answers to the question.
+            - `answers`: A list of answers to the question.
         """
         answers = self._model.ask_question(image=Image(image.data), question=question, **self._kwargs)
         return {"answers": answers}
diff --git a/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/text_generator.py b/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/text_generator.py
index e16954f8f..a00678f19 100644
--- a/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/text_generator.py
+++ b/integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/text_generator.py
@@ -115,10 +115,10 @@ def run(self, prompt: str):
 
         :param prompt: The prompt to use for text generation.
         :returns:  A dictionary with the following keys:
-            - answers: A list of generated answers.
-            - safety_attributes: A dictionary with the [safety scores](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/responsible-ai#safety_attribute_descriptions)
+            - `answers`: A list of generated answers.
+            - `safety_attributes`: A dictionary with the [safety scores](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/responsible-ai#safety_attribute_descriptions)
               of each answer.
-            - citations: A list of citations for each answer.
+            - `citations`: A list of citations for each answer.
         """
         res = self._model.predict(prompt=prompt, **self._kwargs)
 
diff --git a/integrations/llama_cpp/src/haystack_integrations/components/generators/llama_cpp/generator.py b/integrations/llama_cpp/src/haystack_integrations/components/generators/llama_cpp/generator.py
index c5d37c1a8..1c504b6f3 100644
--- a/integrations/llama_cpp/src/haystack_integrations/components/generators/llama_cpp/generator.py
+++ b/integrations/llama_cpp/src/haystack_integrations/components/generators/llama_cpp/generator.py
@@ -79,8 +79,8 @@ def run(self, prompt: str, generation_kwargs: Optional[Dict[str, Any]] = None):
             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: A dictionary with the following keys:
-            - "replies": the list of replies generated by the model.
-            - "meta": metadata about the request.
+            - `replies`: the list of replies generated by the model.
+            - `meta`: metadata about the request.
         """
         if self.model is None:
             error_msg = "The model has not been loaded. Please call warm_up() before running."
diff --git a/integrations/opensearch/src/haystack_integrations/document_stores/opensearch/document_store.py b/integrations/opensearch/src/haystack_integrations/document_stores/opensearch/document_store.py
index e91347728..e9c88274c 100644
--- a/integrations/opensearch/src/haystack_integrations/document_stores/opensearch/document_store.py
+++ b/integrations/opensearch/src/haystack_integrations/document_stores/opensearch/document_store.py
@@ -257,7 +257,7 @@ def _bm25_retrieval(
         :param scale_score: If `True` scales the Document`s scores between 0 and 1, defaults to False
         :param all_terms_must_match: If `True` all terms in `query` must be present in the Document, defaults to False
         :raises ValueError: If `query` is an empty string
-        :return: List of Document that match `query`
+        :returns: List of Document that match `query`
         """
 
         if not query:
@@ -314,7 +314,7 @@ def _embedding_retrieval(
             Filters are applied during the approximate kNN search to ensure that top_k matching documents are returned.
         :param top_k: Maximum number of Documents to return, defaults to 10
         :raises ValueError: If `query_embedding` is an empty list
-        :return: List of Document that are most similar to `query_embedding`
+        :returns: List of Document that are most similar to `query_embedding`
         """
 
         if not query_embedding:
diff --git a/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/document_store.py b/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/document_store.py
index 4a47bf59e..645db88ae 100644
--- a/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/document_store.py
+++ b/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/document_store.py
@@ -420,7 +420,7 @@ def _handle_duplicate_documents(
                                     overwrite: Update any existing documents with the same ID when adding documents.
                                     fail: an error is raised if the document ID of the document being added already
                                     exists.
-        :return: A list of Haystack Document objects.
+        :returns: A list of Haystack Document objects.
         """
 
         index = index or self.index
@@ -443,7 +443,7 @@ def _drop_duplicate_documents(self, documents: List[Document], index: Optional[s
 
         :param documents: A list of Haystack Document objects.
         :param index: name of the index
-        :return: A list of Haystack Document objects.
+        :returns: A list of Haystack Document objects.
         """
         _hash_ids: Set = set()
         _documents: List[Document] = []
diff --git a/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/filters.py b/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/filters.py
index 77d800853..fc18a3ab2 100644
--- a/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/filters.py
+++ b/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/filters.py
@@ -186,7 +186,7 @@ def _squeeze_filter(self, payload_filter: models.Filter) -> models.Filter:
         Simplify given payload filter, if the nested structure might be unnested.
         That happens if there is a single clause in that filter.
         :param payload_filter:
-        :return:
+        :returns:
         """
         filter_parts = {
             "must": payload_filter.must,
diff --git a/integrations/unstructured/src/haystack_integrations/components/converters/unstructured/converter.py b/integrations/unstructured/src/haystack_integrations/components/converters/unstructured/converter.py
index 84865cee5..637c0840f 100644
--- a/integrations/unstructured/src/haystack_integrations/components/converters/unstructured/converter.py
+++ b/integrations/unstructured/src/haystack_integrations/components/converters/unstructured/converter.py
@@ -139,7 +139,7 @@ def run(
             (same metadata for all files).
 
         :returns: A dictionary with the following key:
-            - "documents": List of Haystack Documents.
+            - `documents`: List of Haystack Documents.
 
         :raises ValueError: If `meta` is a list and `paths` contains directories.
         """
diff --git a/nodes/text2speech/text2speech/utils/text_to_speech.py b/nodes/text2speech/text2speech/utils/text_to_speech.py
index 84c08e90f..e7d22cc11 100644
--- a/nodes/text2speech/text2speech/utils/text_to_speech.py
+++ b/nodes/text2speech/text2speech/utils/text_to_speech.py
@@ -104,7 +104,7 @@ def text_to_audio_file(
             leaves it untouched.
         :param audio_naming_function: A function mapping the input text into the audio file name.
                 By default, the audio file gets the name from the MD5 sum of the input text.
-        :return: The path to the generated file.
+        :returns: The path to the generated file.
         """
         if not os.path.exists(generated_audio_dir):
             os.mkdir(generated_audio_dir)
@@ -140,7 +140,7 @@ def text_to_audio_data(self, text: str, _models_output_key: str = "wav") -> np.a
 
         :param text: The text to convert into audio.
         :param _models_output_key: The key in the prediction dictionary that contains the audio data. Defaults to 'wav'.
-        :return: A numpy array representing the audio generated by the model.
+        :returns: A numpy array representing the audio generated by the model.
         """
         prediction = self.model(text)
         if not prediction: