docs: backticks everywhere

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:

integrations/chroma/src/haystack_integrations/components/retrievers/chroma/retriever.py

@@ -65,7 +65,7 @@ def run(
         :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.
+            - `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.
         """
@@ -120,7 +120,7 @@ def run(
 
         :param query_embedding: the query embeddings.
         :return: a dictionary with the following keys:
-            - "documents": List of documents returned by the search engine.
+            - `documents`: List of documents returned by the search engine.
         """
         top_k = top_k or self.top_k
 

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):

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 {})}

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

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)

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}

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)
 

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."

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.
         """