docs: Multiplexer - review and normalize docstrings for component.others (#7276)

* Multiplexer - review and normalize docstrings

* Update PR - Stefano

* simplify language

* re-generation

---------

Co-authored-by: Daria Fokina <daria.fokina@deepset.ai>

haystack/components/others/multiplexer.py

@@ -17,52 +17,121 @@ logger = logging.getLogger(__name__)
 @component(is_greedy=True)
 class Multiplexer:
     """
-    This component is used to distribute a single value to many components that may need it.
-    It can take such value from different sources (the user's input, or another component), so
-    it's only input is of Variadic type.
+    `Multiplexer` offers the ability to both receive data connections from multiple other
+    components and to distribute it to various other components, enhancing the functionality of complex data
+    processing pipelines.
 
-    The type of the expected input (and therefore of the output as well) must be given at init time.
+    `Multiplexer` is important for spreading outputs from a single source like a Large Language Model (LLM) across
+    different branches of a pipeline. It is especially valuable in error correction loops by rerouting data for
+    reevaluation if errors are detected. For instance, in an example pipeline below, `Multiplexer` helps create
+    a schema valid JSON object (given a person's data) with the help of an `OpenAIChatGenerator` and a `JsonSchemaValidator`.
+    In case the generated JSON object fails schema validation, `JsonSchemaValidator` starts a correction loop, sending
+    the data back through the `Multiplexer` to the `OpenAIChatGenerator` until it passes schema validation. If we didn't
+    have `Multiplexer`, we wouldn't be able to loop back the data to `OpenAIChatGenerator` for re-generation, as components
+    accept only one input connection for the declared run method parameters.
 
-    Example usage:
+    Usage example:
 
     ```python
-    >>> mp = Multiplexer(str)
-    >>> mp.run(value=["hello"])
-    {"value" : "hello"}
+    import json
+    from typing import List
 
-    >>> mp = Multiplexer(int)
-    >>> mp.run(value=[3])
-    {"value": 3}
+    from haystack import Pipeline
+    from haystack.components.converters import OutputAdapter
+    from haystack.components.generators.chat import OpenAIChatGenerator
+    from haystack.components.others import Multiplexer
+    from haystack.components.validators import JsonSchemaValidator
+    from haystack.dataclasses import ChatMessage
+
+    person_schema = {
+        "type": "object",
+        "properties": {
+            "first_name": {"type": "string", "pattern": "^[A-Z][a-z]+$"},
+            "last_name": {"type": "string", "pattern": "^[A-Z][a-z]+$"},
+            "nationality": {"type": "string", "enum": ["Italian", "Portuguese", "American"]},
+        },
+        "required": ["first_name", "last_name", "nationality"]
+    }
+
+    # Initialize a pipeline
+    pipe = Pipeline()
+
+    # Add components to the pipeline
+    pipe.add_component('mx', Multiplexer(List[ChatMessage]))
+    pipe.add_component('fc_llm', OpenAIChatGenerator(model="gpt-3.5-turbo-0125"))
+    pipe.add_component('validator', JsonSchemaValidator(json_schema=person_schema))
+    pipe.add_component('adapter', OutputAdapter("{{chat_message}}", List[ChatMessage])),
+    # And connect them
+    pipe.connect("adapter", "mx")
+    pipe.connect("mx", "fc_llm")
+    pipe.connect("fc_llm.replies", "validator.messages")
+    pipe.connect("validator.validation_error", "mx")
+
+    result = pipe.run(data={"fc_llm": {"generation_kwargs": {"response_format": {"type": "json_object"}}},
+                            "adapter": {"chat_message": [ChatMessage.from_user("Create json object from Peter Parker")]}})
+
+    print(json.loads(result["validator"]["validated"][0].content))
+
+
+    >> {'first_name': 'Peter', 'last_name': 'Parker', 'nationality': 'American', 'name': 'Spider-Man', 'occupation':
+    >> 'Superhero', 'age': 23, 'location': 'New York City'}
     ```
 
-    This component won't handle several inputs at the same time: it always only expects one.
-    If more than one input is received when run is invoked, it will raise a ValueError.
+    Note that `Multiplexer` is created with a single type parameter. This determines the
+    type of data that `Multiplexer` will receive from the upstream connected components and also the
+    type of data that `Multiplexer` will distribute to the downstream connected components. In the example
+    above, the `Multiplexer` is created with the type `List[ChatMessage]`. This means `Multiplexer` will receive
+    a list of `ChatMessage` objects from the upstream connected components and also distribute a list of `ChatMessage`
+    objects to the downstream connected components.
 
-    ```python
-    >>> mp = Multiplexer(int)
-    >>> mp.run([2, 4])
-    ValueError: Multiplexer expects only one input, but 2 were received.
-
-    >>> mp = Multiplexer(int)
-    >>> mp.run([2, None])
-    ValueError: Multiplexer expects only one input, but 2 were received.
-    ```
+    In the code example, `Multiplexer` receives a looped back `List[ChatMessage]` from the `JsonSchemaValidator` and
+    sends it down to the `OpenAIChatGenerator` for re-generation. We can have multiple loop back connections in the
+    pipeline. In this instance, the downstream component is only one – the `OpenAIChatGenerator` – but the pipeline can have more
+    than one downstream component.
     """
 
     def __init__(self, type_: TypeAlias):
+        """
+        Create a `Multiplexer` component.
+
+        :param type_: The type of data that the `Multiplexer` will receive from the upstream connected components and
+                        distribute to the downstream connected components.
+        """
         self.type_ = type_
         component.set_input_types(self, value=Variadic[type_])
         component.set_output_types(self, value=type_)
 
     def to_dict(self):
+        """
+        Serializes the component to a dictionary.
+
+        :returns:
+            Dictionary with serialized data.
+        """
         return default_to_dict(self, type_=serialize_type(self.type_))
 
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "Multiplexer":
+        """
+        Deserializes the component from a dictionary.
+
+        :param data:
+            Dictionary to deserialize from.
+        :returns:
+              Deserialized component.
+        """
         data["init_parameters"]["type_"] = deserialize_type(data["init_parameters"]["type_"])
         return default_from_dict(cls, data)
 
     def run(self, **kwargs):
+        """
+        Multiplexes the input data from the upstream connected components and distributes it to the downstream connected
+        components.
+
+        :param **kwargs: The input data. Must be of the type declared in `__init__`.
+        :return: A dictionary with the following keys:
+            - `value`: The input data.
+        """
         if (inputs_count := len(kwargs["value"])) != 1:
             raise ValueError(f"Multiplexer expects only one input, but {inputs_count} were received.")
         return {"value": kwargs["value"][0]}