Docs/pydocs (iusztinpaul#40)

* docs: Rename GA files

* docs: Add PyDocs

.github/workflows/cd_streaming_pipeline.yaml

@@ -1,4 +1,4 @@
-name: Continuous Deployment (CD) for the Streaming Pipeline
+name: Continuous Deployment (CD) | Streaming Pipeline
 
 on: [workflow_dispatch]
 

.github/workflows/ci_financial_bot.yaml

@@ -1,4 +1,4 @@
-name: Continuous Integration (CI) for the Financial Bot
+name: Continuous Integration (CI) | Financial Bot
 
 on: [push]
 

.github/workflows/ci_streaming_pipeline.yaml

@@ -1,4 +1,4 @@
-name: Continuous Integration (CI) for the Streaming Pipeline
+name: Continuous Integration (CI) | Streaming Pipeline
 
 on: [push]
 

.github/workflows/ci_training_pipeline.yaml

@@ -1,4 +1,4 @@
-name: Continuous Integration (CI) for the Training Pipeline
+name: Continuous Integration (CI) | Training Pipeline
 
 on: [push]
 

.github/workflows/destroy_aws_streaming_pipeline.yaml → .github/workflows/destroy_aws_infrastructure.yaml

@@ -1,4 +1,4 @@
-name: Destroy the AWS infrastructure for the Streaming Pipeline
+name: Destroy AWS Infrastructure
 
 on: [workflow_dispatch]
 

modules/financial_bot/tools/bot.py

@@ -52,8 +52,18 @@ def load_bot(
     """
     Load the financial assistant bot in production or development mode based on the `debug` flag
 
-    production: the embedding model runs on GPU and the fine-tuned LLM is used.
-    dev: the embedding model runs on CPU and the fine-tuned LLM is mocked.
+    In DEV mode the embedding model runs on CPU and the fine-tuned LLM is mocked.
+    Otherwise, the embedding model runs on GPU and the fine-tuned LLM is used.
+
+    Args:
+        env_file_path (str): Path to the environment file.
+        logging_config_path (str): Path to the logging configuration file.
+        model_cache_dir (str): Path to the directory where the model cache is stored.
+        embedding_model_device (str): Device to use for the embedding model.
+        debug (bool): Flag to indicate whether to run the bot in debug mode or not.
+
+    Returns:
+        FinancialBot: An instance of the FinancialBot class.
     """
 
     from financial_bot import initialize
@@ -83,7 +93,17 @@ def load_bot_dev(
     logging_config_path: str = "logging.yaml",
     model_cache_dir: str = "./model_cache",
 ):
-    """Load the Financial Assistant Bot in dev mode: the embedding model runs on CPU and the LLM is mocked."""
+    """
+    Load the Financial Assistant Bot in dev mode: the embedding model runs on CPU and the LLM is mocked.
+
+    Args:
+        env_file_path (str): Path to the environment file.
+        logging_config_path (str): Path to the logging configuration file.
+        model_cache_dir (str): Path to the directory where the model cache is stored.
+
+    Returns:
+        The loaded Financial Assistant Bot in dev mode.
+    """
 
     return load_bot(
         env_file_path=env_file_path,
@@ -99,7 +119,19 @@ def load_bot_dev(
 
 @financial_bot.rest_api(keep_warm_seconds=300, loader=load_bot)
 def run(**inputs):
-    """Run the bot under the Beam RESTful API endpoint."""
+    """
+    Run the bot under the Beam RESTful API endpoint.
+
+     Args:
+        inputs (dict): A dictionary containing the following keys:
+            - context: The bot instance.
+            - about_me (str): Information about the user.
+            - question (str): The user's question.
+            - history (list): A list of previous conversations (optional).
+
+    Returns:
+        str: The bot's response to the user's question.
+    """
 
     response = _run(**inputs)
 
@@ -108,7 +140,19 @@ def run(**inputs):
 
 @financial_bot_dev.rest_api(keep_warm_seconds=300, loader=load_bot_dev)
 def run_dev(**inputs):
-    """Run the bot under the Beam RESTful API endpoint [Dev Mode]."""
+    """
+    Run the bot under the Beam RESTful API endpoint [Dev Mode].
+
+     Args:
+        inputs (dict): A dictionary containing the following keys:
+            - context: The bot instance.
+            - about_me (str): Information about the user.
+            - question (str): The user's question.
+            - history (list): A list of previous conversations (optional).
+
+    Returns:
+        str: The bot's response to the user's question.
+    """
 
     response = _run(**inputs)
 
@@ -121,7 +165,19 @@ def run_local(
     history: List[Tuple[str, str]] = None,
     debug: bool = False,
 ):
-    """Run the bot locally in production or dev mode."""
+    """
+    Run the bot locally in production or dev mode.
+
+    Args:
+        about_me (str): A string containing information about the user.
+        question (str): A string containing the user's question.
+        history (List[Tuple[str, str]], optional): A list of tuples containing the user's previous questions
+            and the bot's responses. Defaults to None.
+        debug (bool, optional): A boolean indicating whether to run the bot in debug mode. Defaults to False.
+
+    Returns:
+        str: A string containing the bot's response to the user's question.
+    """
 
     if debug is True:
         bot = load_bot_dev(model_cache_dir=None)
@@ -141,7 +197,19 @@ def run_local(
 
 
 def _run(**inputs):
-    """Central function that calls the bot and returns the response"""
+    """
+    Central function that calls the bot and returns the response.
+
+    Args:
+        inputs (dict): A dictionary containing the following keys:
+            - context: The bot instance.
+            - about_me (str): Information about the user.
+            - question (str): The user's question.
+            - history (list): A list of previous conversations (optional).
+
+    Returns:
+        str: The bot's response to the user's question.
+    """
 
     from financial_bot import utils
 

modules/financial_bot/tools/ui.py

@@ -10,6 +10,13 @@
 
 
 def parseargs() -> argparse.Namespace:
+    """
+    Parses command line arguments for the Financial Assistant Bot.
+
+    Returns:
+        argparse.Namespace: An object containing the parsed arguments.
+    """
+
     parser = argparse.ArgumentParser(description="Financial Assistant Bot")
 
     parser.add_argument(
@@ -66,8 +73,18 @@ def load_bot(
     """
     Load the financial assistant bot in production or development mode based on the `debug` flag
 
-    production: the embedding model runs on GPU and the fine-tuned LLM is used.
-    dev: the embedding model runs on CPU and the fine-tuned LLM is mocked.
+    In DEV mode the embedding model runs on CPU and the fine-tuned LLM is mocked.
+    Otherwise, the embedding model runs on GPU and the fine-tuned LLM is used.
+
+    Args:
+        env_file_path (str): Path to the environment file.
+        logging_config_path (str): Path to the logging configuration file.
+        model_cache_dir (str): Path to the directory where the model cache is stored.
+        embedding_model_device (str): Device to use for the embedding model.
+        debug (bool): Flag to indicate whether to run the bot in debug mode or not.
+
+    Returns:
+        FinancialBot: An instance of the FinancialBot class.
     """
 
     from financial_bot import initialize
@@ -106,6 +123,18 @@ def load_bot(
 
 
 def predict(message: str, history: List[List[str]], about_me: str) -> str:
+    """
+    Predicts a response to a given message using the financial_bot Gradio UI.
+
+    Args:
+        message (str): The message to generate a response for.
+        history (List[List[str]]): A list of previous conversations.
+        about_me (str): A string describing the user.
+
+    Returns:
+        str: The generated response.
+    """
+
     generate_kwargs = {
         "about_me": about_me,
         "question": message,

modules/streaming_pipeline/streaming_pipeline/__init__.py

@@ -9,6 +9,14 @@
 
 
 def initialize(logging_config_path: str = "logging.yaml", env_file_path: str = ".env"):
+    """
+    Initializes the logger and environment variables.
+
+    Args:
+        logging_config_path (str): The path to the logging configuration file. Defaults to "logging.yaml".
+        env_file_path (str): The path to the environment variables file. Defaults to ".env".
+    """
+
     logger.info("Initializing logger...")
     try:
         initialize_logger(config_path=logging_config_path)

modules/streaming_pipeline/streaming_pipeline/alpaca_batch.py

@@ -17,6 +17,8 @@ class AlpacaNewsBatchInput(DynamicInput):
 
     Args:
         tickers: list - should be a list of tickers, use "*" for all
+        from_datetime: datetime.datetime - the start datetime for the news data
+        to_datetime: datetime.datetime - the end datetime for the news data
     """
 
     def __init__(
@@ -52,6 +54,15 @@ def build(self, worker_index, worker_count):
 
 
 class AlpacaNewsBatchSource(StatelessSource):
+    """
+    A batch source for retrieving news articles from Alpaca.
+
+    Args:
+        tickers (List[str]): A list of ticker symbols to retrieve news for.
+        from_datetime (datetime.datetime): The start datetime to retrieve news from.
+        to_datetime (datetime.datetime): The end datetime to retrieve news from.
+    """
+
     def __init__(
         self,
         tickers: List[str],
@@ -63,6 +74,13 @@ def __init__(
         )
 
     def next(self):
+        """
+        Retrieves the next batch of news articles.
+
+        Returns:
+            List[dict]: A list of news articles.
+        """
+
         news = self._alpaca_client.list()
 
         if news is None or len(news) == 0:
@@ -71,6 +89,10 @@ def next(self):
         return news
 
     def close(self):
+        """
+        Closes the batch source.
+        """
+
         pass
 
 
@@ -81,6 +103,23 @@ def build_alpaca_client(
     api_secret: Optional[str] = None,
     tickers: Optional[List[str]] = None,
 ) -> "AlpacaNewsBatchClient":
+    """
+    Builds an AlpacaNewsBatchClient object with the specified parameters.
+
+    Args:
+        from_datetime (datetime.datetime): The start datetime for the news batch.
+        to_datetime (datetime.datetime): The end datetime for the news batch.
+        api_key (Optional[str], optional): The Alpaca API key. Defaults to None.
+        api_secret (Optional[str], optional): The Alpaca API secret. Defaults to None.
+        tickers (Optional[List[str]], optional): The list of tickers to retrieve news for. Defaults to None.
+
+    Raises:
+        KeyError: If api_key or api_secret is not provided and is not found in the environment variables.
+
+    Returns:
+        AlpacaNewsBatchClient: The AlpacaNewsBatchClient object.
+    """
+
     if api_key is None:
         try:
             api_key = os.environ["ALPACA_API_KEY"]
@@ -110,7 +149,19 @@ def build_alpaca_client(
 
 
 class AlpacaNewsBatchClient:
-    """Alpaca News API Client that uses a RESTful API to fetch news data."""
+    """
+    Alpaca News API Client that uses a RESTful API to fetch news data.
+
+    Attributes:
+        NEWS_URL (str): The URL for the Alpaca News API.
+        _from_datetime (datetime.datetime): The start datetime for the news data.
+        _to_datetime (datetime.datetime): The end datetime for the news data.
+        _api_key (str): The API key for the Alpaca News API.
+        _api_secret (str): The API secret for the Alpaca News API.
+        _tickers (List[str]): A list of tickers to filter the news data.
+        _page_token (str): The page token for the next page of news data.
+        _first_request (bool): A flag indicating whether this is the first request for news data.
+    """
 
     NEWS_URL = "https://data.alpaca.markets/v1beta1/news"
 
@@ -122,6 +173,17 @@ def __init__(
         api_secret: str,
         tickers: List[str],
     ):
+        """
+        Initializes a new instance of the AlpacaNewsBatchClient class.
+
+        Args:
+            from_datetime (datetime.datetime): The start datetime for the news data.
+            to_datetime (datetime.datetime): The end datetime for the news data.
+            api_key (str): The API key for the Alpaca News API.
+            api_secret (str): The API secret for the Alpaca News API.
+            tickers (List[str]): A list of tickers to filter the news data.
+        """
+
         self._from_datetime = from_datetime
         self._to_datetime = to_datetime
         self._api_key = api_key
@@ -133,11 +195,21 @@ def __init__(
 
     @property
     def try_request(self) -> bool:
+        """
+        A property indicating whether a request should be attempted.
+
+        Returns:
+            bool: True if a request should be attempted, False otherwise.
+        """
+
         return self._first_request or self._page_token is not None
 
     def list(self):
         """
         Convenience function to fetch a batch of news from Alpaca API
+
+        Returns:
+            List[Dict]: A list of news items.
         """
 
         if not self.try_request: