DanielDaCosta / ai-flashcards-generator

Parses lengthy YouTube transcripts with its Semantic Extraction Algorithm (SEA) to create flashcards
MIT License
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Greptile: Add docstrings to backend/services/genai.py file #3

Open DanielDaCosta opened 3 months ago

greptile-apps[bot] commented 3 months ago

Add the following docstrings to backend/services/genai.py:

class GeminiProcessor:
    """
    A processor class for handling operations with the Gemini model.

    Attributes:
        model (VertexAI): The VertexAI model instance.
    """

    def __init__(self, model_name, project) -> None:
        """
        Initialize the GeminiProcessor with a model name and project.

        Args:
            model_name (str): The name of the model to use.
            project (str): The project identifier.
        """

    def generate_document_summary(self, documents: list, **args):
        """
        Generate a summary for a list of documents.

        Args:
            documents (list): A list of documents to summarize.
            **args: Additional arguments for the summarization chain.

        Returns:
            str: The generated summary.
        """

    def count_total_tokens(self, docs: list):
        """
        Count the total number of billable characters in a list of documents.

        Args:
            docs (list): A list of documents.

        Returns:
            int: The total number of billable characters.
        """

    def get_model(self):
        """
        Get the VertexAI model instance.

        Returns:
            VertexAI: The model instance.
        """

class YoutubeProcessor:
    """
    A processor class for handling YouTube video transcripts.

    Attributes:
        text_splitter (RecursiveCharacterTextSplitter): The text splitter instance.
        GeminiProcessor (GeminiProcessor): The GeminiProcessor instance.
    """

    def __init__(self, genai_processor: GeminiProcessor) -> None:
        """
        Initialize the YoutubeProcessor with a GeminiProcessor instance.

        Args:
            genai_processor (GeminiProcessor): The GeminiProcessor instance.
        """

    def retrieve_youtube_documents(self, video_url: str, verbose=False):
        """
        Retrieve and split the transcript of a YouTube video.

        Args:
            video_url (str): The URL of the YouTube video.
            verbose (bool): Whether to log detailed information.

        Returns:
            list: A list of split documents.
        """

    def format_processed_concepts(self, processed_concepts):
        """
        Format processed concepts into a list of dictionaries.

        Args:
            processed_concepts (list): A list of processed concepts.

        Returns:
            list: A formatted list of dictionaries with terms and definitions.
        """

    def find_key_concepts(self, documents: list, sample_size: int=0, verbose=False):
        """
        Find key concepts in a list of documents.

        Args:
            documents (list): A list of documents.
            sample_size (int): The sample size for processing.
            verbose (bool): Whether to log detailed information.

        Returns:
            list: A list of key concepts and their definitions.
        """

References

/backend/services/genai.py

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