lyser

A python package with classes that processes text and creates a rag chef chat agent that gives cooking tips and recipes.

How to install

pip install lyser

A class to perform text analysis on a given corpus.

    Attributes:
        corpus (str): The corpus to analyze.
        
    Methods:
        pipeline(): A function to perform text analysis on a given corpus.
        Returns a list of tokens.
        e.g. ['the', 'cat', 'sat', 'on', 'the', 'mat']
        
        pos_tagging(): A function to perform POS tagging on a given corpus.
        Returns a list of tuples containing the word and its POS tag.
        e.g. [('the', 'DT'), ('cat', 'NN'), ('sat', 'VB'), ('on', 'IN'), ('the', 'DT'), ('mat', 'NN')]
        
        visualize_word_cloud(): A function to visualize the word cloud of a given corpus.
        
    Example:
        analyzer = TextAnalyzer(corpus=sample_text)
        analyzer.pipeline()
        analyzer.pos_tagging()
        analyzer.visualize_word_cloud()

A class to invoke Chef Chat

    Attributes:
        folder_path (str, pdf): path to folder_path with recipe files
        query (str): user input
        
        
    Methods:
        data_loader(): A function that loads the pdf books in a folder and returns a list of documents.
        Returns:
            list: A list of documents. Each document is a dictionary with a 'page_content' key and 'metadata' key.
        
        chat_summary():  A function that creates a chat summary 
        Returns:
            chat_memory: A AgentTokenBufferMemory chat summary memory.

        
        rag_agent(): A function that creates a rag agent that returns recipes of meals from cook pdf books 
        Returns:
            agent_result: A dictionary with the output of the rag agent.
        
        
    Example:
        # chef_chat = ChefChat(folder_path="data", query="How can i make pineapple chicken")
        print(chef_chat.rag_agent())