Dialogue Rule augmentation

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Table of Contents

Parameters

main(q, d, m): The primary function that takes in the Question (q), Domain (d), and Medical (m) parameters and returns the Answer (a) and Grade (grade).

Decision Logic: Implements the conditional logic as per the pseudocode to determine the appropriate answer a.

answer_content(): A placeholder function to generate the answer content c.

retrieve_documents(): A placeholder function to simulate document retrieval r.

define_ability(answer_content): Grades the ability based on the answer content c.

reflect(answer_content, documents): Reflects on the answer content and documents to provide a grade grade_Re.

Example Usage: At the end, there’s an example of how to use the main function with sample inputs.

Python Implementation

def main(q, d, m):
    # Initialize variables
    a = None  # Answer
    c = None  # Grade of Answer Content

    # Decision logic based on question type, domain, and medical flag
    if (q == "Informational" or q == "Open-Ended") and (d == "Yes") and (m == "Yes"):
        a = "Direct or Elaborated"
    elif (q == "Any") and (d == "No"):
        if m == "No":
            a = "(deferred) Dialogue Reset"
        elif m == "Yes":
            a = "(deferred) Refer To Specialist"

    # Generate answer content
    c = answer_content()

    # Retrieve relevant documents
    r = retrieve_documents()

    # Grade the ability and reflection based on the content and documents
    grade_Ab = define_ability(c)
    grade_Re = reflect(c, r)

    # Compile the grades into a dictionary
    grade = {"Ab": grade_Ab, "Re": grade_Re}

    # Return the answer and the grades
    return a, grade

def answer_content():
    """
    Generates the answer content.
    """
    # Placeholder implementation
    return "Sample Answer Content"

def retrieve_documents():
    """
    Retrieves relevant documents.
    """
    # Placeholder implementation
    return ["Document1", "Document2"]

def define_ability(answer_content):
    """
    Grades the ability based on the answer content.
    """
    # Placeholder grading logic
    # For example, grade based on the length of the content
    return len(answer_content)

def reflect(answer_content, documents):
    """
    Reflects on the answer content and documents to provide a grade.
    """
    # Placeholder reflection logic
    # For example, grade based on the number of documents retrieved
    return len(documents)

# Example usage:
if __name__ == "__main__":
    q = "Informational"
    d = "Yes"
    m = "Yes"
    answer, grades = main(q, d, m)
    print("Answer:", answer)
    print("Grades:", grades)

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