src copy/tools/functions.py

import json
import logging
import os

logger = logging.getLogger(__name__)

"""Schedule meeting integration function."""


def fetch_next_question() -> str:
    """Fetch the next question.

    Returns:
        str: The next question.
    """
    questions = [
        "What is the capital of France?",
        "What is 2 + 2?",
        "Who wrote Romeo and Juliet?",
        "What is the chemical symbol for gold?",
        "Which planet is known as the Red Planet?",
    ]
    question = questions[0]

    return f"You need to ask the candidate following question: `{question}`. Allow the candidate some time to respond "


fetch_next_question_tool = {
    "name": "fetch_next_question",
    "description": "Fetch the next question",
}


def validate_answer(
    question_id: int, answer: str, answer_type: str | int | list
) -> str:
    """Validate the user's answer against an expected answer type.

        question_id (int): The identifier of the question being validated
        answer (str): The user's provided answer to validate
        answer_type (type): The expected python type that the answer should match (e.g. str, int, list)

        str: Returns "Answer is valid" if answer matches expected type, raises ValueError otherwise

    Raises:
        ValueError: If the answer's type does not match the expected answer_type

    Example:
        >>> validate_answer(1, "42", str)
        True
        >>> validate_answer(1, 42, str)
        ValueError: Invalid answer type
    """
    logging.info(
        {
            "question_id": question_id,
            "answer": answer,
            "answer_type": answer_type,
        }
    )
    if type(answer) is answer_type:
        raise ValueError("Invalid answer type")

    # Create or load the answers file
    answers_file = "/Users/georgeslorre/ML6/internal/gemini-voice-agents/answers.json"
    answers = []

    if os.path.exists(answers_file):
        with open(answers_file, "r") as f:
            answers = json.load(f)

    # Append new answer
    answers[question_id] = {"question_id": question_id, "answer": answer}

    # Write back to file
    with open(answers_file, "w") as f:
        json.dump(answers, f, indent=2)

    return "Answer is valid"


validate_answer_tool = {
    "name": "validate_answer",
    "description": "Validate the user's answer against an expected answer type",
    "parameters": {
        "type": "OBJECT",
        "properties": {
            "question_id": {
                "type": "INTEGER",
                "description": "The identifier of the question being validated"
            },
            "answer": {
                "type": "STRING",
                "description": "The user's provided answer to validate"
            },
            "answer_type": {
                "type": "STRING", 
                "description": "The expected python type that the answer should match (e.g. str, int, list)"
            }
        },
        "required": ["question_id", "answer", "answer_type"]
    }
}


def store_input(role: str, input: str) -> str:
    """Store conversation input in a JSON file.

    Args:
        role (str): The role of the speaker (user or assistant)
        input (str): The text input to store

    Returns:
        str: Confirmation message
    """
    conversation_file = "/Users/georgeslorre/ML6/internal/gemini-voice-agents/conversation.json"
    conversation = []

    if os.path.exists(conversation_file):
        with open(conversation_file, "r") as f:
            conversation = json.load(f)

    conversation.append({"role": role, "content": input})

    with open(conversation_file, "w") as f:
        json.dump(conversation, f, indent=2)

    return "Input stored successfully"



store_input_tool = {
    "name": "store_input",
    "description": "Store user input in conversation history",
    "parameters": {
        "type": "OBJECT",
        "properties": {
            "role": {
                "type": "STRING",
                "description": "The role of the speaker (user or assistant)"
            },
            "input": {
                "type": "STRING", 
                "description": "The text input to store"
            }
        }
    }
}