Attaching tool functions to Mirascope Calls

Using Mirascope OpenAI Tool

Create your call and pass in your OpenAITool:

from typing import Literal

from pydantic import Field

from mirascope.base import tool_fn
from mirascope.openai import OpenAICall, OpenAITool

@tool_fn(get_current_weather)
class GetCurrentWeather(OpenAITool):
    """Get the current weather in a given location."""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
    unit: Literal["celsius", "fahrenheit"] = "fahrenheit"

class TodaysForecast(OpenAICall):
    prompt_template = "What's the weather like in San Francisco, Tokyo, and Paris?"

    call_params = OpenAICallParams(
        model="gpt-3.5-turbo-1106", tools=[GetCurrentWeather]
    )

The tools are attached to the call_params attribute in a Mirascope Call. For more information, check out why colocation is so important and how combining it with the Mirascope CLI makes engineering better prompts and calls easy.

Using a function properly documented with a docstring

Create your call and pass in your function:

import json

from typing import Literal

from mirascope.openai import OpenAICall


def get_current_weather(
        location: str, unit: Literal["celsius", "fahrenheit"] = "fahrenheit"
) -> str:
    """Get the current weather in a given location.

    Args:
        location: The city and state, e.g. San Francisco, CA.
        unit: The unit for the temperature.

    Returns:
        A JSON object containing the location, temperature, and unit.
    """
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": unit})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})


class TodaysForecast(OpenAICall):
    prompt_template = "What's the weather like in San Francisco, Tokyo, and Paris?"

    call_params = OpenAICallParams(
        model="gpt-3.5-turbo-1106", tools=[get_current_weather]
    )

Calling Tools

Generate content by calling the call method:

# using same code as above
forecast = TodaysForecast()
response = forecast.call()
if tools := response.tools:
    for tool in tools:
        print(tool.fn(**tool.args))

#> {"location": "San Francisco", "temperature": "72", "unit": "celsius"}
#> {"location": "Tokyo", "temperature": "10", "unit": "celsius"}
#> {"location": "Paris", "temperature": "22", "unit": "celsius"}

The response.tools property returns an actual instance of the tool.

Async

All of the examples above also work with async by replacing call with call_async or stream with stream_async.