An intuitive approach to building with LLMs

1from openai.types.chat import ChatCompletionMessageParam
2
3from mirascope.openai import OpenAICall
4
5
6class Librarian(OpenAICall):
7    prompt_template = """
8    SYSTEM: You are the world's greatest librarian.
9    MESSAGES: {history}
10    USER: {question}
11    """
12
13    question: str
14    history: list[ChatCompletionMessageParam] = []
15
16
17librarian = Librarian(question="", history=[])
18while True:
19    librarian.question = input("(User): ")
20    response = librarian.call()
21    librarian.history += [
22        {"role": "user", "content": librarian.question},
23        {"role": "assistant", "content": response.content},
24    ]
25    print(f"(Assistant): {response.content}")
26
27#> (User): What fantasy book should I read?
28#> (Assistant): Have you read the Name of the Wind?
29#> (User): I have! What do you like about it?
30#> (Assistant): I love the intricate world-building...

1from typing import Literal
2
3from mirascope.openai import OpenAICall, OpenAICallParams
4
5
6def get_current_weather(
7    location: str, unit: Literal["celsius", "fahrenheit"] = "fahrenheit"
8):
9    """Get the current weather in a given location."""
10    if "tokyo" in location.lower():
11        print(f"It is 10 degrees {unit} in Tokyo, Japan")
12    elif "san francisco" in location.lower():
13        print(f"It is 72 degrees {unit} in San Francisco, CA")
14    elif "paris" in location.lower():
15        print(f"It is 22 degress {unit} in Paris, France")
16    else:
17        print("I'm not sure what the weather is like in {location}")
18
19
20class Forecast(OpenAICall):
21    prompt_template = "What's the weather in Tokyo?"
22
23    call_params = OpenAICallParams(model="gpt-4", tools=[get_current_weather])
24
25tool = Forecast().call().tool
26if tool:
27    tool.fn(**tool.args)
28    #> It is 10 degrees fahrenheit in Tokyo, Japan

1import os
2from functools import cached_property
3
4from mirascope.openai import OpenAICall, OpenAICallParams
5
6os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
7
8
9class ChefSelector(OpenAICall):
10    prompt_template = "Name a chef who is really good at cooking {food_type} food"
11
12    food_type: str
13
14    call_params = OpenAICallParams(model="gpt-3.5-turbo-0125")
15
16
17class RecipeRecommender(ChefSelector):
18    prompt_template = """
19    SYSTEM:
20    Imagine that you are chef {chef}.
21    Your task is to recommend recipes that you, {chef}, would be excited to serve.
22    USER:
23    Recommend a {food_type} recipe using {ingredient}.
24    """
25
26    ingredient: str
27    
28    call_params = OpenAICallParams(model="gpt-4")
29
30    @cached_property  # !!! so multiple access doesn't make multiple calls
31    def chef(self) -> str:
32        """Uses `ChefSelector` to select the chef based on the food type."""
33        return ChefSelector(food_type=self.food_type).call().content
34
35response = RecipeRecommender(food_type="japanese", ingredient="apples").call()
36print(response.content)
37# > Certainly! Here's a recipe for a delicious and refreshing Japanese Apple Salad: ...

1from typing import Literal, Type
2
3from mirascope.openai import OpenAIExtractor
4from pydantic import BaseModel
5
6
7class TaskDetails(BaseModel):
8    description: str
9    due_date: str
10    priority: Literal["low", "normal", "high"]
11
12
13class TaskExtractor(OpenAIExtractor[TaskDetails]):
14    extract_schema: Type[TaskDetails] = TaskDetails
15    prompt_template = """
16    Extract the task details from the following task:
17    {task}
18    """
19
20    task: str
21
22
23task = "Submit quarterly report by next Friday. Task is high priority."
24task_details = TaskExtractor(task=task).extract()
25assert isinstance(task_details, TaskDetails)
26print(TaskDetails)
27#> description='Submit quarterly report' due_date='next Friday' priority='high'

1import os
2from typing import Type
3
4from fastapi import FastAPI
5from mirascope.openai import OpenAIExtractor
6from pydantic import BaseModel
7
8os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
9
10app = FastAPI()
11
12
13class Book(BaseModel):
14    title: str
15    author: str
16
17
18class BookRecommender(OpenAIExtractor[Book]):
19    extract_schema: Type[Book] = Book
20    prompt_template = "Please recommend a {genre} book."
21
22    genre: str
23
24
25@app.post("/")
26def root(book_recommender: BookRecommender) -> Book:
27    """Generates a book based on provided `genre`."""
28    return book_recommender.extract()