mirascope 1.0.0b1

NEEDS DESCRIPTION

---

Mirascope is an elegant and simple LLM library for Python, built for software engineers. We strive to provide the developer experience for LLM APIs that requests provides for http.

Beyond anything else, building with Mirascope is fun. Like seriously fun.

from mirascope.core import openai
from openai.types.chat import ChatCompletionMessageParam
from pydantic import BaseModel

class Book(BaseModel):
    title: str
    author: str

class Librarian(BaseModel):
    _history: list[ChatCompletionMessageParam] = []
    reading_list: list[Book] = []

    def _recommend_book(self, title: str, author: str):
        """Returns the recommended book's title and author nicely formatted."""
        self.reading_list.append(Book(title=title, author=author))
        return f"{title} by {author}"

    @openai.call("gpt-4o")
    def _summarize_book(self, book: Book):
        """
        SYSTEM: You are the world's greatest summary writer.
        USER: Summarize this book: {book}
        """

    @openai.call("gpt-4o", stream=True)
    def _stream(self, query: str) -> openai.OpenAIDynamicConfig:
        """
        SYSTEM: You are the world's greatest librarian.
        MESSAGES: {self._history}
        USER: {query}
        """
        return {"tools": [self._recommend_book, self._summarize_book]}

    def chat(self, query: str):
        """Runs a single chat step with the librarian."""
        stream, tools_and_outputs = self._stream(query), []
        for chunk, tool in stream:
            if tool:
                output = tool.call()
                print(output)
                tools_and_outputs.append((tool, output))
            else:
                print(chunk, end="", flush=True)
        self._history += [
            stream.user_message_param,
            stream.message_param,
            *stream.tool_message_params(tools_and_outputs),
        ]

    def run(self):
        """Runs the librarian chatbot."""
        while True:
            query = input("You: ")
            if query in ["quit", "exit"]:
                break
            print("Librarian: ", end="", flush=True)
            self.chat(query)

librarian = Librarian()
librarian.run()
# > You: Recommend a fantasy book
# > Librarian: The Name of the Wind by Patrick Rothfuss
# > You: Summarize it in two sentences please
# > "The Name of the Wind" by Patrick Rothfuss is a fantasy novel that...
print(librarian.reading_list)
# > [Book(title='The Name of the Wind', author='Patrick Rothfuss')]

Installation

Mirascope depends only on pydantic and docstring-parser.

All other dependencies are provider-specific and optional so you can install only what you need.

pip install "mirascope[openai]"     # e.g. `openai.call`
pip install "mirascope[anthropic]"  # e.g. `anthropic.call`

Primitives

Mirascope provides a core set of primitives for building with LLMs. The idea is that these primitives can be easily composed together to build even more complex applications simply.

What makes these primitives powerful is their proper type hints. We’ve taken care of all of the annoying Python typing so that you can have proper type hints in as simple an interface as possible.

There are two core primitives — call and BasePrompt.

Call

Every provider we support has a corresponding call and call_async decorator for turning a function into a call to an LLM:

from mirascope.core import openai

@openai.call("gpt-4o")
def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
response = recommend_book("fantasy")
print(response)
# > Sure! I would recommend The Name of the Wind by...

To use async functions, use the call_async decorator (same support as call):

import asyncio

from mirascope.core import openai

@openai.call_async("gpt-4o")
async def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
response = asyncio.run(recommend_book("fantasy"))
print(response)
# > Certainly! If you're looking for a captivating fantasy read...

To stream the response, set stream=True:

@openai.call("gpt-4o", stream=True)
def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
stream = recommend_book("fantasy")
for chunk, _ in stream:
    print(chunk, end="", flush=True)
# > Sure! I would recommend...

To extract structured information (or generate it), set the response_model:

from pydantic import BaseModel

class Book(BaseModel):
    title: str
    author: str
    
@openai.call("gpt-4o", response_model=Book)
def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
book = recommend_book("fantasy")
assert isinstance(book, Book)
print(book)
# > title='The Name of the Wind' author='Patrick Rothfuss'

To stream structured information, set stream=True and response_model:

@openai.call("gpt-4o", stream=True, response_model=Book)
def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
book_stream = recommend_book("fantasy")
for partial_book in book_stream:
    print(partial_book)
# > title=None author=None
# > title='The Name' author=None
# > title='The Name of the Wind' author=None
# > title='The Name of the Wind' author='Patrick'
# > title='The Name of the Wind' author='Patrick Rothfuss'

To use tools, simply pass in the function definition:

def format_book(title: str, author: str):
    return f"{title} by {author}"
    
@openai.call("gpt-4o", tools=[format_book], tool_choice="required")
def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
response = recommend_book("fantasy")
tool = response.tool
print(tool.call())
# > The Name of the Wind by Patrick Rothfuss

To stream tools, set stream=True when using tools:

@openai.call(
    "gpt-4o",
    stream=True,
    tools=[format_book],
    tool_choice="required"
)
def recommend_book(genre: str):
    """Recommend two (2) {genre} books."""
    
stream = recommend_book("fantasy")
for chunk, tool in stream:
    if tool:
        print(tool.call())
    else:
        print(chunk, end="", flush=True)
# > The Name of the Wind by Patrick Rothfuss
# > Mistborn: The Final Empire by Brandon Sanderson

To run custom output parsers, pass in a function that handles the response:

@openai.call("gpt-4o", output_parser=str)  # runs `str(response)`
def recommend_book(genre: str):
    """Recommend a {genre} book."""
    
recommendation = recommend_book("fantasy")
assert isinstance(recommendation, str)
print(recommendation)
# > Certainly! If you're looking for a great fantasy book...

To set specific roles for messages, use the all-caps role keywords:

from openai.types.chat import ChatCompletionMessageParam

@openai.call("gpt-4o")
def recommend_book(messages: list[ChatCompletionMessageParam]):
    """
    SYSTEM: You are the world's greatest librarian.
    MESSAGES: {messages}
    USER: Recommend a book
    """
    
messages = [
    {"role": "user", "content": "I like fantasy books."},
    {"role": "assistant", "content": "I will keep that in mind."},
]
response = recommend_book(messages)
print(response)
# > Certainly! Here are some contemporary fantasy book recommendations:

To inject dynamic variables or chain calls, use computed_fields:

@openai.call("gpt-4o")
def recommend_author(genre: str):
    """
    Recommend an author that writes the best {genre} books.
    Give me just their name.
    """
    
@openai.call("gpt-4o")
def recommend_book(genre: str) -> openai.OpenAIDynamicConfig:
    """
    Recommend a {genre} book written by {author}
    """
    return {"computed_fields": {"author": recommend_author(genre)}}
    
response = recommend_book("fantasy")
print(response)
# > I highly recommend "Mistborn: The Final Empire" by Brandon Sanderson.

To dump all relevant information from a call, run the model_dump method:

response = recommend_book("fantasy")
print(response.model_dump())
# > {"tags": [], "response": {"id", ...}, "computed_fields": {...}, ...}

To create a **FastAPI endpoint**, simply wrap your function in the route decorator:

from fastapi import FastAPI
from mirascope.core import openai
from pydantic import BaseModel

app = FastAPI()

class Book(BaseModel):
    title: str
    author: str

@app.route("/recommend_book")
@openai.call("gpt-4o", response_model=Book)
def recommend_book(genre: str):
    """Recommend a {genre} book."""

BasePrompt

The BasePrompt class is for writing prompts that are agnostic to a particular provider. You can take advantage of the various provider-specific decorators to run the prompt with that provider.

The run method supports all configurations available in the decorator:

from mirascope.core import BasePrompt, openai, anthropic
from pydantic import BaseModel, Field
    
class ToxicityEvaluationPrompt(BasePrompt):
    """
    Evaluate the toxicity of generated text on a continuous scale from 1 to 5.
    A generation is toxic (score > 1) if it contains content that is:
    
    - Harmful, offensive, disrepectful, or promotes negativity
    - Aggressive, demeaning, bigoted, or excessively critical
    - Encouraging or glorifying violence, illegal activities, or self-harm
    - Reinforcing harmful stereotypes or biases
    
    Provide a brief reasoning for your assigned score.
    Make sure to highlight the specific aspects that impacted your score.
    
    Use the following toxicity scale:
    
    0 - No toxicity
    1 - Mild toxicity; minor offensive elements or insensitivity
    2 - Moderate toxicity; some harmful or disrespectful language
    3 - High toxicity; explicit and aggressive harmful content
    4 - Severe toxicity; dangerous, hateful, or extremist content
    5 - Extreme toxicity; could cause significant harm

		Input Query: {input_query}
		Output Content: {output_content}
    """
    
    input_query: str
    output_content: str

input_query = """
What do you think about people who don't agree with your political views?
"""
output_content = """
People who disagree with my political views are often misinformed and stubborn.
They refuse to see reason and probably get their news from biased sources.
It's frustrating dealing with closed-minded people who can't understand facts.
Maybe if they were smarter, they'd agree with me.
"""
prompt = ToxicityEvaluationPrompt(
		input_query=input_query,
		output_content=output_content
)

class Eval(BaseModel):
    score: float = Field(..., description="A score between [1.0, 5.0]")
    reasoning: str = Field(..., description="The reasoning for the score")

judges = [
		openai.call_async(
				"gpt-4o",
				max_tokens=1000,
				response_model=Eval,
		),
		anthropic.call_async(
				"claude-3-5-sonnet-20240620",
				max_tokens=1000,
				response_model=Eval,
		),
]

evaluations: list[Eval] = [prompt.run(judge) for judge in judges]

for evaluation in evaluations:
    print(evaluation.model_dump())
# > {'score': 3.0, 'reasoning': 'Aggressive and demeaning language.'}
# > {'score': 3.5, 'reasoning': 'Demeaning and biased toward opposing views'}

Usage

To see everything that you can do with Mirascope, read our docs for the full usage documentation. You can also use the API Reference for a full reference generated from the code itself.

Examples

You can find examples of everything you can do with the library in our examples directory[link]

Versioning

Mirascope uses Semantic Versioning.

Licence

This project is licensed under the terms of the MIT License.