Mirascope is an LLM toolkit for lightning-fast, high-quality development. Building with Mirascope feels like writing the Python code you’re already used to writing.
pip install mirascopeYou can also install additional optional dependencies if you’re using those features:
pip install mirascope[anthropic] # AnthropicCall, ...
pip install mirascope[gemini] # GeminiCall, ...
pip install mirascope[wandb] # WandbOpenAICall, ...
pip install mirascope[all] # all optional dependenciesColocation is one of the core tenets of our philosophy. Everything that can impact the quality of a call to an LLM — from the prompt to the model to the temperature — must live together so that we can properly version and test the quality of our calls over time. This is useful since we have all of the information including metadata that we could want for analysis, which is particularly important during rapid development.
import os
from mirascope import tags
from mirascope.openai import OpenAICall, OpenAICallParams
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
@tags(["version:0003"])
class Editor(OpenAICall):
prompt_template = """
SYSTEM:
You are a top class manga editor.
USER:
I'm working on a new storyline. What do you think?
{storyline}
"""
storyline: str
call_params = OpenAICallParams(model="gpt-4", temperature=0.4)
storyline = "..."
editor = Editor(storyline=storyline)
print(editor.messages())
# > [{'role': 'system', 'content': 'You are a top class manga editor.'}, {'role': 'user', 'content': "I'm working on a new storyline. What do you think?\n..."}]
critique = editor.call()
print(critique.content)
# > I think the beginning starts off great, but...
print(editor.dump() | critique.dump())
# {
# "tags": ["version:0003"],
# "template": "SYSTEM:\nYou are a top class manga editor.\n\nUSER:\nI'm working on a new storyline. What do you think?\n{storyline}",
# "inputs": {"storyline": "..."},
# "start_time": 1710452778501.079,
# "end_time": 1710452779736.8418,
# "output": {
# "id": "chatcmpl-92nBykcXyTpxwAbTEM5BOKp99fVmv",
# "choices": [
# {
# "finish_reason": "stop",
# "index": 0,
# "logprobs": None,
# "message": {
# "content": "I think the beginning starts off great, but...",
# "role": "assistant",
# "function_call": None,
# "tool_calls": None,
# },
# }
# ],
# "created": 1710452778,
# "model": "gpt-4-0613",
# "object": "chat.completion",
# "system_fingerprint": None,
# "usage": {"completion_tokens": 25, "prompt_tokens": 33, "total_tokens": 58},
# },
# }Our template parser makes inserting chat history beyond easy:
from openai.types.chat import ChatCompletionMessageParam
from mirascope.openai import OpenAICall
class Librarian(OpenAICall):
prompt_template = """
SYSTEM: You are the world's greatest librarian.
MESSAGES: {history}
USER: {question}
"""
question: str
history: list[ChatCompletionMessageParam] = []
librarian = Librarian(question="", history=[])
while True:
librarian.question = input("(User): ")
response = librarian.call()
librarian.history += [
{"role": "user", "content": librarian.question},
{"role": "assistant", "content": response.content},
]
print(f"(Assistant): {response.content}")
#> (User): What fantasy book should I read?
#> (Assistant): Have you read the Name of the Wind?
#> (User): I have! What do you like about it?
#> (Assistant): I love the intricate world-building...We’ve made implementing and using tools (function calling) intuitive:
from typing import Literal
from mirascope.openai import OpenAICall, OpenAICallParams
def get_current_weather(
location: str, unit: Literal["celsius", "fahrenheit"] = "fahrenheit"
):
"""Get the current weather in a given location."""
if "tokyo" in location.lower():
print(f"It is 10 degrees {unit} in Tokyo, Japan")
elif "san francisco" in location.lower():
print(f"It is 72 degrees {unit} in San Francisco, CA")
elif "paris" in location.lower():
print(f"It is 22 degress {unit} in Paris, France")
else:
print("I'm not sure what the weather is like in {location}")
class Forecast(OpenAICall):
prompt_template = "What's the weather in Tokyo?"
call_params = OpenAICallParams(model="gpt-4", tools=[get_current_weather])
tool = Forecast().call().tool
if tool:
tool.fn(**tool.args)
#> It is 10 degrees fahrenheit in Tokyo, JapanChaining multiple calls together for Chain of Thought (CoT) is as simple as writing a function:
import os
from functools import cached_property
from mirascope.openai import OpenAICall, OpenAICallParams
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
class ChefSelector(OpenAICall):
prompt_template = "Name a chef who is really good at cooking {food_type} food"
food_type: str
call_params = OpenAICallParams(model="gpt-3.5-turbo-0125")
class RecipeRecommender(ChefSelector):
prompt_template = """
SYSTEM:
Imagine that you are chef {chef}.
Your task is to recommend recipes that you, {chef}, would be excited to serve.
USER:
Recommend a {food_type} recipe using {ingredient}.
"""
ingredient: str
call_params = OpenAICallParams(model="gpt-4")
@cached_property # !!! so multiple access doesn't make multiple calls
def chef(self) -> str:
"""Uses `ChefSelector` to select the chef based on the food type."""
return ChefSelector(food_type=self.food_type).call().content
response = RecipeRecommender(food_type="japanese", ingredient="apples").call()
print(response.content)
# > Certainly! Here's a recipe for a delicious and refreshing Japanese Apple Salad: ...Convenience built on top of tools that makes extracting structured information reliable:
from typing import Literal, Type
from mirascope.openai import OpenAIExtractor
from pydantic import BaseModel
class TaskDetails(BaseModel):
description: str
due_date: str
priority: Literal["low", "normal", "high"]
class TaskExtractor(OpenAIExtractor[TaskDetails]):
extract_schema: Type[TaskDetails] = TaskDetails
prompt_template = """
Extract the task details from the following task:
{task}
"""
task: str
task = "Submit quarterly report by next Friday. Task is high priority."
task_details = TaskExtractor(task=task).extract()
assert isinstance(task_details, TaskDetails)
print(TaskDetails)
#> description='Submit quarterly report' due_date='next Friday' priority='high'Since we’ve built our BasePrompt on top of Pydantic, we integrate with tools like FastAPI out-of-the-box:
import os
from typing import Type
from fastapi import FastAPI
from mirascope.openai import OpenAIExtractor
from pydantic import BaseModel
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
app = FastAPI()
class Book(BaseModel):
title: str
author: str
class BookRecommender(OpenAIExtractor[Book]):
extract_schema: Type[Book] = Book
prompt_template = "Please recommend a {genre} book."
genre: str
@app.post("/")
def root(book_recommender: BookRecommender) -> Book:
"""Generates a book based on provided `genre`."""
return book_recommender.extract()You can find a list of supported providers with examples of how to use them with Mirascope.
We are constantly working to further integrate Mirascope as seamlessly as possible with as many tools as possible. You can find the integrations that we currently support in our docs. If there are any integrations that you want, let us know!
- Agents
- Easy tool calling and execution
- More convenience around TOOL messages
- Base classes for ReAct agents
- Base classes for Query Planning agents
- Tons of examples...
- RAG
- ChromaDB
- Pinecone
- OpenAI Embeddings
- Cohere Embeddings
- Hugging Face
- Tons of examples...
- Mirascope CLI
- Versioning prompts / calls / extractors
- RAG CLI (e.g. versioning stores, one-off vector store interactions)
- Versioning integrations with LLMOps tools (e.g. Weave, LangSmith, ...)
- LLM Provider Auto-conversion
- Templates (
mirascope from_template pinecone_rag_openai_call my_call_name)
- Extracting structured information using LLMs
- Streaming extraction for tools (function calling)
- Additional template parsing for more complex messages
- Chat History
- List + List[List] Convenience
- Additional Metadata
- Vision
- Support for more LLM providers:
- Anthropic
- Cohere
- Mistral
- Groq
- Gemini
- HuggingFace
- Integrations
- Weights & Biases Trace
- Weave by Weights & Biases
- LangChain / LangSmith
- … tell us what you’d like integrated!
- Evaluating prompts and their quality by version
Mirascope uses Semantic Versioning.
This project is licensed under the terms of the MIT License.