Seamlessly integrate LLMs as Python functions
Project description
magentic
Easily integrate Large Language Models into your Python code. Simply use the @prompt
decorator to create functions that return structured output from the LLM. Mix LLM queries and function calling with regular Python code to create complex logic.
magentic
is
- Compact: Query LLMs without duplicating boilerplate code.
- Atomic: Prompts are functions that can be individually tested and reasoned about.
- Transparent: Create "chains" using regular Python code. Define all of your own prompts.
- Compatible: Use
@prompt
functions as normal functions, including with decorators like@lru_cache
. - Type Annotated: Works with linters and IDEs.
Continue reading for sample usage, or go straight to the examples directory.
Installation
pip install magentic
or using poetry
poetry add magentic
Configure your OpenAI API key by setting the OPENAI_API_KEY
environment variable or using openai.api_key = "sk-..."
. See the OpenAI Python library documentation for more information.
Usage
The @prompt
decorator allows you to define a template for a Large Language Model (LLM) prompt as a Python function. When this function is called, the arguments are inserted into the template, then this prompt is sent to an LLM which generates the function output.
from magentic import prompt
@prompt('Add more "dude"ness to: {phrase}')
def dudeify(phrase: str) -> str:
... # No function body as this is never executed
dudeify("Hello, how are you?")
# "Hey, dude! What's up? How's it going, my man?"
The @prompt
decorator will respect the return type annotation of the decorated function. This can be any type supported by pydantic including a pydantic
model.
from magentic import prompt
from pydantic import BaseModel
class Superhero(BaseModel):
name: str
age: int
power: str
enemies: list[str]
@prompt("Create a Superhero named {name}.")
def create_superhero(name: str) -> Superhero:
...
create_superhero("Garden Man")
# Superhero(name='Garden Man', age=30, power='Control over plants', enemies=['Pollution Man', 'Concrete Woman'])
An LLM can also decide to call functions. In this case the @prompt
-decorated function returns a FunctionCall
object which can be called to execute the function using the arguments provided by the LLM.
from typing import Literal
from magentic import prompt, FunctionCall
def activate_oven(temperature: int, mode: Literal["broil", "bake", "roast"]) -> str:
"""Turn the oven on with the provided settings."""
return f"Preheating to {temperature} F with mode {mode}"
@prompt(
"Prepare the oven so I can make {food}",
functions=[activate_oven],
)
def configure_oven(food: str) -> FunctionCall[str]:
...
output = configure_oven("cookies!")
# FunctionCall(<function activate_oven at 0x1105a6200>, temperature=350, mode='bake')
output()
# 'Preheating to 350 F with mode bake'
Sometimes the LLM requires making one or more function calls to generate a final answer. The @prompt_chain
decorator will resolve FunctionCall
objects automatically and pass the output back to the LLM to continue until the final answer is reached.
In the following example, when describe_weather
is called the LLM first calls the get_current_weather
function, then uses the result of this to formulate its final answer which gets returned.
from magentic import prompt_chain
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
# Pretend to query an API
return {
"location": location,
"temperature": "72",
"unit": unit,
"forecast": ["sunny", "windy"],
}
@prompt_chain(
"What's the weather like in {city}?",
functions=[get_current_weather],
)
def describe_weather(city: str) -> str:
...
describe_weather("Boston")
# 'The current weather in Boston is 72°F and it is sunny and windy.'
LLM-powered functions created using @prompt
and @prompt_chain
can be supplied as functions
to other @prompt
/@prompt_chain
decorators, just like regular python functions. This enables increasingly complex LLM-powered functionality, while allowing individual components to be tested and improved in isolation.
See the examples directory for more.
Chat Prompting
The @chatprompt
decorator works just like @prompt
but allows you to pass chat messages as a template rather than a single text prompt. This can be used to provide a system message or for few-shot prompting where you provide example responses to guide the model's output. Format fields denoted by curly braces {example}
will be filled in all messages - use the escape_braces
function to prevent a string being used as a template.
from magentic import chatprompt, AssistantMessage, SystemMessage, UserMessage
from magentic.chatprompt import escape_braces
from pydantic import BaseModel
class Quote(BaseModel):
quote: str
character: str
@chatprompt(
SystemMessage("You are a movie buff."),
UserMessage("What is your favorite quote from Harry Potter?"),
AssistantMessage(
Quote(
quote="It does not do to dwell on dreams and forget to live.",
character="Albus Dumbledore",
)
),
UserMessage("What is your favorite quote from {movie}?"),
)
def get_movie_quote(movie: str) -> Quote:
...
get_movie_quote("Iron Man")
# Quote(quote='I am Iron Man.', character='Tony Stark')
Streaming
The StreamedStr
(and AsyncStreamedStr
) class can be used to stream the output of the LLM. This allows you to process the text while it is being generated, rather than receiving the whole output at once.
from magentic import prompt, StreamedStr
@prompt("Tell me about {country}")
def describe_country(country: str) -> StreamedStr:
...
# Print the chunks while they are being received
for chunk in describe_country("Brazil"):
print(chunk, end="")
# 'Brazil, officially known as the Federative Republic of Brazil, is ...'
Multiple StreamedStr
can be created at the same time to stream LLM outputs concurrently. In the below example, generating the description for multiple countries takes approximately the same amount of time as for a single country.
from time import time
countries = ["Australia", "Brazil", "Chile"]
# Generate the descriptions one at a time
start_time = time()
for country in countries:
# Converting `StreamedStr` to `str` blocks until the LLM output is fully generated
description = str(describe_country(country))
print(f"{time() - start_time:.2f}s : {country} - {len(description)} chars")
# 22.72s : Australia - 2130 chars
# 41.63s : Brazil - 1884 chars
# 74.31s : Chile - 2968 chars
# Generate the descriptions concurrently by creating the StreamedStrs at the same time
start_time = time()
streamed_strs = [describe_country(country) for country in countries]
for country, streamed_str in zip(countries, streamed_strs):
description = str(streamed_str)
print(f"{time() - start_time:.2f}s : {country} - {len(description)} chars")
# 22.79s : Australia - 2147 chars
# 23.64s : Brazil - 2202 chars
# 24.67s : Chile - 2186 chars
Object Streaming
Structured outputs can also be streamed from the LLM by using the return type annotation Iterable
(or AsyncIterable
). This allows each item to be processed while the next one is being generated. See the example in examples/quiz for how this can be used to improve user experience by quickly displaying/using the first item returned.
from collections.abc import Iterable
from time import time
from magentic import prompt
from pydantic import BaseModel
class Superhero(BaseModel):
name: str
age: int
power: str
enemies: list[str]
@prompt("Create a Superhero team named {name}.")
def create_superhero_team(name: str) -> Iterable[Superhero]:
...
start_time = time()
for hero in create_superhero_team("The Food Dudes"):
print(f"{time() - start_time:.2f}s : {hero}")
# 2.23s : name='Pizza Man' age=30 power='Can shoot pizza slices from his hands' enemies=['The Hungry Horde', 'The Junk Food Gang']
# 4.03s : name='Captain Carrot' age=35 power='Super strength and agility from eating carrots' enemies=['The Sugar Squad', 'The Greasy Gang']
# 6.05s : name='Ice Cream Girl' age=25 power='Can create ice cream out of thin air' enemies=['The Hot Sauce Squad', 'The Healthy Eaters']
Asyncio
Asynchronous functions / coroutines can be used to concurrently query the LLM. This can greatly increase the overall speed of generation, and also allow other asynchronous code to run while waiting on LLM output. In the below example, the LLM generates a description for each US president while it is waiting on the next one in the list. Measuring the characters generated per second shows that this example achieves a 7x speedup over serial processing.
import asyncio
from time import time
from typing import AsyncIterable
from magentic import prompt
@prompt("List ten presidents of the United States")
async def iter_presidents() -> AsyncIterable[str]:
...
@prompt("Tell me more about {topic}")
async def tell_me_more_about(topic: str) -> str:
...
# For each president listed, generate a description concurrently
start_time = time()
tasks = []
async for president in await iter_presidents():
# Use asyncio.create_task to schedule the coroutine for execution before awaiting it
# This way descriptions will start being generated while the list of presidents is still being generated
task = asyncio.create_task(tell_me_more_about(president))
tasks.append(task)
descriptions = await asyncio.gather(*tasks)
# Measure the characters per second
total_chars = sum(len(desc) for desc in descriptions)
time_elapsed = time() - start_time
print(total_chars, time_elapsed, total_chars / time_elapsed)
# 24575 28.70 856.07
# Measure the characters per second to describe a single president
start_time = time()
out = await tell_me_more_about("George Washington")
time_elapsed = time() - start_time
print(len(out), time_elapsed, len(out) / time_elapsed)
# 2206 18.72 117.78
Additional Features
- The
functions
argument to@prompt
can contain async/coroutine functions. When the correspondingFunctionCall
objects are called the result must be awaited. - The
Annotated
type annotation can be used to provide descriptions and other metadata for function parameters. See the pydantic documentation on usingField
to describe function arguments. - The
@prompt
and@prompt_chain
decorators also accept amodel
argument. You can pass an instance ofOpenaiChatModel
to use GPT4 or configure a different temperature. See below.
Backend/LLM Configuration
Currently two backends are available
openai
: the default backend that uses theopenai
Python package. Supports all features.litellm
: uses thelitellm
Python package to enable querying LLMs from many different providers. Install this withpip install magentic[litellm]
. Note: some models may not support all features ofmagentic
e.g. function calling/structured output and streaming.
The backend and LLM used by magentic
can be configured in several ways. The order of precedence of configuration is
- Arguments explicitly passed when initializing an instance in Python
- Values set using a context manager in Python
- Environment variables
- Default values from src/magentic/settings.py
from magentic import OpenaiChatModel, prompt
from magentic.chat_model.litellm_chat_model import LitellmChatModel
@prompt("Say hello")
def say_hello() -> str:
...
@prompt(
"Say hello",
model=LitellmChatModel("ollama/llama2"),
)
def say_hello_litellm() -> str:
...
say_hello() # Uses env vars or default settings
with OpenaiChatModel("gpt-3.5-turbo", temperature=1):
say_hello() # Uses openai with gpt-3.5-turbo and temperature=1 due to context manager
say_hello_litellm() # Uses litellm with ollama/llama2 because explicitly configured
The following environment variables can be set.
Environment Variable | Description | Example |
---|---|---|
MAGENTIC_BACKEND | The package to use as the LLM backend | openai |
MAGENTIC_LITELLM_MODEL | LiteLLM model | claude-2 |
MAGENTIC_LITELLM_API_BASE | The base url to query | http://localhost:11434 |
MAGENTIC_LITELLM_MAX_TOKENS | LiteLLM max number of generated tokens | 1024 |
MAGENTIC_LITELLM_TEMPERATURE | LiteLLM temperature | 0.5 |
MAGENTIC_OPENAI_MODEL | OpenAI model | gpt-4 |
MAGENTIC_OPENAI_API_KEY | OpenAI API key to be used by magentic | sk-... |
MAGENTIC_OPENAI_API_TYPE | Allowed options: "openai", "azure" | azure |
MAGENTIC_OPENAI_BASE_URL | Base URL for an OpenAI-compatible API | http://localhost:8080 |
MAGENTIC_OPENAI_MAX_TOKENS | OpenAI max number of generated tokens | 1024 |
MAGENTIC_OPENAI_SEED | Seed for deterministic sampling | 42 |
MAGENTIC_OPENAI_TEMPERATURE | OpenAI temperature | 0.5 |
When using the openai
backend, setting the MAGENTIC_OPENAI_BASE_URL
environment variable or using OpenaiChatModel(..., base_url="http://localhost:8080")
in code allows you to use magentic
with any OpenAI-compatible API e.g. Azure OpenAI Service, LiteLLM OpenAI Proxy Server, LocalAI. Note that if the API does not support function calling then you will not be able to create prompt-functions that return Python objects, but other features of magentic
will still work.
To use Azure with the openai backend you will need to set the MAGENTIC_OPENAI_API_TYPE
environment variable to "azure" or use OpenaiChatModel(..., api_type="azure")
, and also set the environment variables needed by the openai package to access Azure. See https://github.com/openai/openai-python#microsoft-azure-openai
Type Checking
Many type checkers will raise warnings or errors for functions with the @prompt
decorator due to the function having no body or return value. There are several ways to deal with these.
- Disable the check globally for the type checker. For example in mypy by disabling error code
empty-body
.# pyproject.toml [tool.mypy] disable_error_code = ["empty-body"]
- Make the function body
...
(this does not satisfy mypy) orraise
.@prompt("Choose a color") def random_color() -> str: ...
- Use comment
# type: ignore[empty-body]
on each function. In this case you can add a docstring instead of...
.@prompt("Choose a color") def random_color() -> str: # type: ignore[empty-body] """Returns a random color."""
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