declarai 0.1.8

Declarai, turning Python code into LLM tasks, easy to use, and production-ready.

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Documentation: https://declarai.com

Source Code: https://github.com/vendi-ai/declarai

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Using AI in your code shouldn't be difficult. Supporting the mission of bringing AI to the masses, Declarai is meant to abstract the know-how of prompt engineering and make using LLMs for daily programming accessible to everyone.

If you know how to write python code and have written any doc-string in your life, this should be a breeze.

🚀 Quickstart

Installation

pip install declarai

Setup

export DECLARAI_OPENAI_API_KEY=<your openai token>

or pass the token when initializing the declarai object

from declarai import Declarai

declarai = Declarai(provider="openai", model="gpt-3.5-turbo", openai_token="<your-openai-key>")

💡 Basic Usage

Craft AI-powered functionalities with ease using the @task decorator. Just add some type hints and a bit of documentation, and watch Declarai do its magic!

from declarai import Declarai

declarai = Declarai(provider="openai", model="gpt-3.5-turbo")

@declarai.task
def generate_poem(title: str) -> str:
    """
    Write a 4 line poem on the provided title
    """


res = generate_poem(
    title="Declarai, the declarative AI framework for LLMs"
)
print(res)
# Declarai, the AI framework,
# Empowers LLMs with declarative power,
# Efficiently transforming data and knowledge,
# Unlocking insights in every hour.

Not the best poem out there, but hey! You've written your first declarative AI code!

Declarai aims to promote clean and readable code by enforcing the use of doc-strings and typing. The resulting code is readable and easily maintainable.

🔍 Why Declarai?

• Intuitive Pythonic Interface: Use your Python expertise instead of wrestling with complex prompts.
• Lightweight: Minimal dependencies to avoid clutter.
• Extensible: Modify and customize as per your needs.
• Type-Driven Prompt Design: Automatic detailed prompts using Python's type annotations.
• Context-Rich Prompts via Docstrings: Make your tasks clear and improve LLM performance.
• Automated LLM Task Execution: Declarai manages the heavy lifting, letting you focus on the logic.

Features

Tasks with python native output parsing:

Python primitives

@declarai.task
def rank_by_severity(message: str) -> int:
    """
    Rank the severity of the provided message by it's urgency.
    Urgency is ranked on a scale of 1-5, with 5 being the most urgent.
    :param message: The message to rank
    :return: The urgency of the message
    """


rank_by_severity(message="The server is down!")

>>> 5
rank_by_severity(message="How was your weekend?"))

>>> 1

Python complex objects

@declarai.task
def datetime_parser(raw_date: str) -> datetime:
    """
    Parse the input into a valid datetime string of the format YYYY-mm-ddThh:mm:ss
    :param raw_date: The provided raw date
    :return: The parsed datetime output
    """


datetime_parser(raw_date="Janury 1st 2020"))

>>> 2020-01-01 00:00:00

pydantic models

class Animal(BaseModel):
    name: str
    family: str
    leg_count: int


@declarai.task
def suggest_animals(location: str) -> Dict[int, List[Animal]]:
    """
    Create a list of numbers from 0 to 5
    for each number, suggest a list of animals with that number of legs
    :param location: The location where the animals can be found
    :return: A list of animal leg count and for each count, the corresponding animals
    """


suggest_animals(location="jungle")

>>> {
       0: [
           Animal(name='snake', family='reptile', leg_count=0)
       ], 
       2: [
           Animal(name='monkey', family='mammal', leg_count=2), 
           Animal(name='parrot', family='bird', leg_count=2)
       ], 
       4: [
          Animal(name='tiger', family='mammal', leg_count=4), 
          Animal(name='elephant', family='mammal', leg_count=4)
       ]
 }

Simple Chat interface

@declarai.experimental.chat
class CalculatorBot:
    """
    You a calculator bot,
    given a request, you will return the result of the calculation
    """

    def send(self, message: str) -> int: ...


calc_bot = CalculatorBot()
calc_bot.send(message="1 + 1")

>>> 2

📚 For a thorough introduction, features, and best practices, explore our official documentation and beginner's guide.

Contributing 💼

Join our mission to make declarative AI even better together! Check out our contributing guide to get started.