declarai

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文檔：https：//declarai.com

源代碼：https：//github.com/vendi-ai/declarai

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什麼是declarai？

Declarai將您的Python代碼轉換為LLM任務，使您可以輕鬆地將LLM集成到現有的代碼庫中。它以一個簡單的原則運行：只需定義python函數/類即可。通過使用Docstrings和類型提示來註釋此功能，您可以為AI模型提供明確的說明集，而無需其他努力。

聲明函數後，Declarai會智能編譯該函數的Docstrings，並將提示鍵入AI模型的提示，以確保模型確切地了解所需的內容。

執行任務後，Declarai檢索AI的響應並解析它，然後將其轉換為Python函數的返回類型。這消除了您的任何手動解析或後處理。

Declarai將其保留為本地：從其核心中，Declarai是關於擁抱本地Python實踐的核心。您無需學習新的語法或適應其他編碼範式。只需像往常一樣編寫Python函數，然後讓Declarai無縫處理AI集成即可。

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主要組成部分？

任務

AI任務用於任何業務邏輯或轉換。

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . 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

聊天 ？

AI聊天用於與AI模型的迭代對話，AI模型可以記住以前的消息和上下文。

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . experimental . chat
class SQLBot :
    """
    You are a sql assistant. You help with SQL related questions 
    """


sql_bot = SQLBot ()
sql_bot . send ( "When should I use a LEFT JOIN?" )

> " You should use a LEFT JOIN when you want to return all rows from ....

特徵：

• ？智能提示：使用類型提示和DocStrings自動生成提示。
• ？會話AI ：配備內存和上下文管理的聊天接口。
• ⚡實時流：流LLM響應需要更長的時間才能完成。
• Pydantic模型解析：將LLM響應無縫解析為Pydantic模型。
• ？ Pythonic ：對Python原語的LLM響應的本地理解和解析。
• ？多個AI後端：與OpenAI和Azure AI LLM提供商集成。
• ？中間件：通過模塊化中間件系統調整和擴展任務行為。
• ？即將推出：與Huggingface Hub集成

Quickstart

安裝

pip install declarai

設定

 export OPENAI_API_KEY= < your openai token >

或初始化dectarai對象時通過令牌

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" , openai_token = "<your-openai-key>" )

基本用法

使用@task裝飾器輕鬆製作AI驅動功能。只需添加一些類型的提示和一些文檔，然後觀看Declarai做魔術！

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . 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.

不是那裡最好的詩，但是嘿！您已經寫了第一個聲明性的AI代碼！

Declarai的目的是通過強制使用DOC串和鍵入來促進清潔和可讀的代碼。結果代碼是可讀的，易於維護。

Python本機輸出解析的任務

python原語

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . 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列表/dicts等。

 from typing import List
import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . task
def multi_value_extraction ( text : str ) -> List [ str ]:
    """
    Extract the phone numbers from the provided text
    :param text: content to extract phone number from
    :return: The phone numbers that where identified in the input text
    """


multi_value_extraction (
    text = "Hey jenny, n you can call me at 124-3435-132. n "
         "you can also reach me at [phone]"
)
>> > [ '124-3435-132' , '[phone]' ]

Python複雜對象

 from datetime import datetime
import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . 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模型

 from pydantic import BaseModel
from typing import List , Dict
import declarai


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

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . 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 )
       ]
 }

Jinja模板？

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . task
def sentiment_classification ( string : str , examples : List [ str , int ]) -> int :
    """
    Classify the sentiment of the provided string, based on the provided examples.
    The sentiment is ranked on a scale of 1-5, with 5 being the most positive.
    {% for example in examples %}
    {{ example[0] }} // {{ example[1] }}
    {% endfor %}
    {{ string }} //
    """
    
sentiment_classification ( string = "I love this product but there are some annoying bugs" ,
                         examples = [[ "I love this product" , 5 ], [ "I hate this product" , 1 ]])

>> > 4

簡單的聊天接口

 import declarai

gpt_35 = declarai . openai ( model = "gpt-3.5-turbo" )

@ gpt_35 . 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

有關詳盡的介紹，功能和最佳實踐，請探討我們的官方文檔和初學者指南。

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