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Вызов функций с помощью LLM

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Всем привет, меня зовут Алан, я разработчик-исследователь из команды фундаментальных исследований MTS AI. Мы изучаем возможности генеративного ИИ, и видим, что большие языковые модели отлично справляются с различными текстовыми задачами, но мы можем расширить их функционал. Например, пока что LLM не может правильно посчитать логарифм, узнать погоду или какую-то другую информацию. Как решить эту задачу? Нужно научить модель пользоваться внешними инструментами/функциями. В этой статье мы поговорим о вызове функций с помощью больших языковых моделей, рассмотрим некоторые проприетарные и открытые модели, связанные исследования, а затем проведем небольшой эксперимент с отправкой электронной почты при помощи LLM.

Что нужно знать про function calling

Вызов функций или Function calling — это концепция, которая позволяет LLM использовать внешние инструменты. Как уже было сказано ранее, LLM зачастую просто не умеют выполнять какие-то операции, и для этого нам придется научить языковую модель вызывать функции при необходимости.

Такие LLM как GPT-4 и GPT-3.5 были дообучены самостоятельно определять, когда необходимо вызвать функцию, а затем генерировать JSON, содержащий имя нужной функции, и аргументы для вызова этой функции. Например, запрос «Какая погода в Москве?» будет преобразован в вызов функции get_current_weather(location: string, unit: 'celsius' | 'fahrenheit')

Базовая схема вызова функций

Схема вызова функций является общей для всех языковых моделей и выглядит следующим образом:

  1. Передаем в LLM промпт пользователя и описание доступных функций/инструментов.

  2. Модель сопоставляет промпт пользователя с описанием функций. Если LLM решает, что для выполнения запроса требуется вызов одной или нескольких функций, то она возвращает JSON с именем и аргументами функции (модель может их придумать).

  3. Вызываем функцию в коде.

  4. Передаем результаты выполнения функции обратно ассистенту, затем он генерирует ответ с суммаризацией результатов (если они есть).

Общая схема работы
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Схема работы

Большинство проприетарных function calling LLM умеют работать в нескольких режимах:

  • режим Auto позволяет языковой модели решить, требуется ли вызывать функцию и вернуть JSON или сгенерировать ответ на инструкцию пользователя;

  • в режиме Required модель всегда будет вызывать одну или несколько функций;

  • None предполагает, что модель не будет вызывать какие-либо функции

Также модель можно "заставить" вызывать одну конкретную функцию.

Существующие решения

Возможность вызова функций есть у многих языковых моделей как проприетарных, так и опенсорсных. В целом их возможности одинаковы, отличается лишь формат предоставления данных.

OpenAI

API Chat Completions не вызывает функцию, вместо этого модель генерирует JSON, который мы можем использовать для вызова функции в своем коде. Такие модели, как gpt-4o, gpt-4-turbo и gpt-3.5-turbo, обучены определять, когда следует вызывать функцию (в зависимости от входных данных), а также умеют возвращать JSON, который соответствует нужной функции.

OpenAI настоятельно рекомендует создавать этапы подтверждения со стороны пользователей, прежде чем предпринимать действия, которые могут повлиять на мир (отправка электронной почты, публикация чего-либо в Интернете, совершение покупок и т. д.).

Формат описания передаваемой в модель функции выглядит следующим образом (в данном примере это вызов Weather API):

Формат описания функции
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]

Gemini

При использовании Gemini API мы создаем одну или несколько функций, а затем добавляем их в tools object, передаваемый модели. Каждое описание функции включает в себя следующее:

  • Имя функции

  • Параметры функции

  • Описание функции

Gemini API также поддерживает параллельный вызов функций, при котором мы можем вызвать несколько функций за раз.

На данный момент function calling поддерживают следующие модели:

  • gemini-1.0-pro

  • gemini-1.0-pro-001

  • gemini-1.5-flash-latest

  • gemini-1.5-pro-latest

Mistral-7B-Instruct-v0.3🤗

Новая версия мистраля mistralai/Mistral-7B-Instruct-v0.3 теперь также поддерживает function calling с помощью библиотеки mistral_inference:

Пример с mistral_inference 
from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.model import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(
    tools=[
        Tool(
            function=Function(
                name="get_current_weather",
                description="Get the current weather",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city",
                        },
                        "format": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "The temperature unit to use. Infer this from the users location.",
                        },
                    },
                    "required": ["location", "format"],
                },
            )
        )
    ],
    messages=[
        UserMessage(content="What's the weather like today in Moscow?"),
        ],
)

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

Gorilla🤗

Gorilla OpenFunctions-v2 — это модель примерно с 7 миллиардами параметров, дообученная на основе Deepseek-Coder-7B-Instruct-v1.5 6.91B. Данные, на которых обучалась модель, представляют собой 65 283 сэмпла вопрос-функция-ответ из разных источников: Python packages (19 353), репозиториев Java (16 586), репозиториев Javascript (4 245), общедоступных API (6 009) и инструментов командной строки (19 090)

После сбора данных была проведена аугментация, чтобы разнообразить датасет. Во-первых, для того, чтобы модель не запоминала маппинг API, были изменены названия функций. Во-вторых, были добавлены сэмплы с параллельным вызовом функций. Также были добавлены примеры, где переданных в запросе функций недостаточно для решения пользовательской задачи

Glaive🤗

glaive-function-calling-v1 — это модель с открытым исходным кодом с 2,7 млрд параметров, обученная на синтетических данных. Модель способна вести диалог и понимает когда необходимо вызвать функцию (предоставляемую в начале диалога в виде системной подсказки). Модель обучена на основе модели https://huggingface.co/replit/replit-code-v1-3b.

Набор данных Glaive состоит из 52 тысяч сэмплов в следующем формате

SYSTEM: You are an helpful assistant who has access to the following functions to help the user, you can use the functions if needed-
{
  JSON function definiton
}
USER: user message
ASSISTANT: assistant message

Function call invocations are formatted as-

ASSISTANT: <functioncall> {json function call}

Response to the function call is formatted as-
FUNCTION RESPONSE: {json function response}

NexusRaven🤗

Nexusflow/NexusRaven-V2-13B- это LLM, дообученная на синтетических данных на основе CodeLlama-13B. Модель показывает хорошую производительность относительно GPT- 4 на function calling бенчмарках

API accuracy на различных задачах
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Functionary 🤗

Functionary — это языковая модель, которая может интерпретировать и выполнять функции/плагины. Модель определяет, когда выполнять функции, как именно — параллельно или последовательно — и может понимать результат их выполнения. Functionary запускает функции только по мере необходимости. Определения функций предоставляются в виде JSON, аналогично вызовам функций с помощью OpenAI.

Помимо этого functionary отлично показывает себя относительно других моделей в задачах поддержки диалога и генерации ответа на основе результатов выполнения функций

Сравнение моделей на задачах function calling
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Связанные исследования

Далее мы коротко рассмотрим различные подходы для вызова функций из нескольких статей, которые касаются темы function calling

Toolformer

Toolformer — это модель, дообученная решать, какие API вызывать, когда их вызывать, какие аргументы передавать и как лучше всего включать результаты в генерируемую последовательность. Это производится с помощью метода self-supervised, который требует несколько демонстраций для каждого API. Toolformer обеспечивает существенное улучшение производительности при выполнении множества задач, конкурируя с более крупными моделями, при этом не жертвуя языковым моделированием

Ссылка на статью

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Chain of Tools

Самая крутая работа (на мой взгляд), которая фокусируется на вызове цепочки функций, где вызов каждой последующей функция зависит от результатов работы предыдущей. Задача планирования решается с помощью генерации специальной программы, которая будет последовательно запускать необходимые функции, как это показано на рисунке (справа). Для имплементации подойдут LLM, которые умеют хорошо генерировать код.

Ссылка на статью

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TPTU-v2

TPTU-v2 это фреймворк, состоящий из трех основных компонентов

  1. API Retriever. Как понятно из названия, этот компонент находит наиболее релевантные пользовательскому запроса API

  2. LLMFinetuner. Этот компонент файнтюнит LLM с помощью тщательно подобранного датасета, расширяя возможности модели по планированию и эффективному выполнению вызовов API

  3. Demo Selector. Demo selector динамически извлекает демонстрации, связанные с трудно распознаваемыми вызовами API, что облегчает контекстное обучение для LLM

Ссылка на статью

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EasyTool

EasyTool — это платформа, преобразующая документацию по внешним инструментам в краткую инструкцию, упрощающую их использование агентами. EasyTool фильтрует важную информацию из документации и генерирует унифицированный интерфейс, предлагающий стандартизированные описания инструментов и функциональные возможности для LLM-агентов. Эксперименты с множеством различных задач показывают, что EasyTool может значительно сократить расход токенов и повысить производительность LLM-агентов при вызове функций в реальных сценариях.

Ссылка на статью

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ToolQA

ToolQA — это бенчмарк, предназначенный для оценки способности LLM использовать внешние инструменты для ответов на вопросы. ToolQA включает данные из 8 доменов и определяет 13 типов инструментов для извлечения информации из внешних источников. Каждый сэмпл в ToolQA состоит из вопроса, ответа, источника данных и списка доступных инструментов. Бенчмарк ToolQA уникален тем, что на все его вопросы можно ответить только используя соответствующие инструменты для извлечения информации из источника данных. Это сводит к минимуму возможность того, что LLM будут отвечать на вопросы, просто извлекая свои внутренние знания, и это позволяет достоверно оценить способности LLM в использовании инструментов

Ссылка на статью

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Эксперименты

В этой части статьи мы покажем, как уже сейчас попробовать вызов функций с помощью одной проприетарной модели от Google и нескольких решений с открытым исходным кодом. Во всех экспериментах мы будем отправлять почту с помощью библиотеки smtplib.

Gemini function calling

Для работы с Gemini понадобиться использование VPN стран из разрешенных локаций (например Япония)

Для начала установим библиотеку для работы с генеративным ИИ от Google

pip install -U -q google-generativeai

Импортируем все необходимое

import textwrap
import google.generativeai as genai
from IPython.display import Markdown

from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build

import smtplib as smtp
from getpass import getpass

def to_markdown(text):
  text = text.replace('•', '  *')
  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))

Сгенерируем и сконфигурируем свой API key

genai.configure(api_key=api_key)

Определим функции, которые Gemini сможет вызывать по необходимости

def multiply(a:float, b:float):
    """returns a * b."""
    return a*b



def send_email(destination_email: str, message_text: str):
    """Send message to destination_email with message_text"""

    email = 'your_yandex_mail'
    password = 'your_password'
    dest_email = destination_email
    subject = 'Test'
    email_text = message_text

    message = 'From: {}\nTo: {}\nSubject: {}\n\n{}'.format(email,
                                                        dest_email, 
                                                        subject, 
                                                        email_text)

    server = smtp.SMTP_SSL('smtp.yandex.com')
    server.set_debuglevel(1)
    server.ehlo(email)
    server.login(email, password)
    server.auth_plain()
    server.sendmail(email, dest_email, message)
    server.quit()

Передаем функции в модель (будем использовать gemini-1.5-flash)

model = genai.GenerativeModel(model_name='gemini-1.5-flash',
                              tools=[multiply, send_email])

Инициализируем чат и отправляем запрос

chat = model.start_chat(enable_automatic_function_calling=True)
response = chat.send_message('I have 57 cats, each owns 44 mittens, how many mittens is that in total?')
response.text
>>>"That's a total of 2508 mittens. \n

Как мы видим, Gemini воспользовалась функцией перемножения двух чисел, которую мы определили ранее. Теперь попробуем отправить сообщение:

response = chat.send_message('Send a message to [email protected] about the MTS AI meeting on June 13. And tell him to call my number back. Be kind')
response.text

В результате мне на почту прилетело следующее письмо(в спойлере)

Пример работы Gemini function calling

Давайте посмотрим что происходит по капотом:

for content in chat.history:
    part = content.parts[0]
    print(content.role, "->", type(part).to_dict(part))
    print('-'*80)

>>> user -> {'text': 'Send a message to [email protected] about the MTS AI meeting on June 13. And tell him to call my number back. Be kind'}
>>> --------------------------------------------------------------------------------
>>> model -> {'function_call': {'name': 'send_email', 'args': {'message_text': 'Hi Alan, this is Newton. Just a friendly reminder about the MTS AI meeting on June 13th. Please give me a call back at your earliest convenience. Thanks!', 'destination_email': '[email protected]'}}}
>>> --------------------------------------------------------------------------------
>>> user -> {'function_response': {'name': 'send_email', 'response': {'result': None}}}
>>> --------------------------------------------------------------------------------
>>> model -> {'text': "OK. I've sent an email to [email protected].  Let me know if you'd like me to add anything else to the message. \n"}
>>> --------------------------------------------------------------------------------

Google.generativeai сама выполняет функции под капотом, нам не требуется отдельно запускать функции на основе вывода модели. Помимо этого gemini продолжает работать в режиме диалога и генерирует дополнительный текст, который говорит нам о том, что сообщение отправлено

gorilla-llm function calling

В этом и следующих примерах мы попробуем вызвать функцию с помощью моделей с отрытым исходным кодом.

Для начала импортируем все необходимое и проинициализуем модель с токенайзером. Мы будем использовать модель gorilla-llm/gorilla-openfunctions-v2

import json
import smtplib as smtp
from getpass import getpass
from transformers import AutoTokenizer, AutoModelForCausalLM
import re
import ast

tokenizer = AutoTokenizer.from_pretrained("gorilla-llm/gorilla-openfunctions-v2")
model = AutoModelForCausalLM.from_pretrained("gorilla-llm/gorilla-openfunctions-v2")

Функция отправки сообщений из предыдущего примера никак не меняется. В отличие от работы с Gemini, теперь нам придется предоставить LLM понятное для нее описание функции:

functions = [
    {
        "name": "send_email",
        "description": "Send message to user_email with message_text",
        "parameters": {
            "type": "object",
            "properties": {
                "destination_email": {
                    "type": "string",
                    "description": "Destination email",
                },
                "message_text": {
                    "type": "string",
                    "description": "Text of message",
                },
            },
            "required": ["destination_email", "message_text"],
        },
    }
]

Преобразуем промпт, для того чтобы подать в модель описание функции и инструкцию пользователя:

def get_prompt(user_query: str, functions: list = []) -> str:
    """
    Generates a conversation prompt based on the user's query and a list of functions.

    Parameters:
    - user_query (str): The user's query.
    - functions (list): A list of functions to include in the prompt.

    Returns:
    - str: The formatted conversation prompt.
    """
    system = "You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
    if len(functions) == 0:
        return f"{system}\n### Instruction: <<question>> {user_query}\n### Response: "
    functions_string = json.dumps(functions)
    return f"{system}\n### Instruction: <<function>>{functions_string}\n<<question>>{user_query}\n### Response: "

  
sentence = get_prompt("Send a message to [email protected] about the MTS AI meeting on June 13. And tell him to call my number back. Be kind", functions=functions)

Теперь промпт, подаваемый в модель, выглядит следующим образом:

You are an AI programming assistant, utilizing the Gorilla LLM model, 
developed by Gorilla LLM, and you only answer questions related to computer science. 
For politically sensitive questions, security and privacy issues, 
and other non-computer science questions, you will refuse to answer.
### Instruction: <<function>>[
    {
        "name": "send_email",
        "description": "Send message to user_email with message_text",
        "parameters": {
            "type": "object",
            "properties": {
                "destination_email": {
                    "type": "string",
                    "description": "Destination email",
                },
                "message_text": {
                    "type": "string",
                    "description": "Text of message",
                },
            },
            "required": ["destination_email", "message_text"],
        },
    }
<<question>>Send a message to [email protected] about the MTS AI meeting on June 13. 
And tell him to call my number back. Be kind
### Response: '

Запускаем генерацию модели:

tokenized = tokenizer(sentence, return_tensors='pt')
res = model.generate(**tokenized, max_length=500)

Вывод модели выглядит следующим образом:

"send_email(
destination_email='[email protected]', 
message_text='Hello, this is a reminder about the MTS AI meeting on June 13. Please call me back. Best, [Your Name]'
)"

Вызываем функцию на основе вывода модели:

def call_function(res):
    responce = tokenizer.decode(res[0], skip_special_tokens=True)
    func_call = responce.split("<<function>>")[-1]
    func_name = func_call.split("(")[0]
    str_args = "{" + func_call.split("(")[1].replace("=", ":").replace(")", "}")
    input_str = str_args
    pattern = r"(\w+):"
    output_str = re.sub(pattern, r'"\1":', input_str)
    args = ast.literal_eval(output_str)

    locals()[func_name](**args)

Результат вызова функции:

Пример работы gorilla function calling

Модель справляется с вызовом функции, но при этом никак не поясняет результат своей работы

Glaive function calling

Импорты и определение функции для отправки сообщений ничем не отличаются, поэтому сразу перейдем к инициализации модели и токенайзера

tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-function-calling-v1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-function-calling-v1", trust_remote_code=True).half().cuda()

Определим промпт, в котором мы также опишем функцию отправки сообщений:

prompt = """
SYSTEM: You are an helpful assistant who has access to the following functions to help the user, you can use the functions if needed-
{
            "name": "send_email",
            "description": "Plan a holiday based on user's interests",
            "parameters": {
                "type": "object",
                "properties": {
                    "destination_email": {
                        "type": "string",
                        "description": "Destination email",
                    },
                    "message_text": {
                        "type": "string",
                        "description": "Text of message",
                    },
                },
                "required": ["destination_email", "message_text"],
            },
}
USER: Send a message to [email protected] about the MTS AI meeting on June 13. And tell him to call my number back. Be kind

"""

Произведем вызов функции:

def function_calling(prompt):
    inputs = tokenizer(prompt,return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=200)
    res = tokenizer.decode(outputs[0],skip_special_tokens=True)
    res = res.split("<functioncall>")[-1]
    res = json.loads(res)
    locals()[res["name"]](**res["arguments"])

Результат работы function calling:

Пример работы glaive function calling

Помимо того, что glaive справилась с задачей, она также сгенерировала более удобный JSON, который требует меньше преобразований перед вызовом функции в коде

NexusRaven function calling

Воспользуемся пайплайном из библиотеки transformers

from transformers import pipeline


pipeline = pipeline(
    "text-generation",
    model="Nexusflow/NexusRaven-V2-13B",
    torch_dtype="auto",
    device_map="auto",
)

Определим функции следующим образом:

prompt_template = \
'''
Function:
def get_weather_data(coordinates):
    """
    Fetches weather data from the Open-Meteo API for the given latitude and longitude.

    Args:
    coordinates (tuple): The latitude of the location.

    Returns:
    float: The current temperature in the coordinates you've asked for
    """

Function:
def get_coordinates_from_city(city_name):
    """
    Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API.

    Args:
    city_name (str): The name of the city.

    Returns:
    tuple: The latitude and longitude of the city.
    """
    
Function:
def send_email(destination_email, message_text):
    """
    Send message to destination_email with message_text
    
    Args:
    destination_email (str): Destination email
    message_text (str): Text of message
    """


User Query: {query}<human_end>

'''

Запускаем генерацию:

prompt = prompt_template.format(query="Send a message to [email protected] about the MTS AI meeting on June 13. And tell him to call my number back. Be kind")

result = pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0]["generated_text"]
print (result)

В результате работы модель выдает много лишнего текста:

Пример работы NexusRaven function calling

В результате работы модель выдает много лишнего текста

Заключение

В статье мы рассмотрели, как с помощью function calling LLM-агенты получают удобный способ отправлять письма, получать доступ к API, управлять роботами и т.д. Использование LLM, которые могут вызывать функции, дает большие возможности для создания различных ИИ-приложений

Ноутбуки с кодом отправки электронной почты с помощью LLM доступны по ссылке:

https://github.com/mts-ai/function-calling

Спасибо за внимание

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  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

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  • 23.09.24 18:56 matthewshimself

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  • 01.10.24 14:54 Sinewclaudia

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  • 02.10.24 22:27 Emily Hunter

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

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  • 31.10.24 00:13 ytre89

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

  • 02.11.24 14:44 diannamendoza732

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  • 12.11.24 00:50 TERESA

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  • 17.11.24 09:31 Vivianlocke223

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  • 19.11.24 03:06 [email protected]

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  • 19.11.24 03:07 [email protected]

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  • 21.11.24 04:14 ronaldandre617

    Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886  Email digital tech guard . com  Telegram: digital tech guard recovery . com  website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!

  • 21.11.24 08:02 Emily Hunter

    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

  • 22.11.24 15:26 cliftonhandyman

    Your Lost Bitcoins Are Not Gone Forever? Enquire From iBolt Cyber Hacker iBolt Cyber Hacker is a cybersecurity service that specializes in Bitcoin and cryptocurrency recovery. Even if your Bitcoin is locked away in a scammer inaccessible wallet, they have the tools and expertise to retrieve it. Many people, including seasoned cryptocurrency investors, face the daunting possibility of never seeing their lost funds again. iBolt cyber hacker service is a potential lifeline in these situations. I understand the concerns many people might have about trusting a third-party service to recover their Bitcoin. iBolt Cyber Hacker takes security seriously, implementing encryption and stringent privacy protocols. I was assured that no sensitive data would be compromised during the recovery process. Furthermore, their reputation in the cryptocurrency community, based on positive feedback from previous clients, gave me confidence that I was in good hands. Whtp +39, 351..105, 3619 Em.ail: ibolt @ cyber- wizard. co m

  • 22.11.24 23:43 teresaborja

    all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets.   ENAIL < Tech cybers force recovery @ cyber services. com >   WEBSITE < ht tps : // tech cyber force recovery. info  >   TEXT < +1. 561. 726. 3697 >

  • 24.11.24 02:21 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

  • 25.11.24 02:19 briankennedy

    COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY

  • 25.11.24 02:20 briankennedy

    After countless hours of research and desperate attempts to find a solution, I stumbled upon FASTFUND RECOVERY. It was like finding an oasis in the middle of a desert. Their website promised to help victims of scams reclaim what was rightfully theirs, and I instantly knew I had to give them a shot. Before diving headfirst into the recovery process, I wanted to make sure that FASTFUND RECOVERY was the real deal. So, I did my due diligence and looked into their expertise and reputation. To my relief, I found that they had an impeccable track record, successfully assisting countless individuals in recovering their lost funds. Their team consisted of experts in cybersecurity and financial fraud, armed with the knowledge and tools needed to tackle even the most intricate scams. With their reputation preceding them, I felt a renewed sense of hope. FASTFUND RECOVERY successfully came to my aid and got back the amount I lost to these scammers and for this, I am sending this article for clarification. The info of FASTFUND RECOVERY is email: Fastfundrecovery8 (@)Gmail (.) com. Web fastfundrecovery(.)com. (W/A 1 807/500/7554)

  • 26.11.24 21:59 [email protected]

    In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com

  • 26.11.24 23:12 rickrobinson8

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  • 26.11.24 23:12 rickrobinson8

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  • 00:39 [email protected]

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