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LangChain vs LlamaIndex: проектируем RAG и разбираемся, что выбрать для вашего проекта

Сегодня it-сообщество предлагает большое количество любопытных инструментов для создания RAG-систем. Среди них особенно выделяются два фреймворка — LangChain и LlamaIndex. Как понять, какой из них подходит лучше для вашего проекта? Давайте разбираться вместе!

Меня зовут София, я сотрудница компании Raft, работаю на стыке backend и ML. Сегодня мы затронем сразу несколько вопросов. План таков:

Обсудим, что такое RAG и зачем он нужен;
Рассмотрим side-by-side, как написать простую реализацию чат-бота на основе RAG с помощью каждого из фреймворков (LangChain и LlamaIndex);
Поговорим о преимуществах и слабых местах каждого из инструментов.

Что такое RAG?

Начнем с того, что освежим в памяти основные понятия. RAG (Retrieval-Augmented Generation) — это метод, позволяющий большим языковым моделям (LLM) улучшать качество ответов за счет использования актуальной информации из внешних источников, таких как базы данных, документы или API. Этот подход снижает вероятность ошибок модели (галлюцинаций) и обеспечивает более точные и контекстуально обоснованные ответы, даже если сама модель обучалась на устаревших данных.

Что у RAG внутри? Концептуально можно выделить несколько ключевых компонентов и этапов, которые обеспечивают работу RAGа:

Ввод пользовательского запроса.
Запрос преобразуется в векторное представление (эмбеддинг), которое математически описывает смысл запроса. Обычно используется предобученная модель для генерации эмбеддингов, например, мы сегодня будем использовать модель от OpenAI text-embedding-ada-002.
Далее вектор запроса сравнивается с хранящимися в базе векторами для поиска наиболее релевантных данных. В качестве баз для хранения часто используют Qdrant, Pinecone или Weaviate.
Из базы данных извлекаются фрагменты текста или документов, которые лучше всего соответствуют запросу. Эти данные формируют контекст для ответа.
LLM получает извлеченный контекст и запрос. Из этих данных она генерирует ответ, который после возвращается пользователю.

Когда дело касается продуктовых решений, важно помнить о вопросах безопасности и оптимизации. Чтобы сделать RAG более продвинутым и эффективным, можно добавить предобработку пользовательского запроса и постобработку ответа модели. Что касается безопасности, я всегда рекомендую при использовании LLM где-либо ознакомиться с топом уязвимостей LLM от OWASP.

LangChain и LlamaIndex

Тема с использованием RAG далеко не нова, впервые об этом подходе заговорили еще в 2020 году. За такой короткий срок сфера получила огромный буст в развитии, а также появились и успели себя зарекомендовать инструменты для эффективной разработки RAG-систем.

Одним из таких фреймворков является LangChain. Фреймворк был представлен в 2022 году. Он быстро стал популярным инструментом для специалистов, работающих с языковыми моделями. Большинство разработчиков любят его за гибкость и широким возможностям для интеграций. Забегая вперед, скажу, что LangChain более универсальный инструмент в сравнении с LlamaIndex.

Чтобы соорудить минимальную реализацию RAG с помощью LangChain, мне потребовалось примерно 10 минут и 25 строчек кода на питоне:

from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAI, OpenAIEmbeddings

text_data = [
    "Котики — любимые животные Сони.",
    "Котики не любят цитрусовые.",
    "Котики ласковые, усатые и пугливые."
]

embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_texts(text_data, embeddings)

retriever = vector_store.as_retriever()
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(temperature=0),
    retriever=retriever
)

query = "Что ты знаешь о котиках?"
response = qa_chain.invoke(query)
print("Ответ:", response["result"])

После выполнения кода получаю вот такой ответ:

Ответ: Я знаю, что котики могут быть ласковыми, усатыми и пугливыми, они являются любимыми животными Сони и не любят цитрусовые.

Другим, не менее популярным решением для написания RAG и чат-ботов является фреймворк LlamaIndex. Впервые фреймворк был представлен в 2022 году под именем GPT Index как инструмент, позволяющий пользователям эффективно индексировать и извлекать информацию из различных источников данных для использования с лингвистическими моделями. В 2023 году инструмент был переименован и здорово подрос благодаря комьюнити.

Чтобы создать тот же самый простейший RAG с помощью LlamaIndex у меня ушло еще меньше времени и строчек кода:

from llama_index.core import SimpleDirectoryReader, GPTVectorStoreIndex, Document


text_data = [
    Document(text="Котики — любимые животные Сони."),
    Document(text="Котики не любят цитрусовые."),
    Document(text="Котики ласковые, усатые и пугливые.")
]

index = GPTVectorStoreIndex.from_documents(text_data)

query_engine = index.as_query_engine()
response = query_engine.query("Что ты знаешь о котиках?")
print("Ответ:", response)

Выполняем код и получаем ответ:

Ответ: Котики обладают ласковым характером, у них есть усы, и они склонны к пугливости. Котики являются любимыми животными Сони.

Примечание

Чтобы воспроизвести примеры выше локально, вам понадобится api-ключ OpenAI. Для примера на LangChain его нужно будет передать в качестве параметра внутрь OpenAIEmbeddings(), а для примера на LlamaIndex ключ нужно добавить в переменные окружения.

Либо можно воспользоваться другим поставщиком эмбеддингов на ваше усмотрение.

Пишем своего чат-бота

Чтобы погрузиться в тему и лучше понять, каково работать с каждым из фреймворком, напишем не очень сложного чат-бота (вернее, даже двух). Реализуем одну и ту же функциональность и на LangChain, и на LlamaIndex! В конце статьи оставлю ссылку на репозиторий со всем исходным кодом.

У меня на ноутбуке завалялась классная методичка по инфекционным болезням кошек и собак в формате .pdf, используем ее в качестве источника данных. Вектора будем хранить в облачной базе Qdrant, а общаться с чат-ботом будем через консоль. Концептуально архитектура приложения будет выглядеть так:

Перед тем, как начинать готовить, нужно убедиться, что в холодильнике есть все ингредиенты. Нам потребуется api-ключ OpenAI, хост и api-ключ от кластера в Qdrant, путь к PDF-файлу, а имя для коллекции можно выдумать любое (дальше мы будем создавать ее прямо из кода). Заполним переменные окружения по шаблону:

OPENAI_API_KEY=
OPENAI_MODEL=gpt-3.5-turbo

QDRANT_HOST=
QDRANT_KEY=
COLLECTION_NAME=

PDF_PATH=
PDF_FOLDER=

VECTOR_SIZE=1536

Обработка PDF-файла

Начнем с того, что нам нужно подготовить PDF-файл ко всем трансформациям и отправке в хранилище. Для этого нужно разбить большой файл на кусочки поменьше, а также привести к типу данных Document .

Для LangChain сделаем это с помощью PyPDFLoader:

Реализация на LangChain

import os
from dotenv import load_dotenv
from langchain.document_loaders import PyPDFLoader


load_dotenv()

class PdfLoader:
    """
    A class for loading and processing PDF files using PyPDFLoader.
    """

    def __init__(self):
        self.file_path = os.getenv("PDF_PATH")

    def load_and_process_pdf(self):
        """
        Load and process the PDF file.
        Uses PyPDFLoader to extract documents from the file.

        :return: A list of Documents.
        """
        try:
          loader = PyPDFLoader(self.file_path)
          documents = loader.load()
          return documents
        except Exception as e:
            raise RuntimeError(f"Failed to load and process PDF: {e}")

Для реализации на LlamaIndex я использую SimpleDirectoryReader. Это самый простой способ загрузить данные из локальных файлов в LlamaIndex. Для производственных задач авторы документации рекомендуют воспользоваться каким-либо из специализированных ридеров, доступных на LlamaHub. Тем не менее, SimpleDirectoryReader будет достаточно для нашего проекта.

Реализация на LlamaIndex

import os
from dotenv import load_dotenv
from llama_index.core import Document, SimpleDirectoryReader


load_dotenv()

class PdfLoader:
    """
    A class for loading and processing PDF files using SimpleDirectoryReader.
    """

    def __init__(self):
        self.file_path = os.getenv("PDF_FOLDER")

    def load_and_process(self):
        """
        Load and process the PDF file.
        Extracts text from pages and returns a list of Document objects.

        :return: A list of Document objects containing the text of PDF pages.
        """
        try:
            reader = SimpleDirectoryReader(input_dir=self.file_path)
            documents = reader.load_data()
            return documents
        except Exception as e:
            raise RuntimeError(f"Failed to load and process PDF: {e}")

Работа с файлами в обоих фреймворках реализована одинаково лаконично.

Взаимодействие с Qdrant

Далее нам нужно написать логику работы с векторным хранилищем. Потребуются следующие методы: создание коллекции, создание индекса, а также загрузка уже существующего в базе индекса. Можно придерживаться такого сценария:

При запуске бота вызывается метод загрузки существующего индекса.
Внутри метода происходит проверка, существует ли коллекция в векторной базе.
Если коллекция отсутствует в Qdrant, мы создаем ее, а затем вызываем метод по созданию индекса.
Если коллекция уже есть, значит, мы загружаем индекс и используем его.

Я не стану представлять код класса целиком, потому что он несложный, но при этом довольно массивный. Тем не менее, я бы хотела обратить ваше внимание на метод, отвечающий за создание индекса, поскольку именно в нем будет заметно самое любопытное отличие.

Реализация на LangChain

    def create_index(self, documents):
        """
        Creates an index for the provided documents
        in the specified Qdrant collection.
        """
        try:
            client = self.get_qdrant_client()

            logger.info("Uploading documents to the collection...")
            vectorstore = Qdrant.from_documents(
                documents,
                self.embeddings,
                url=self.qdrant_host,
                api_key=self.qdrant_api_key,
                collection_name=self.collection_name
            )

            logger.info(
                f"Index successfully created for collection '{self.collection_name}'."
                )
            return vectorstore
        except Exception as e:
            logger.error(f"Error creating index: {e}")
            raise

За преобразование документов в векторные представления и их загрузку в Qdrant здесь отвечает метод Qdrant.from_documents(). Теперь реализуем ту же самую механику, но уже с помощью LlamaIndex:

Реализация на LlamaIndex

def create_index(self, documents):
        """
        Creates an index for the provided documents
        in the specified Qdrant collection.
        """
        try:
            client = self.get_qdrant_client()

            logger.info("Uploading documents to the collection...")
            pipeline = IngestionPipeline(
                transformations=[
                    SentenceSplitter(chunk_size=512, chunk_overlap=0),
                    TitleExtractor(),
                    embeddings,
                ],
                vector_store=client,
            )

            pipeline.run(documents=documents)

            index = VectorStoreIndex.from_vector_store(
                llm=Settings.llm,
                vector_store=vector_store,
                embed_model=Settings.embed_model,
            )

            logger.info(
                f"Index successfully created for collection '{self.collection_name}'."
                )
            return index
        except Exception as e:
            logger.error(f"Error creating index: {e}")
            raise

Здесь обратим внимание на IngestionPipeline . Внутрь этого объекта мы передаем параметры, в соответствии с которыми будет происходить обработка входных данных. Обработанные узлы сохраняются в векторное хранилище.

Собираем всё вместе и тестируем

Самое сложное позади, осталось собрать детали воедино и написать простейший консольный интерфейс. Приведу чуть более подробную реализацию на LangChain:

Реализация на LangChain

vector_db = VectorDatabase()
pdf_loader = PdfLoader()
model_name = os.getenv("OPENAI_MODEL")

try:
    documents = pdf_loader.load_and_process_pdf()
    retriever = vector_db.load_index(documents)
except Exception as e:
    raise Exception(f"Error occured while loading index: {e}")

llm = ChatOpenAI(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    model=model_name,
    temperature=0
)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever.as_retriever(
        search_type="similarity",
        search_kwargs={"k": 5, "score_threshold": 0.5}
        )
)

logger.info("Chatbot is ready! Enter a query (or type 'e' to quit):")
while True:
    query = input("> ")
    if query.lower() == "e":
        break

    try:
        response = qa_chain.invoke({"query": query})
        logger.info(f"Response: {response['result']}")
    except Exception as e:
        logger.error(f"Error during query execution: {e}")

Для LlamaIndex код будет практически идентичным, разве что ответ на свой вопрос мы будем получать вот таким образом:

response = index.as_query_engine(similarity_top_k=5, llm=llm).query(query)

Перед самым стартом возникает вопрос, а как же нам понять, что модель взяла данные для ответа именно из нашего документа? Что если модель не нашла информацию и вернулась с ответом, не имеющим отношение к нашим данным?

Есть несколько способов обезопасить себя от такой ситуации, но самый лаконичный из них — установка трешхолда. Например, в LangChain мы делаем это при настройке ретривера "score_threshold": 0.5

Помните, я рассказывала, что в качестве источника данных использую справочник инфекционных болезней кошек? Я придумала хороший вопрос для того, чтобы достоверно определить даже без дополнительных проверок, что модель генерирует ответ на основе документа. Если мы спросим бота, у которого нет доступа к этой методичке, о том, что такое конституция, то он ответит нам, что это документ.

Определение конституции из справочника болезней

Если же ответ сформулирован на основе методички, то чат-бот ответит, что это совокупность анатомо-морфологических особенностей организма. При этом, если в процессе векторного поиска не удастся найти достаточного совпадения, чат-бот честно ответит, что он не знает ответа на вопрос. Например, в методичке нет информации о том, что такое кефир:

И чат-бот на LangChain, и чат-бот на LlamaIndex одинаково хорошо справляются с вопросами. Можно дополнительно управлять ответом модели, например, с помощью промпта, чтобы чат-бот при ответе придерживался определенного стиля речи, был лаконичнее или использовал конкретные выражения в случае, если ответ не был найден.

Работа с агентами

Агентные системы всё еще вызывают интерес. Определим, что AI-агент — это программная сущность, которая использует методы ИИ для анализа среды, принятия решений и выполнения действий. Агент действует автономно, адаптируется к изменяющимся условиям и может взаимодействовать с другими агентами или пользователями.

Оба фреймворка предоставляют инструментарий для создания агентов, и мне также хочется его опробовать. Иногда нам может не хватать однообразного сценария работы чат-бота для решения всех наших проблем. Зачастую запросы могут быть составными или содержать в себе необходимость обратиться к дополнительным источникам.

Допустим, у нас в методичке может содержаться формула, но если мы попросим классического чат-бота на основе RAG произвести по ней расчеты, то бот нам здесь помочь не сумеет. В такой ситуации можно построить агентный RAG, это позволит и получать информацию из специфического источника, и выполнять какие-либо дополнительные действия. Например, подсчеты или походы в другие сервисы.

В рамках нашего примера сделаем агента, который будет выполнять две глобальные задачи: либо он найдет для вас информацию об инфекционных болезнях в методичке, либо поможет понять, есть у вашего котика проблемы с лишним весом. Чтобы наш чат-бот смог решать вторую задачу, нужно подготовить почву. Напишем очень простую функцию:

def is_cat_obese(weight_kg:  float) -> str:
    """
    Determines the weight status of a cat (normal, underweight, or obese).
    
    :param weight_kg: Weight of the cat in kilograms.
    :return: Message describing the cat's weight status.
    """

    if float(weight_kg) < 3.5:
        return f"Кошка с весом {weight_kg} кг имеет недостаточный вес."
    elif 3.5 <= float(weight_kg) <= 5.5:
        return f"Кошка с весом {weight_kg} кг находится в пределах нормы."
    else:
        return f"Кошка с весом {weight_kg} кг имеет избыточный вес."

Теперь нам нужно настроить Tools для нашего агента. Agent Tools — это функциональные компоненты, которые предоставляют AI-агенту доступ к дополнительным возможностям или ресурсам для выполнения задач. В нашем случае будет два компонента: поиск по документу и определение степени упитанности кошки.

В LangChain метод, создающий набор инструментов, будет выглядеть так:

Реализация на LangChain

def get_tools(qa_chain: RetrievalQA) -> list:
    """
    Creates a list of tools for the agent.
    
    :param qa_chain: The RetrievalQA chain for document retrieval.
    :return: List of tools.
    """
    
    return [
        Tool(
            name="Document Retrieval",
            func=lambda q: qa_chain({"query": q})["result"],
            description="Retrieves info about cat and dog diseases from document."
        ),
        Tool(
            name="Cat Weight Tool",
            func=is_cat_obese,
            description="Determines if a cat's weight is within a healthy range."
        )
    ]

Реализация с помощью LlamaIndex не будет сильно отличаться:

Реализация на LlamaIndex

def get_tools(index) -> list:
    """
    Creates a list of tools for the agent.
    
    :param index: LlamaIndex instance for document retrieval.
    :return: List of tools.
    """

    agent_tools = [
        QueryEngineTool(
            query_engine=index,
            metadata=ToolMetadata(
                name="Document",
                description="Retrieves info about cat and dog diseases from document.",
            ),
        ),

        FunctionTool.from_defaults(
        fn=is_cat_obese, name="Weight"
        )
    ]

    return agent_tools

Далее нам потребуется промпт. Задание системного промпта является опциональным, но почему бы не напомнить лишний раз модели о том, какой ответ нам бы хотелось видеть? Не будем изобретать велосипед и напишем простые инструкции:

def get_agent_instructions() -> str:
    """
    Provides instructions for the agent to always respond in Russian.
    
    :return: Instruction string.
    """
    return (
        "Ты умный и добрый ассистент, который ВСЕГДА отвечает на русском языке."
        "Если пользователь задаёт вопрос, дай ответ на русском."
        "Верни пользователю ответы на все заданные им вопросы."
        "Если в вопросе есть вес кошки, передавай на обработку ТОЛЬКО число."
    )

Осталось только собрать всё воедино! Вот так это будет смотреться для LangChain:

Реализация на LangChain

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever.as_retriever(
        search_type="similarity",
        search_kwargs={"k": 5, "score_threshold": 0.5}
        ),
    return_source_documents=True
)

tools = get_tools(qa_chain)
instructions = get_agent_instructions()

agent = initialize_agent(
        tools=tools,
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        verbose=True
    )

query_with_instructions = f"{instructions}\n{query}"
response = agent.run(query_with_instructions)

И вот так эта же самая функциональность будет выглядеть на другом фреймворке:

Реализация на LlamaIndex

query_engine = index.as_query_engine(llm=llm, similarity_top_k=5)

tools = get_tools(query_engine)
instructions = get_agent_instructions()

agent = ReActAgent.from_tools(
    tools,
    llm=llm,
    verbose=True,
    system_prompt=instructions
)

response = agent.chat(query)

Теперь запускаем наш код и видим, что теперь чат-бот не только расскажет нам, что такое конституция, но и поведает о том, что кошка весом 5 кг находится в отличной форме:

Вот так цепочка размышлений будет смотреться в LlamaIndex

Аналогично для чат-бота на LangChain

При этом мы все также можем задавать вопросы, задействующие только один из инструментов. А если мы в рамках данной реализации поручим чат-боту задачу за пределами предусмотренной функциональности (например, попросим сделать двойное сальто), то агент сначала отправится искать информацию в документе, не найдет ничего релевантного и ответит, что не сможет нам помочь в этом вопросе. Сделать процесс более элегантным можно путем предобработки запроса. Например, перед поиском ответа можно проводить классификацию.

Делаем выводы: какой инструмент использовать?

Мне кажется, что для создания несложных систем можно использовать любой из фреймворков. Они одинаково дружелюбны. Муки выбора начинаются там, где есть больше специфических деталей.

Одним из важных критериев выбора технологий лично для меня является количество информации об инструменте и полнота документации. Поскольку LangChain более популярный фреймворк, найти решение почти любой проблемы, связанной с ним, на форумах или в документации намного проще.

Тем не менее, и в том, и в другом фреймворке я заметила черту сходства: относительно быстрый цикл устаревания. Поскольку оба фреймворка появились не так давно и продолжают активно развиваться, в них часто вносятся заметные изменения, которые могут дестабилизировать или даже ломать уже существующий код. Очевидным решением здесь будет жесткая фиксация версий пакетов, но если проектов на LangChain или LlamaIndex у вас много, будьте готовы к тому, что в голове придется удерживать информацию об изменениях.

В LlamaIndex действительно заметно, что разработчики делают акцент на инструментах для работы с данными. Зачастую, когда мы хотим построить базу знаний, информация раскидана по разным источникам и даже может храниться в разных форматах. LlamaIndex хорошо справится с тем, чтобы привести все данные в нужный для дальнейшей обработки языковой моделью вид.

LangChain больше похож на конструктор. С ним несколько проще подружить другие инструменты разработки, но LangChain определенно проигрывает по скорости. Пока я исследовала предметную область, я неоднократно натыкалась на размышления о том, что LangChain не очень удобен для продакшна из-за медлительности на больших объемах данных и частоты вносимых во фреймворк изменений. Тем не менее, он все еще является одним из самых популярных решений. Если инструментом широко пользуются, значит, он достаточно хорошо справляется с задачами.

Функция	LangChain	LlamaIndex
Основная функция	Создание AI-приложений из базовых компонентов с возможностью объединения различных LLM и инструментов	Обработка больших объемов данных и управление графами знаний
Работа с данными	Предоставляет инструменты для связывания промптов, управления памятью и взаимодействия с внешними инструментами и API	Сильный акцент на подключении LLM к различным структурированным и неструктурированным источникам данных (базы данных, API, документы)
Примеры использования	Подходит для динамических процессов, включающих агентов LLM, работу с разными инструментами	Подходит для создания приложений, которые требуют графов знаний, структурированных запросов и индексирования документов
Простота использования	Универсальный, но может потребовать более высокого уровня знаний для разработки сложных систем	Высокая специализация для работы с данными и индексирования; простой подход для процессов, ориентированных на данные
Поддерживаемые языки разработки	Python, JavaScript	Python, TypeScript
Комьюнити и распространенность	Более крупное сообщество и бóльшая распространенность	Растущее open-source сообщество, но меньшее по сравнению с LangChain
Производительность	Оптимизирован для связывания множества действий и задач LLM	Оптимизирован для загрузки данных в больших масштабах

Мы все это время сравнивали фреймворки между собой, но ни разу не упомянули, что их можно использовать в связке. Несмотря на то, что оба инструмента хорошо справляются по отдельности, можно скомбинировать их сильные стороны для лучшего перформанса.

Например, в кейсе с созданием чат-бота, который дает ответы на основе документов, функциональность, связанную с обработкой данных можно поручить LlamaIndex, а цепочку взаимодействий создать с помощью LangChain, но это уже совсем другая история...

А какой из инструментов предпочитаете использовать вы? Делитесь в комментариях, мне будет очень интересно почитать.

Спасибо, что прогулялись со мной по этой теме! Код для обоих чат-ботов и методичку, как и обещала, можно найти вот здесь.

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22 янв. 2025 г.

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/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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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

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25.11.24 02:20 briankennedy

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

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01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

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04.12.24 22:24 andreygagloev

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HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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27.12.24 00:21 swiftdream

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04.01.25 04:56 florencestella

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06.01.25 19:09 michaeljordan15

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18.01.25 12:41 michaeldavenport218

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