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Интеграция LLM в корпоративные чат-боты: RAG-подход и эксперименты

Всем привет! На связи команда AI-Run из X5 Tech, мы занимаемся генеративными сетями в целом и языковыми моделями в частности. В этой статье мы опишем наш опыт работы с большими языковыми моделями (LLM), их внедрение для обработки корпоративных данных, а также поделимся нашими результатами и выводами.

Ещё мы расскажем о нашем подходе к использованию LLM, подробно остановимся на методе Retrieval Augmented Generation (RAG) и рассмотрим примеры использования чат-ботов на корпоративных порталах X5.

Эта статья будет полезна разработчикам, которые интересуются внедрением LLM для работы с корпоративными данными. Она основана на нашем выступлении на митапе, но не ограничивается им, а, скорее, дополняет его.

Что такое LLM?

LLM или большая языковая модель (Large Language Model) — это нейросетевая модель, обученная на огромных массивах текстовой информации для понимания и генерации естественного языка. Основная задача LLM — принимать на вход текст и отвечать на него также текстом. Важно отметить, что LLM ограничены длиной принимаемой информации (контекста) – максимального количества токенов, которое способна обработать модель за один вызов.

Токен – языковая единица, с которой работает модель. Чаще всего в токене содержатся 2-4 буквы. Контекст у моделей обычно имеет фиксированную длину, которая обычно составляет 4000 токенов. Например, субтитры нашего часового выступления занимают примерно 8000 токенов.

Работая с LLM, мы сталкиваемся не только с ограничениями на длину контекста: обработка корпоративных данных также накладывает свои ограничения. Пользоваться моделями, которые хостятся не на наших серверах, мы не можем из-за риска утечки чувствительной информации, а потому нам нужно либо маскировать эти данные, либо пользоваться моделями c открытым кодом и поддерживать их на своих серверах. Сегодня будем говорить о том, как мы работали со вторым вариантом.

Дело в том, что не все LLM "из коробки" идеально подходят для использования. Многие модели имеют недостаточную поддержку русского языка или не обладают специализированными знаниями, необходимыми для определённых задач. Опенсорсные модели также могут дублировать информацию и склонны к галлюцинациям, что снижает качество ответов.

Для решения этих проблем можно дообучать модели на собственных данных, но это часто бывает сложно и дорого. Как же тогда быть? Здесь на помощь приходит метод Retrieval Augmented Generation (RAG).

RAG: альтернатива дообучению

Схема работы RAG из оригинальной статьи

В качестве альтернативы подход RAG предлагает передавать в LLM релевантную вопросу пользователя информацию прямо внутри контекста. Например, если пользователь хочет получить отчёт по продажам за март 2024 года, мы можем найти соответствующие документы, передать их в текстовом виде в контекст и использовать LLM для создания структурированного ответа. Этот подход значительно увеличивает полезность LLM в корпоративной среде, позволяя модели работать с конкретными и актуальными данными.

Отлично, но как же сделать это? Процесс построения простого RAG пайплайна состоит из трёх этапов:

Преобразование документов: внутренние документы (инструкции, нормативные документы, FAQ и т. д.) преобразуются в векторы с помощью моделей эмбеддингов или индексируются, например, при помощи Elastic Search.
Поиск документов: при поступлении запроса пользователя он используется для поиска ближайших документов в базе данных.
Генерация ответа: LLM генерирует ответ на основе найденных документов и запроса пользователя.

В виде кода это может выглядеть так

from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains.retrieval_qa.base import RetrievalQA
from transformers import AutoModelForCausalLM
from langchain_community.embeddings import HuggingFaceInstructEmbeddings


# Загрузка языковой модели с huggingface
model = AutoModelForCausalLM.from_pretrained(
  "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True
)


"""Определяем размер чанков - длина кусков, на которые мы разбиваем исходный текст и chunk_overlap - пересечение между чанками """
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(data)


# Инициализация эмбеддингов для дальнейшего использования
embeddings  = HuggingFaceInstructEmbeddings(model_name="mistralai/Mistral-7B-v0.1",trust_remote_code=True,
                                     model_kwargs={'device': 'mps'}, encode_kwargs={'device': 'mps'})


# Создание векторной базы данных для хранения текстов и соответствующих им векторных представлений
db = Chroma.from_documents(texts, embeddings)


""" Настройка ретривера (системы поиска по схожести векторных представлений документов)
   Здесь параметр k в search_kwargs отвечает за количество наиболее релевантных документов в выдаче"""
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})


# Создаем llm_chain со встроенным Retrieval из langchain для удобства использования


qa = RetrievalQA.from_chain_type(
 llm=model, chain_type="rag", retriever=retriever, return_source_documents=True)


# Формулировка запроса и получение ответа на вопрос
query = "Где мне искать информацию по инвентаризации?"
result = qa({"query": query})

Но, к сожалению, простой RAG почти всегда не будет отвечать качеству поиска и генерации на ваших данных. Главным образом так получается потому, что стандартные подходы не всегда учитывают структуру данных: формат, необходимую длину контекста, распределение важной информации по документу и многое другое.

Поэтому наша работа здесь не закончилась, а только началась: мы долгое время экспериментировали с модификациями RAG-систем, и наконец готовы рассказать, что у нас вышло.

Улучшение RAG-системы. Retrieval

RAG состоит из двух частей: Retrieval и Augmented Generation. Эти части можно улучшать вместе (см. hybrid rag), но намного проще всё же по отдельности. В этой статье рассмотрим Retrieval, а вторую часть оставим для продолжения.

Мы хотим улучшать Retrieval, потому что хотим передавать как можно меньше нерелевантной информации в контекст модели – нерелевантная информация приведёт к лишним галлюцинациям, неправильным ответам, а если мы пользуемся коммерческими сетями (GPT, GigaChat, YaGPT), то передавая меньший контекст, мы ещё и сэкономим на токенах.

Retrieval-часть усложнённого пайплайна состоит из трёх этапов:

обработка запроса пользователя (Query Expansion);
поиск документов (контекстов) для ответа модели;
переранжированиe найденных документов для более точной выдачи.

Давайте рассмотрим, как мы улучшали каждую из этих частей.

Query Expansion

Работаем с исходным запросом пользователя

Query Expansion – это собирательное название техник, суть которых заключается в расширении и уточнении запроса пользователя. Эта техника помогает добавить больше контекста в исходный запрос пользователя, включая в него потенциальные ответы, которые помогают системе охватить более широкий спектр документов.

Как было "до"

Если исходный запрос был слишком неоднозначным, то добавление дополнительных ключевых слов или фраз, основанных на предполагаемых ответах, может значительно улучшить результаты поиска. Например, по запросу “отпуск” мы можем найти ответы на вопрос “оформить отпуск”, “перенести отпуск” и многие подобные.

Давайте рассмотрим примеры алгоритмов с самого простого до более сложных.

Перефразирование

Очень часто пользователи задают вопрос с опечатками или с минимальной информацией. Возьмём тот же пример, который мы уже использовали – запрос “отпуск”. Нам необходимо понять, что же хочет пользователь. Для этого мы можем сделать какое-нибудь предположение и не отвечать на вопрос “отпуск”, а перефразировать в “как оформить отпуск?”. Тогда вопросно-ответным системам гораздо легче будет ответить на такие вопросы. В идеале нужно уточнить у пользователя, что он имеет ввиду, но работа с историей чата – это тема отдельного поста. Основное, что нужно понять – это то, что наша LLM может помочь с перефразированием. Например, используя следующий промпт:

Ты – русскоязычный полезный ассистент. Ты помогаешь технической поддержке с поступающими вопросами. Пользователи не всегда пишут полные вопросы, ты всегда помогаешь перефразировать поступающий вопрос и выделяешь основную проблему.
Вот вопрос пользователя: “отпуск”
Перефразированный вопрос:

Как стало

Основной проблемой будет являться то, что модель будет некорректно перефразировать. Например, мы столкнулись с таким забавным перефразом:

Некорректное преобразование запроса

Нужно постоянно следить и улучшать промпт, тогда система в целом будет отвечать лучше.

Мы используем разные промпты для разных наборов данных, чтобы избежать возникающих вопросов “про пироги”, а также предварительно оцениваем необходимость перефраза при помощи классификатора. Этот метод оказал существенное влияние на качество выдачи:

	MAP@5	p@1	p@5
Без перефраза	0.437	0.341	0.560
С перефразом	0.794	0.729	0.807

SelfQuery

Этот метод фокусируется на извлечении и использовании метаданных из самого запроса. Определение таких элементов, как географическая принадлежность, временные рамки или специфическая терминология позволяет системе с большей точностью находить документы, наиболее релевантные поставленному вопросу, а выделение числовых значений из запроса позволяет напрямую использовать сравнение.

Схема работает так: мы вычленяем из запроса ключевые слова и значения при помощи промпта, по которым в дальнейшем можно будет проводить фильтрацию данных в базе.

Схема действия SelfQuery метода

Этот метод хорошо подходит, если запросы содержат достаточно подробную информацию, насыщены деталями и, особенно, цифры. Его большой плюс в том, что выбор метаданных производится при помощи нейросети, а потому будет работать даже при наличии опечаток и синонимов в запросе.

К сожалению, данный метод нам не подошёл, так как наши запросы содержат мало метаданных, и в целом достаточно короткие: в нашем же примере про отпуск метаданные выделяли категорию запроса (“отпуск”), а последующее переформулирование запроса (заложенное в промпт) приводило к тому, что полученный промпт получался пустым. Чисел, которые мы могли бы использовать для сравнения, в наших запросах тоже не было.

Вдобавок ко всему, если вы используете опенсорсную модель, она может плохо выделять метаданные и следовать шаблонам, что тоже является минусом данного метода.

Мы решили отказаться от использования SelfQuery, по крайней мере в имеющемся проекте, однако, этот метод выглядит крайне полезным для использования в проектах поиска недвижимости по заданным фильтрам.

FLARE (Forward-Looking Active Retrieval)

Схема работы метода FLARE

Эта техника включает в себя механизм повторного поиска, который активируется, если LLM не уверена в процессе генерации ответа. Уверенность модели мы измеряем при помощи распределения логитов функции потерь на выходе нейросети. Если проще, это "очки" ответов сети – если много ответов имеют похожие очки, то модель не уверена в своём ответе. Например, при генерации LLM ответ о биографии Байдена, если она начинает сомневаться, в каком университете он учился (это отслеживается, если вероятность токенов потенциального ответа снижается). Система отправляет запрос в LLM для уточнения места его учёбы, а дальше она продолжает генерировать ответ. Это продолжается до того момента, пока не будет сгенерирован символ остановки.

И хоть мы можем ограничить количество максимальных повторений цикла “поиск – генерация”, при таком подходе мы тратим много времени и вычислительных мощностей на повторный поиск и приостановку генерации. Также модель имеет свойство далеко отходить от темы. В вопросах об отпуске она имела свойство размышлять о том, что такое отпуск, и не отвечать на заданный вопрос, поэтому на наших данных этот подход тоже было решено не использовать.

Данный метод будет хорош для использования в более творческих задачах, например, для написания статей, рассказов и эссе. Если ваша задача в бизнесе предполагает под собой какую-либо творческую свободу, этот метод может быть крайне полезным.

Методы Query Expansion, за исключением перефразирования, не стали применимыми в нашей задаче, однако в других они способны принести значительный результат. Поэтому вам всё же следует протестировать эти способы, а мы перейдём к другому этапу – Search Engine.

Search Engine

Теперь работаем с поиском

После обработки запроса пользователя начинается первичный поиск подходящих документов. Здесь можно сделать параллель с поисковыми движками, например, Google или Yandex. Но в нашем случае нужно искать информацию на наших внутренних данных, а не во всём интернете. Давайте рассмотрим, как мы можем искать подходящие под запрос документы.

Векторный поиск

Самый популярный сейчас способ – векторный поиск. Это часто называют семантическим поиском. Суть метода заключается в том, что каждый текст можно представить в виде вектора – набора чисел. Векторы можно сравнивать друг с другом, как смыслы текстов. Чем меньше расстояние между векторами – тем более похожи по смыслу сравниваемые тексты. Для сравнения векторов используется косинусное расстояние. Поэтому давайте найдём векторы всех внутренних документов и будем искать наиболее близкие по конусному расстоянию документы к запросу пользователя. Таким образом и появляется идея векторного поиска.

Косинусное расстояние

Сейчас уже есть очень много специализированных векторных баз данных, мы используем у себя Qdrant. Эта технология позволяет с коробки сделать то, что мы описали выше.

В векторном поиске главным предметом для экспериментов и улучшения качества поиска является способ представить текст в виде векторов. Есть огромное количество методов, их качество на ваших данных будет зависеть от языка, средней длины и домена. В своей работе мы пробовали эмбеддинги clips/mfaq, intfloat/multilingual-e5-large и эмбеддинги от LLaMA. Самый лучшим инструментом векторизации у нас оказался multilingual-e5-large, однако вы можете держать руку на пульсе, просматривая лидерборд здесь, выбирая эмбеддер под задачу, релевантную для своего проекта.

Другие варианты

На самом деле, есть очень много способов сравнить два текста с друг другом. И даже не обязательно использовать что-то одно, их можно комбинировать и объединять в ансамблевый ретривер.

BM25 и полнотекстовый поиск

Использование векторного поиска не означает, что мы не можем использовать поиск по ключевым словам – зачастую (в особенности при поиске документов с различными аббревиатурами и терминами) бывает полезным использовать информацию о словах, встречающихся в документе.

Реализацию BM25 и подробности можно посмотреть, например, здесь.

Симметричный поиск

Идея данного подхода проста, и при этом он работает очень хорошо: мы ищем не документы, релевантные запросу, а другие вопросы, похожие на этот. Их мы достаём из базы данных, которая легко пополняется на всём протяжении работы сервиса при помощи статистик вопросов пользователей.

Каждый вопрос в базе данных привязан к конкретному документу – именно эти документы мы передаём в качестве результата симметричного поиска. Таким образом мы значительно повышаем качество поиска релевантного контекста на часто встречающихся вопросах.

Если идти дальше, то можно определять степень “частоты” запроса (например, измеряя его расстояние до остальных вопросов в базе) и, если этот вопрос всё же окажется редким, то уже запускать другие алгоритмы поиска.

SVM

Для ранжирования документов на основе их релевантности запросу можно также использовать SVM. Для этого мы трансформируем задачу ранжирования в задачу классификации: релевантные документы отмечаем, как положительный класс, а нерелевантные – как отрицательный. Таким образом мы используем SVM-классификатор для разделения документов на нужные нам и посторонние.

Для наших же данных лучше всего себя показал ансамбль из векторного поиска + SVM ретривера.

Вы же для своих данных можете попробовать разные варианты, библиотека langchain предоставляет очень много вариантов.

Post Processing

Теперь работаем с обработкой найденных документов

Финальной стадией работы Retriever в RAG-системах является постобработка. На этом этапе происходит пересмотр и перераспределение найденных документов так, чтобы наиболее релевантные запросу пользователя находились на первых позициях.

Первоначальное ранжирование документов, выполненное ретривером, может быть неидеальным. Это связано с использованием упрощённых алгоритмов, которые не всегда учитывают контекст запроса. В результате релевантные документы могут быть не на первых позициях, что снижает эффективность поиска. Напомню, что мы можем передать ограниченное количество документов в наш LLM из-за ограничения размера контекста модели.

Для улучшения точности ранжирования документов используются более сложные методы.

Cross-encoder

Механизм работы постобработчика

Это нейросеть, обученная оценивать степень близости текстов друг с другом. Казалось бы, в чём отличие от нашего семантического поиска? Огромное – Cross-Encoder более сложный алгоритм, который отлавливает глубокие смысловые значения в текстах. В процессе работы он сравнивает каждый найденный документ с запросом пользователя, выдавая численную оценку схожести. Теперь можно повторно отсортировать документы по новой оценке.

Это также позволило значительно повысить значения целевых метрик:

	MAP@5	p@1	p@5
До переранжирования	0.794	0.729	0.807
Посла переранжирования	0.904	0.835	0.972

Но что, если мы хотим сортировать не только по схожести от кросс-энкодера, но и от других?

Boosting

Используем классический ML. Например, мы можем обучить градиентный бустинг. Этот метод объединяет множество слабых моделей для создания сильного прогнозирующего алгоритма. В нашем контексте градиентный бустинг может использоваться для более точного определения релевантности каждого документа по отношению к запросу на основе любых фичей: схожесть от кросс-энкодера, BM25 и другие скоры.

Постобработка является ключевым этапом в работе RAG-систем, так как она позволяет существенно повысить качество поиска за счёт более точного ранжирования документов. Применение методов, таких как Cross-Encoder и градиентный бустинг, обеспечивает более контекстуальное и релевантное расположение документов.

В заключение

На этом у нас всё. В этот раз мы рассмотрели несколько вариантов того, как мы улучшали поиск релевантных документов для RAG-систем:

Первичную обработку запроса пользователя при помощи методов Query Expansion: перефразирования вопроса, выделения метаданных и поддержания уверенности нейросети в своих генерациях.
Векторный поиск документов в базе данных.
Последующее переранжирование найденных документов.

В следующих статьях расскажем про улучшение генераций на основе уже найденных документов, как мы мониторим LLM в проде, боремся с галлюцинациями и многое другое.

Авторы: Мичил Егоров , Глеб Панин , Дарья Андреева

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9 авг. 2024 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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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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My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
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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

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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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