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[Перевод] Добавление собственных данных в LLM с помощью RAG

Этот материал посвящён тому, как добавлять собственные данные в предварительно обученные LLM (Large Language Model, большая языковая модель) с применением подхода, основанного на промптах, который называется RAG (Retrieval‑Augmented Generation, генерация ответа с использованием результатов поиска).

Большие языковые модели знают о мире многое, но не всё. Так как обучение таких моделей занимает много времени, данные, использованные в последнем сеансе их обучения, могут оказаться достаточно старыми. И хотя LLM знакомы с общеизвестными фактами, сведения о которых имеются в интернете, они ничего не знают о ваших собственных данных. А это — часто именно те данные, которые нужны в вашем приложении, основанном на технологиях искусственного интеллекта. Поэтому неудивительно то, что уже довольно давно и учёные, и разработчики ИИ‑систем уделяют серьёзное внимание вопросу расширения LLM новыми данными.

До наступления эры LLM модели часто дополняли новыми данными, просто проводя их дообучение. Но теперь, когда используемые модели стали гораздо масштабнее, когда обучать их стали на гораздо больших объёмах данных, дообучение моделей подходит лишь для совсем немногих сценариев их использования. Дообучение особенно хорошо подходит для тех случаев, когда нужно сделать так, чтобы модель взаимодействовала бы с пользователем, используя стиль и тональность высказываний, отличающиеся от изначальных. Один из отличных примеров успешного применения дообучения — это когда компания OpenAI доработала свои старые модели GPT-3.5, превратив их в модели GPT-3.5-turbo (ChatGPT). Первая группа моделей была нацелена на завершение предложений, а вторая — на общение с пользователем в чате. Если модели, завершающей предложения, передавали промпт наподобие «Можешь рассказать мне о палатках для холодной погоды», она могла выдать ответ, расширяющий этот промпт: «и о любом другом походном снаряжении для холодной погоды?». А модель, ориентированная на общение в чате, отреагировала бы на подобный промпт чем‑то вроде такого ответа: «Конечно! Они придуманы так, чтобы выдерживать низкие температуры, сильный ветер и снег благодаря…». В данном случае цель компании OpenAI была не в том, чтобы расширить информацию, доступную модели, а в том, чтобы изменить способ её общения с пользователями. В таких случаях дообучение способно буквально творить чудеса!

Но дообучение уже не так хорошо себя показывает при необходимости добавления новых данных в модель. Это, как мне удалось выяснить, гораздо более распространённый бизнес‑сценарий. Кроме того, дообучение LLM требует больших объёмов высококачественных данных, серьёзных затрат на вычислительные ресурсы и много времени. Для большинства пользователей LLM всё это относится к разряду ограниченных ресурсов.

В этом материале мы рассмотрим RAG — альтернативный подход к добавлению собственных данных в LLM. Он основан на промптах и был представлен в 2021 году командой Facebook AI Research (FAIR), работавшей над ним вместе с другими исследователями. Концепция RAG, с одной стороны, даёт пользователю достаточно мощные возможности, которые позволяют применять её в поисковой системе Bing и на других высоконагруженных сайтах для внедрения самых свежих данных в их модели. А, с другой стороны, это — достаточно простая концепция, которую я смогу объяснить читателю в этой статье. Надо отметить, что применение RAG отличается эффективностью и в тех случаях, когда у пользователя нет большого объёма данных, нет серьёзного бюджета и нет большого количества времени на манипуляции с моделями.

Три примера кода, демонстрирующие различные сценарии применения RAG, вы можете найти в этом GitHub‑репозитории. В одном из них API OpenAI используются напрямую, во втором и в третьем применяются опенсорсные API LangChan и Semantic Kernel. Первый фрагмент кода и соответствующий ему сценарий использования RAG мы рассмотрим в этом материале, а два других примера я советую вам изучить самостоятельно.

Но, прежде чем мы займёмся кодом, предлагаю разобраться с основными понятиями RAG.

Обзор RAG

Идеи RAG могут быть реализованы разными способами, но на концептуальном уровне использование RAG в приложении, основанном на технологиях искусственного интеллекта, включает в себя следующие шаги:

Пользователь вводит вопрос.
Система ищет подходящие документы, которые могут содержать ответ на этот вопрос. Эти документы часто включают в себя собственные данные компании, которая разработала систему, а хранятся они обычно в некоем каталоге документов.
Система создаёт промпт для LLM, в котором скомбинированы данные, введённые пользователем, подходящие документы и инструкции для LLM. Модель должна ответить на вопрос пользователя, применив предоставленные ей документы.
Система отправляет промпт LLM.
LLM возвращает ответ на вопрос пользователя, основанный на предоставленных контекстных сведениях. Это — выходные данные системы.

Вот диаграмма, отражающая эту общую идею, лежащую в основе RAG:

https://miro.medium.com/v2/resize:fit:700/1*bIjsKJchyMJzf--LwWjeMg.png

Общее устройство системы, использующей RAG

Тут приведено описание работы RAG, сделанное простыми словами. Здесь почти нет сведений о реализации этого механизма. Предлагаю перейти к подробностям о нём, а для начала — взглянуть на научную публикацию, в которой была предложена идея RAG.

Публикация, посвящённая RAG

Термин «RAG» был предложен в 2021 году, в публикации «Retrieval‑Augmented Generation for Knowledge‑Intensive NLP Tasks», подготовленной командой FAIR и сотрудничавшими с ней учёными. Идеи, предложенные авторами этой работы, оказали огромное воздействие на ИИ‑решения, которыми мы пользуемся сегодня, поэтому с данными идеями стоит познакомиться поближе.

Вот — обзор архитектуры ИИ‑системы, предложенной в работе:

https://miro.medium.com/v2/resize:fit:700/1*WPNPxlCxh6Nw4muUyfULTQ.png

Архитектура ИИ-системы, использующей RAG

Ниже мы подробно обсудим каждую из составных частей этой архитектуры. Если смотреть в общем, то предложенная в работе структура системы составлена из двух компонентов: из поискового модуля и из модуля генератора. Поисковый компонент преобразует входной текст в последовательность чисел с плавающей запятой (вектор), используя кодировщик запроса. Далее, он, используя единый подход, трансформирует каждый из документов, применяя кодировщик документов, после чего сохраняет закодированные документы в виде поискового индекса. Затем, в поисковом индексе, поисковый компонент выполняет поиск векторов документов, которые имеют отношение к входному вектору. После этого он преобразует векторы документов обратно в их текстовое представление и возвращает эти тексты в качестве результата своей работы. Теперь в дело вступает генератор, который принимает текст, введённый пользователем, и соответствующие ему документы, комбинирует всё это в единый промпт и предлагает LLM дать ответ на вопрос пользователя с учётом информации, имеющейся в найденных ранее документах. То, что выдаст после этого LLM, и будет выходными данными всей этой системы.

Вы могли заметить, что кодировщик запроса, кодировщик документов и LLM на предыдущем изображении показаны с использованием похожих фигур. Это из‑за того, что все они реализованы с использованием трансформеров. Традиционные трансформеры состоят из двух частей: из кодировщика и декодера. Кодировщик отвечает за трансформацию входного текста в вектор (или в последовательность векторов), которая, в общих чертах, отражает смысл слов. А цель декодера — сгенерировать новый текст, основываясь на входных данных. В архитектуре системы, предложенной в вышеупомянутой публикации, кодировщик запроса и кодировщик документов реализованы с помощью трансформеров, в состав которых входит лишь кодировщик. Дело в том, что этим компонентам системы нужно лишь преобразовывать фрагменты текста в векторы, состоящие из чисел. А LLM в модуле генератора реализована на базе традиционного трансформера, содержащего и кодировщик, и декодер.

Как обучают модель, обладающую такой архитектурой? В публикации предлагается использовать предварительно обученные трансформеры и проводить совместное дообучение только кодировщика запросов и LLM из блока генератора. Это дообучение выполняется с использованием пар фрагментов данных, один из которых — это то, что ввёл пользователь, а второй — это то, что ожидается получить на выходе LLM. Кодировщик документов не дообучают, так как это может быть достаточно затратным, и из‑за того, что авторы работы обнаружили, что в этом нет необходимости для обеспечения хороших результатов работы системы.

В публикации предлагается два подхода для реализации этой архитектуры:

RAG‑последовательность: получают k документов и используют их для генерирования всех выходных токенов, которые служат ответом на запрос пользователя.
RAG‑токен: получают k документов, используют их для генерирования следующего токена, затем получают ещё k документов, которые используют для генерации следующего токена, и так далее. Это значит, что всё может свестись к нахождению нескольких разных наборов документов при генерировании единственного ответа на запрос пользователя.

Теперь вы получили достаточно хорошее общее представление об архитектуре системы, предложенной в публикации о RAG. Подобный шаблон проектирования достаточно широко распространён в ИИ‑индустрии. Но оказалось, что не всё то, о чём ведётся речь в публикации, реализовано в точности так же, как в этой публикации предложено.

Использование RAG на практике

На практике реализации RAG, которые широко используются в различных организациях, основаны на том, что было предложено в вышеупомянутой публикации, но при этом для них характерны некоторые особенности:

Из двух подходов к реализации архитектуры RAG, предложенных в публикации, почти всегда выбирают RAG‑последовательность. Дело в том, что этот подход оказывается дешевле и проще, чем другой. При этом он даёт отличные результаты.
На практике обычно не занимаются дообучением каких‑либо трансформеров, входящих в состав системы. Те предварительно обученные LLM, которыми мы можем пользоваться в наши дни, достаточно хороши для того чтобы пользоваться ими в их исходном виде. Их, кроме того, слишком затратно дообучать самостоятельно.

Стоит добавить, что применяемые методы поиска документов не всегда реализованы именно так, как предложено в публикации. Поиск обычно выполняется с применением некоего поискового сервиса — наподобие FAISS или Azure Cognitive Search. Такие сервисы поддерживают различные способы поиска документов, которые хорошо сочетаются с RAG. В работе поисковых сервис обычно можно выделить два следующих шага:

Поиск информации. На этом шаге производится сопоставление запроса пользователя с документами, находящимися в поисковом индексе. После этого осуществляется извлечение наиболее подходящих документов. Существуют три самых распространённых подхода к поиску документов: поиск по ключевым словам, векторный поиск, гибридный поиск.
Ранжирование информации. Это — необязательный шаг, который следует за шагом поиска информации. При его выполнении берётся список документов, которые были сочтены подходящими при поиске информации, после чего, по более точным алгоритмам, чем ранее, определяется их соответствие запросу пользователя.

Поговорим о поиске и ранжировании документов подробнее, начав с трёх подходов к поиску информации.

Поиск по ключевым словам

Самый простой способ поиска документов, имеющих отношение к запросу пользователя — это так называемый «поиск по ключевым словам» (ещё известный как «полнотекстовый поиск»). Поиск по ключевым словам использует те ключевые слова, которые ввёл пользователь, для поиска по индексу документов, содержащих эти слова. Сравнение содержимого документов с запросом выполняется исключительно на основе текста, без применения векторов. Этот подход к поиску существует уже давно, но он не потерял актуальности и в наши дни. Он весьма полезен в тех случаях, когда ищут идентификаторы пользователей, коды продуктов, адреса и любые другие данные, при поиске которых важно точное совпадение с текстом запроса. Вот — общая схема системы, в которой используется поиск по ключевым словам.

https://miro.medium.com/v2/resize:fit:700/1*epQPSuH6PvolP9LBZE-2YQ.png

Система, в которой используется поиск по ключевым словам

При таком подходе поисковый сервис поддерживает работу инвертированного индекса, хранящего сведения о соответствии слов и документов, в которых используются эти слова. Текстовые данные, введённые пользователем, разбирают, извлекая из них ключевые слова. Эти слова анализируют, находя их базовые формы. Затем выполняют поиск по инвертированному индексу, каждому совпадению назначают оценку, а после этого наиболее релевантные документы, соответствующие запросу, возвращают из поискового сервиса.

Векторный поиск

«Векторный поиск», который ещё называют «плотным поиском информации» («dense retrieval»), отличается от поиска по ключевым словам. Один из аспектов этого отличия заключается в том, что при векторном поиске возможно нахождение соответствия поисковому запросу в документах, в которых нет ключевых слов из запроса, но общий смысл которых близок к смыслу запроса. Например, представьте, что создаёте чат‑бота, который планируется использовать в службе поддержки сайта для сдачи недвижимости в аренду. Пользователь задаёт боту вопрос: «Do you have recommendations for a spacious apartment close to the sea», интересуясь, может ли он порекомендовать просторное жильё, расположенное близко к морю. В документе, содержащем сведения о подходящем жилище, имеется следующий текст: «4000 sq ft home with ocean view» — тут описан дом площадью 4000 квадратных футов с видом на океан. При поиске по ключевым словам такой документ найден не будет. А вот система векторного поиска его найдёт. Векторный поиск лучше всего показывает себя в тех случаях, когда в неструктурированном тексте ищут некие общие идеи, а не точные ключевые слова.

Вот — общий обзор RAG‑системы, в которой используется векторный поиск:

https://miro.medium.com/v2/resize:fit:700/1*aAd2Np3BvPt_uaUTpo6Z1w.png

Система, в которой используется векторный поиск

Взглянув на эту схему, можно понять, что векторный поиск, используемый на практике, это — практически то же самое, что было предложено в публикации по RAG. За исключением того, что в данном случае не проводится дообучение трансформеров.

В подобных системах для кодирования запроса и документов обычно используют предварительно обученную модель эмбеддингов, такую, как text‑embedding‑ada-002 от OpenAI. А для формирования итогового результата применяют предварительно обученные LLM, например — gpt-35-turbo (ChatGPT), тоже созданную OpenAI. Модель эмбеддингов используется для преобразования входного текста и текста документов в соответствующие «эмбеддинги». Что такое «эмбеддинг»? Это — вектор чисел с плавающей запятой. Он, в общих чертах, «схватывает» смысл текста, который в нём закодирован. Если два фрагмента текста как‑то связаны, тогда можно предположить, что и соответствующие им эмбеддинги будут похожи друг на друга.

Как определить то, что векторы похожи? Для ответа на этот вопрос рассмотрим пример. Мы исходим из предположения о том, что для нахождения следующих векторов эмбеддингов используется наша собственная модель эмбеддингов:

a = (0, 1) представляет «Do you have recommendations for a spacious apartment close to the sea?» — «Можете ли вы порекомендовать просторное жильё, расположенное близко к морю?».
b = (0.12, 0.99) представляет «4000 sq ft home with ocean view» — «дом площадью 4000 квадратных футов с видом на океан»
c = (0.96, 0.26) представляет «I want a donut» — «Я хочу пончик».

Эти векторы можно нарисовать на диаграмме:

https://miro.medium.com/v2/resize:fit:700/1*i9on0CCqt6gx-62HlQVz7A.png

Графическое представление векторов

Просто глядя на это изображение, можно догадаться о том, что вектор, который сильнее всего похож на вектор a — это b (а не c). Если прибегнуть к математическим методам выявления схожести векторов, то можно сказать, что существуют три распространённых подхода для решения этой задачи: скалярное произведение векторов, косинусный коэффициент векторов и евклидово расстояние между векторами. Найдём показатели, характеризующие схожесть векторов, используя эти методы, и посмотрим, подтвердят ли они нашу догадку.

При применении метода скалярного произведения векторов, как можно ожидать из его названия, просто вычисляют скалярное произведение (его ещё называют внутренним произведением) векторов. Чем больше результат — тем ближе друг к другу векторы:

https://miro.medium.com/v2/resize:fit:700/1*lvuIIQnVTfXlT2nUUZJxKg.png

Скалярное произведение векторов

Скалярное произведение векторов a и b больше, чем скалярное произведение a и c. Это подтверждает нашу догадку относительно того, что векторы a и b больше похожи друг на друга, чем a и c. Применяя этот метод, надо помнить о том, что скалярное произведение очень сильно зависит от длины векторов. Оно применимо только для сравнения схожести векторов, имеющих одинаковую длину. Эмбеддинги OpenAI всегда представлены единичными векторами — векторами, имеющими длину, равную единице. Чтобы наши эмбеддинги согласовывались бы с теми, что используются в OpenAI, они тоже должны быть единичными.

Косинусный коэффициент векторов вычисляют, находя косинус угла между двумя векторами. Чем этот коэффициент больше — тем сильнее векторы похожи друг на друга. Формула для вычисления этого коэффициента предусматривает вычисление скалярного произведения векторов, которое делят на произведение длин векторов. Это дополнение предыдущей формулы, нормализующее результаты, позволяет эффективно измерять схожесть векторов, учитывая их направления и не обращая внимания на их длины.

https://miro.medium.com/v2/resize:fit:700/1*Nw9GicUgdlh9z8uSqQEN5w.png

Косинусный коэффициент векторов

Так как мы пользуемся единичными векторами, их скалярное произведение и косинусный коэффициент дают в точности одинаковые результаты.

Вычисляя евклидово расстояние между векторами, находят расстояние между ними — в том смысле, в котором понятие «расстояние» используется в обычной жизни. Чем меньше расстояние между двумя векторами — тем сильнее они друг на друга похожи. В отличие от косинусного коэффициента, при вычислении евклидового расстояния во внимание принимаются длина и направление векторов.

https://miro.medium.com/v2/resize:fit:700/1*3WEwPbQfGtMqcL57dIG69A.png

Евклидово расстояние между векторами

Видно, что расстояние между векторами a и b меньше, чем между векторами a и c. Это говорит нам о том, что векторы a и b больше похожи друг на друга, чем a и c.

В примере используются двумерные векторы, что позволяет нам, просто глядя на их изображения, строить догадки об их схожести. Но в случае с эмбеддингами часто бывает так, что речь идёт о векторах гораздо большей размерности. Например, модель text‑embedding‑ada-002, созданная OpenAI, генерирует векторы, размерность которых равна 1536. Не забывайте о том, что количество измерений, используемых для представления эмбеддингов, не зависит от длины входного текста. Поэтому и короткий запрос, и длинный документ будут преобразованы в векторы эмбеддингов одинаковой размерности.

Большинство поисковых сервисов, существующих в наши дни, поддерживают все три рассмотренные нами метода определения схожести векторов. Они позволяют пользователю выбрать метод, который ему нужен. Какой из этих методов лучше выбрать?

Если ваши эмбеддинги имеют разную длину, и вы хотите учитывать их длину при определении их схожести, тогда лучше всего будет выбрать евклидово расстояние между векторами, так как при его вычислении учитывается и длина векторов, и их направление.
Если все ваши эмбеддинги нормализованы, приведены к единичной длине, тогда все три рассмотренных нами подхода дадут, как вы уже видели, схожие результаты. Но при этом скалярное произведение векторов вычисляется быстрее. Предположим, вы работаете с системой (с приложение или сервисом), которая знает о том, что имеет дело с единичными векторами. В такой ситуации вычисление косинусного коэффициента векторов, скорее всего, будет оптимизировано и сведено к вычислению их скалярного произведения. Поэтому, скорее всего, в подобном случае вычислительная нагрузка на систему при использовании косинусного коэффициента векторов будет столь же скромной, как и при использовании скалярного произведения.

Для того чтобы найти векторы документов, лучше всего соответствующие вектору запроса, поисковый сервис может пойти самым простым и неэффективным, в плане траты вычислительных ресурсов, путём. А именно, он определит схожесть входного вектора с вектором каждого из документов, отсортирует список документов в соответствии с их оценкой, и вернёт верхнюю часть этого списка. Этот простой подход, правда, не масштабируется. Он не подходит для больших корпоративных приложений, в которых используется очень много документов. В результате поисковые сервисы обычно используют некую разновидность алгоритма ANN (Approximate Nearest Neighbor, приближённый поиск ближайшего соседа). В этом алгоритме применяются продуманные оптимизации, которые позволяют ему возвращать приблизительные результаты быстрее, чем при использовании других методов. Одной из популярных реализаций ANN является алгоритм HNSW (Hierarchical Navigable Small World, иерархический маленький мир).

Гибридный поиск

Гибридный поиск подразумевает применение и поиска по ключевым словам, и векторного поиска. Например, представим себе ситуацию, когда имеется идентификатор клиента и текстовый запрос. Нужно провести поиск, учитывающий и точные данные в виде идентификатора, и общий смысл текста, введённого пользователем. Это — отличный сценарий для применения гибридного поиска. Два типа поиска выполняются самостоятельно, после чего полученные данные комбинируются с помощью специального алгоритма, который выбирает лучшее из того, что дали разные подходы к поиску. Этот метод часто используется на практике, особенно — в достаточно сложных приложениях.

Семантическое ранжирование

Семантическое ранжирование (или переранжирование) — это необязательный шаг работы RAG‑системы, который следует за шагом нахождения документов. На шаге нахождения документов система делает всё возможное для ранжирования возвращённых документов на основании их релевантности запросу пользователя. А семантическое ранжирование часто способно улучшить результаты первоначальной оценки документов. При его выполнении берётся подмножество документов, возвращённое после их поиска, после чего, с помощью LLM, обученной специально для выполнения этой задачи, вычисляются коэффициенты релевантности более высокого качества, чем ранее. Эти коэффициенты применяются для переранжирования документов.

https://miro.medium.com/v2/resize:fit:700/1*LrSPog4UlVa05CthUJASdA.png

RAG-система, в которой используется модуль семантического ранжирования документов

Здесь показан модуль семантического ранжирования, скомбинированный с модулем векторного поиска, но этот модуль хорошо сочетается и с подсистемой поиска по ключевым словам. Я решила показать здесь совместную работу семантического ранжирования и векторного поиска из‑за того, что именно так сделано в примере реализации RAG‑системы, подготовленном для этой статьи. Рассмотрим этот пример.

Простая реализация RAG-системы

В этом разделе мы рассмотрим код, который позволяет, используя RAG и сервис Azure Cognitive Search, добавлять собственные данные в ChatGPT. Код, который я тут продемонстрирую, использует API OpenAI для прямого взаимодействия с СhatGPT. Он находится в папке 1_openai репозитория с кодом к статье. В этом репозитории вы можете найти ещё две реализации той же идеи, одна из которых основана на LangChain (папка 2_langchain), а другая — на Semantic Kernel (папка 3_semantic_kernel). Это — два популярных опенсорсных фреймворка, которые помогают разработчикам создавать приложения, использующие LLM.

Цель этого проекта — создание чат-бота, к которому пользователи смогут обращаться для получения дополнительной информации о товарах, которые продаёт некая компания. Данные, которыми мы будем пользоваться, представлены несколькими markdown-файлами. В них содержатся подробные сведения о товарах.

Начнём с разбора файла init_search_1.py:

"""
Инициализирует индекс Azure Cognitive Search нашими данными, используя векторный поиск
и семантическое ранжирование.

Для запуска этого кода в вашей учётной записи Azure уже должны быть созданы ресурсы
"Cognitive Search" и "OpenAI".
"""
import os

import openai
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    HnswParameters,
    HnswVectorSearchAlgorithmConfiguration,
    PrioritizedFields,
    SearchableField,
    SearchField,
    SearchFieldDataType,
    SearchIndex,
    SemanticConfiguration,
    SemanticField,
    SemanticSettings,
    SimpleField,
    VectorSearch,
)
from dotenv import load_dotenv
from langchain.document_loaders import DirectoryLoader, UnstructuredMarkdownLoader
from langchain.text_splitter import Language, RecursiveCharacterTextSplitter

# Конфигурация Azure Search.
AZURE_SEARCH_ENDPOINT = os.getenv("AZURE_SEARCH_ENDPOINT")
AZURE_SEARCH_KEY = os.getenv("AZURE_SEARCH_KEY")
AZURE_SEARCH_INDEX_NAME = "products-index-1"

# Конфигурация Azure OpenAI.
AZURE_OPENAI_API_TYPE = "azure"
AZURE_OPENAI_API_BASE = os.getenv("AZURE_OPENAI_API_BASE")
AZURE_OPENAI_API_VERSION = "2023-03-15-preview"
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
AZURE_OPENAI_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT")

DATA_DIR = "data/"


def load_and_split_documents() -> list[dict]:
    """
    Загружает наши документы с диска и разбивает их на фрагменты.
    Возвращает список словарей.
    """
    # Загрузка данных.
    loader = DirectoryLoader(
        DATA_DIR, loader_cls=UnstructuredMarkdownLoader, show_progress=True
    )
    docs = loader.load()

    # Разбиение документов на фрагменты.
    splitter = RecursiveCharacterTextSplitter.from_language(
        language=Language.MARKDOWN, chunk_size=6000, chunk_overlap=100
    )
    split_docs = splitter.split_documents(docs)

    # Преобразование документов в список словарей.
    final_docs = []
    for i, doc in enumerate(split_docs):
        doc_dict = {
            "id": str(i),
            "content": doc.page_content,
            "sourcefile": os.path.basename(doc.metadata["source"]),
        }
        final_docs.append(doc_dict)

    return final_docs


def get_index(name: str) -> SearchIndex:
    """
    Возвращает индекс Azure Cognitive Search с заданным именем.
    """
    # Поля, которые мы хотим индексировать. Поле "embedding" - это векторное поле,
    # которое будет использоваться для векторного поиска.
    fields = [
        SimpleField(name="id", type=SearchFieldDataType.String, key=True),
        SimpleField(name="sourcefile", type=SearchFieldDataType.String),
        SearchableField(name="content", type=SearchFieldDataType.String),
        SearchField(
            name="embedding",
            type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
            # Размер вектора, созданного моделью text-embedding-ada-002.
            vector_search_dimensions=1536,
            vector_search_configuration="default",
        ),
    ]

    # Поле "content" должно иметь приоритет при семантическом ранжировании.
    semantic_settings = SemanticSettings(
        configurations=[
            SemanticConfiguration(
                name="default",
                prioritized_fields=PrioritizedFields(
                    title_field=None,
                    prioritized_content_fields=[SemanticField(field_name="content")],
                ),
            )
        ]
    )

    # Для векторного поиска мы хотим пользоваться алгоритмом HNSW (Hierarchical Navigable Small World)
    # (разновидность алгоритма приближённого поиска ближайшего соседа) с
    # применением косинусного коэффициента векторов.
    vector_search = VectorSearch(
        algorithm_configurations=[
            HnswVectorSearchAlgorithmConfiguration(
                name="default",
                kind="hnsw",
                parameters=HnswParameters(metric="cosine"),
            )
        ]
    )

    # Создание поискового индекса.
    index = SearchIndex(
        name=name,
        fields=fields,
        semantic_settings=semantic_settings,
        vector_search=vector_search,
    )

    return index


def initialize(search_index_client: SearchIndexClient):
    """
    Инициализирует индекс Azure Cognitive Search нашими данными, используя 
векторный поиск.
    """
    # Загрузка данных.
    docs = load_and_split_documents()
    for doc in docs:
        doc["embedding"] = openai.Embedding.create(
            engine=AZURE_OPENAI_EMBEDDING_DEPLOYMENT, input=doc["content"]
        )["data"][0]["embedding"]

    # Создание индекса Azure Cognitive Search.
    index = get_index(AZURE_SEARCH_INDEX_NAME)
    search_index_client.create_or_update_index(index)

    # Выгрузка данных в индекс.
    search_client = SearchClient(
        endpoint=AZURE_SEARCH_ENDPOINT,
        index_name=AZURE_SEARCH_INDEX_NAME,
        credential=AzureKeyCredential(AZURE_SEARCH_KEY),
    )
    search_client.upload_documents(docs)


def delete(search_index_client: SearchIndexClient):
    """
    Удаляет индекс Azure Cognitive Search.
    """
    search_index_client.delete_index(AZURE_SEARCH_INDEX_NAME)


def main():
    load_dotenv()

    openai.api_type = AZURE_OPENAI_API_TYPE
    openai.api_base = AZURE_OPENAI_API_BASE
    openai.api_version = AZURE_OPENAI_API_VERSION
    openai.api_key = AZURE_OPENAI_API_KEY

    search_index_client = SearchIndexClient(
        AZURE_SEARCH_ENDPOINT, AzureKeyCredential(AZURE_SEARCH_KEY)
    )

    initialize(search_index_client)
    # delete(search_index_client)


if __name__ == "__main__":
    main()

Наша первая задача — загрузить markdown‑файлы с данными и разбить их на фрагменты по 6000 символов, сделав это так, чтобы данные соседних фрагментов перекрывались бы на 100 символов. Каждый из этих фрагментов данных позже будет закодирован с помощью одного эмбеддинга и добавлен в поисковый индекс. Если файлы достаточно малы — не нужно разбивать их на части, их можно преобразовывать в эмбеддинги целиком. Но если они велики — лучше разделить их на фрагменты, так как эмбеддинги имеют фиксированные размеры, поэтому не смогут адекватно представить всю информацию из слишком больших текстовых файлов. Мы добавляем во фрагменты повторяющуюся информацию для того чтобы не терять смысл текстов, разбиваемых границами фрагментов.

Далее — создаётся список, в котором содержатся словари, каждый из которых представляет отдельный фрагмент текста, уникальный идентификатор, имя файла, являющегося источником текста. Затем для каждого фрагмента вычисляется эмбеддинг. Для этого используется модель text‑embedding‑ada-002 от OpenAI. Найденный эмбеддинг добавляется в словарь, соответствующий тому фрагменту текста, на основе которого он создан. Полученный список словарей содержит всю информацию, необходимую для заполнения поискового индекса.

Теперь мы создаём поисковый индекс, используя API Azure Cognitive Search. Для этого осуществляется настройка полей, которые будут созданы в индексе. При этом нужно не забыть указать то, что поле с эмбеддингами должно поддерживать векторный поиск. Векторный поиск настраивается посредством указания нескольких параметров. Первый — это размер эмбеддинга — 1536 (это всегда так при работе с используемой нами моделью от OpenAI). Второй задаёт использование косинусного коэффициента векторов для проверки их сходства (именно этот метод рекомендует OpenAI). Третий отражает наше желание ускорить поиск похожих векторов, воспользовавшись алгоритмом HNSW. Мы, кроме того, указываем, что хотим включить семантическое ранжирование для контекстных текстовых полей. В итоге мы даём индексу имя и сохраняем его.

И наконец — наши данные выгружаются в только что созданный поисковый индекс. Проверить, все ли фрагменты были загружены в поисковый индекс, можно, зайдя на портал Azure, щёлкнув по ресурсу Cognitive Search, затем — по имени ресурса, а потом — по Indexes. Там, справа, должен находиться нужный индекс со сведениями о количестве документов, которое соответствует количеству фрагментов документов, загруженных в индекс. В моём случае в индекс было загружено 45 фрагментов документов. Если вы попали на страницу индекса, щёлкнув по его имени, просмотреть загруженные в него данные можно, щёлкнув по кнопке Search в блоке Search Explorer.

https://miro.medium.com/v2/resize:fit:700/1*eZXu8FOoWShxki4pxDPkng.png

Данные, загруженные в поисковый индекс

Если, в вышеупомянутом репозитории, посмотреть на файлы init_search из папок проектов, основанных на LangChain и Semantic Kernel, эквивалентные тому, который мы только что разобрали, то окажется, что в них содержится гораздо меньше кода. Непосредственное использование API Azure Cognitive Search даёт нам более широкие возможности управления настройками индекса. А библиотека, применяемая в проекте, играет роль промежуточного слоя между программистом и Azure, который скрывает множество сложных механизмов. Ответ на вопрос о том, что лучше использовать в том или ином проекте, всецело зависит от того, что именно нужно программисту.

Теперь, когда мы подготовили к работе поисковый индекс, поговорим о том, как воспользоваться им в чат‑боте. Разберём файл main_1.py. Это — главный исполняемый файл, обеспечивающий функционирование чат‑бота:

"""
Точка входа для чат-бота.
"""
from chatbot_1 import Chatbot


def main():
    chatbot = Chatbot()
    chatbot.ask("I need a large backpack. Which one do you recommend?")
    chatbot.ask("How much does it cost?")
    chatbot.ask("And how much for a donut?")


if __name__ == "__main__":
    main()

Функция main играет роль пользователя, который задаёт чат-боту несколько вопросов. «Задать вопрос» — значит вызвать метод чат-бота ask. Эквивалентные файлы в папках проектов LangChain и Semantic Kernel выглядят точно так же.

Теперь посмотрим на файл chatbot_1.py. Он содержит код для чат-бота, который помнит историю общения и пользуется возможностями RAG:

"""
Чат-бот, поддерживающий контекстные сведения и запоминающий историю беседы.
"""
import os

import openai
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from azure.search.documents.models import Vector
from dotenv import load_dotenv

# Настройки Azure Search.
AZURE_SEARCH_ENDPOINT = os.getenv("AZURE_SEARCH_ENDPOINT")
AZURE_SEARCH_KEY = os.getenv("AZURE_SEARCH_KEY")
AZURE_SEARCH_INDEX_NAME = "products-index-1"

# Настройки Azure OpenAI.
AZURE_OPENAI_API_TYPE = "azure"
AZURE_OPENAI_API_BASE = os.getenv("AZURE_OPENAI_API_BASE")
AZURE_OPENAI_API_VERSION = "2023-03-15-preview"
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
AZURE_OPENAI_CHATGPT_DEPLOYMENT = os.getenv("AZURE_OPENAI_CHATGPT_DEPLOYMENT")
AZURE_OPENAI_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT")

# Роли чата
SYSTEM = "system"
USER = "user"
ASSISTANT = "assistant"


class Chatbot:
    """Чат с LLM с применением RAG. Хранит в памяти историю чата."""

    chat_history = []

    def __init__(self):
        load_dotenv()
        openai.api_type = AZURE_OPENAI_API_TYPE
        openai.api_base = AZURE_OPENAI_API_BASE
        openai.api_version = AZURE_OPENAI_API_VERSION
        openai.api_key = AZURE_OPENAI_API_KEY

    def _summarize_user_intent(self, query: str) -> str:
        """
        Создаёт сообщение пользователя, выражающее его реальное намерение, путём объединения
истории чата и запроса пользователя.
        """
        chat_history_str = ""
        for entry in self.chat_history:
            chat_history_str += f"{entry['role']}: {entry['content']}\n"
        messages = [
            {
                "role": SYSTEM,
                "content": (
                    "You're an AI assistant reading the transcript of a conversation "
                    "between a user and an assistant. Given the chat history and "
                    "user's query, infer user real intent."
                    f"Chat history: ```{chat_history_str}```\n"
                    f"User's query: ```{query}```\n"
                ),
            }
        ]
        chat_intent_completion = openai.ChatCompletion.create(
            deployment_id=AZURE_OPENAI_CHATGPT_DEPLOYMENT,
            messages=messages,
            temperature=0.7,
            max_tokens=1024,
            n=1,
        )
        user_intent = chat_intent_completion.choices[0].message.content

        return user_intent

    def _get_context(self, user_intent: str) -> list[str]:
        """
        Получает подходящие документы из Azure Cognitive Search.
        """
        query_vector = Vector(
            value=openai.Embedding.create(
                engine=AZURE_OPENAI_EMBEDDING_DEPLOYMENT, input=user_intent
            )["data"][0]["embedding"],
            fields="embedding",
        )

        search_client = SearchClient(
            endpoint=AZURE_SEARCH_ENDPOINT,
            index_name=AZURE_SEARCH_INDEX_NAME,
            credential=AzureKeyCredential(AZURE_SEARCH_KEY),
        )

        docs = search_client.search(search_text="", vectors=[query_vector], top=1)
        context_list = [doc["content"] for doc in docs]

        return context_list

    def _rag(self, context_list: list[str], query: str) -> str:
        """
        Просит LLM сформировать ответ на запрос пользователя с использованием предоставленных контекстных сведений.
        """
        user_message = {"role": USER, "content": query}
        self.chat_history.append(user_message)

        context = "\n\n".join(context_list)
        messages = [
            {
                "role": SYSTEM,
                "content": (
                    "You're a helpful assistant.\n"
                    "Please answer the user's question using only information you can "
                    "find in the context.\n"
                    "If the user's question is unrelated to the information in the "
                    "context, say you don't know.\n"
                    f"Context: ```{context}```\n"
                ),
            }
        ]
        messages = messages + self.chat_history

        chat_completion = openai.ChatCompletion.create(
            deployment_id=AZURE_OPENAI_CHATGPT_DEPLOYMENT,
            messages=messages,
            temperature=0.7,
            max_tokens=1024,
            n=1,
        )

        response = chat_completion.choices[0].message.content
        assistant_message = {"role": ASSISTANT, "content": response}
        self.chat_history.append(assistant_message)

        return response

    def ask(self, query: str) -> str:
        """
        Выполняет запросы к LLM с использованием RAG.
        """
        user_intent = self._summarize_user_intent(query)
        context_list = self._get_context(user_intent)
        response = self._rag(context_list, query)
        print(
            "*****\n"
            f"QUESTION:\n{query}\n"
            f"USER INTENT:\n{user_intent}\n"
            f"RESPONSE:\n{response}\n"
            "*****\n"
        )

        return response

Чат‑бот хранит список chat_history, содержащий вопросы и ответы. Поэтому он может рассматривать вопросы в контексте всей беседы. Класс Chatbot обладает единственной публичной функцией — ask, при выполнении которой используются следующие механизмы, представленные несколькими функциями:

Функция _summarize_user_intent использует LLM для перефразирования вопроса пользователя с учётом истории чата. Зачем это нужно? Представьте себе, что пользователь задаёт вопрос, который сам по себе особого смысла не имеет, но при этом, если рассмотреть его в контексте истории чата, это — совершенно нормальный вопрос. Например — если в вопросе упоминается «это» (it) по отношению к чему‑то, о чём речь шла ранее. Если поискать в индексе документы, имеющие отношение исключительно к вопросу пользователя, то, вероятно, хороших результатов получить не удастся. Но если перефразировать вопрос, включив в него историю чата, мы получим гораздо более качественный набор документов. Скоро, демонстрируя сеанс общения с чат‑ботом, я это вам покажу.
Функция _get_context производит поиск по ранее созданному индексу. Она ищет документы, имеющие отношение к намерению пользователя, сформулированному на предыдущем шаге.
Функция _rag предлагает LLM дать ответ на запрос пользователя, основываясь на документах, возвращённых системой поиска, и на истории чата. На этом шаге мы обновляем историю чата, добавляя в неё сообщения пользователя и ассистента.

Если посмотреть на файлы, эквивалентные этому, расположенные в папках проектов LangChain и Semantic Kernel, то окажется, что и там и там используются API для работы с шаблонами, с помощью которых конструируют промпты, отправляемые LLM. Вы, кроме того, заметите, что в библиотеке LangChain имеется встроенная поддержка хранения истории чата. А фреймворк Semantic Kernel, с другой стороны, построен вокруг концепции функций (фрагментов кода, пригодных для многократного использования) и плагинов (коллекций функций, которые могут вызываться внешними приложениями стандартизированным способом).

Вот как выглядит сеанс общения с чат‑ботом:

*****
QUESTION:
I need a large backpack. Which one do you recommend?
USER INTENT:
User's intent: The user is looking for a recommendation for a large backpack.
RESPONSE:
Based on the information in the context, the SummitClimber Backpack has a dedicated laptop compartment that can accommodate laptops up to 17 inches and it also has a hydration sleeve and tube port, making it compatible with most hydration bladders for convenient on-the-go hydration. However, it's important to keep in mind the cautionary notes and warranty information provided as well. If you're looking for a backpack larger than the SummitClimber Backpack, I don't have that information available.
*****

*****
QUESTION:
How much does it cost?
USER INTENT:
User's real intent: The user wants to know the price of the SummitClimber Backpack that the assistant recommended.
RESPONSE:
The price of the SummitClimber Backpack is $120.
*****

*****
QUESTION:
And how much for a donut?
USER INTENT:
User's real intent: This query does not seem related to the previous conversation about backpacks and may be a joke or a non-serious question.
RESPONSE:
I'm sorry, I don't understand your question. Is there anything else I can assist you with?
*****

В дополнение к вопросам и ответам я привожу тут и сведения о намерении пользователя, что позволит вам лучше почувствовать то, в чём польза включения таких сведений в запрос к LLM. Как видите, второй вопрос, если не принимать во внимание историю чата, выглядит неясным, но сведения о намерении пользователя не оставляют неоднозначностей, включая в себя знания о беседе, полученные чат‑ботом после первого вопроса.

Последний ответ показывает, что система отвечает на наши вопросы, полагаясь только на предоставленные ей документы, а не на общую информацию, которую LLM могла усвоить в процессе обучения.

Итоги

В этом материале мы рассмотрели паттерн RAG, который позволяет расширять предварительно обученные LLM, давая им доступ к пользовательским данным. Мы поговорили об исследовательской работе, где был представлен RAG, о том, как этот паттерн применяется на практике, о том, какие способы поиска информации обычно используются при его реализации. В конце статьи мы разобрали пример кода, демонстрирующий реализацию RAG с использованием OpenAI и Azure Cognitive Search.

Если вам интересно взглянуть на полноценное чат‑приложение, основанное на RAG, в состав которого входит клиентский код, в котором использовался передовой опыт создания проектов корпоративного уровня, рекомендую обратиться к репозиторию Azure Chat, созданному моими коллегами из Microsoft. Этот проект демонстрирует решение множества распространённых задач, и, уверена, знакомство с ним сэкономит вам немало времени.

Надеюсь, этот материал вдохновил вас на использование RAG в своих разработках. Спасибо, что дочитали до этого места и удачи вам с вашими ИИ‑проектами!

О, а приходите к нам работать? 🤗 💰

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11 дек. 2023 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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17.07.24 02:26 thompsonrickey

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27.08.24 12:50 James889900

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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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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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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
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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

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

Although recovering from the terrible effects of investment fraud can seem like an impossible task, it is possible to regain financial stability and go on with the correct assistance and tools. In my own experience with Wizard Web Recovery, a specialized company that assisted me in navigating the difficulties of recouping my losses following my fall prey to a sophisticated online fraud, that was undoubtedly the case. My life money had disappeared in an instant, leaving me in a state of shock when I first contacted Spartan Tech Group Retrieval through this Email: spartantechretrieval (@) g r o u p m a i l .c o m The compassionate and knowledgeable team there quickly put my mind at ease, outlining a clear and comprehensive plan of action. They painstakingly examined every aspect of my case, using their broad business contacts and knowledge to track the movement of my pilfered money. They empowered me to make knowledgeable decisions regarding the rehabilitation process by keeping me updated and involved at every stage. But what I valued most was their unrelenting commitment and perseverance; they persisted in trying every option until a sizable amount of my lost money had been successfully restored. It was a long and arduous journey, filled with ups and downs, but having Spartan Tech Group Retrieval in my corner made all the difference. Thanks to their tireless efforts, I was eventually able to rebuild my financial foundation and reclaim a sense of security and control over my life. While the emotional scars of investment fraud may never fully heal, working with this remarkable organization played a crucial role in my ability to move forward and recover. For proper talks, contact on WhatsApp:+1 (971) 4 8 7 - 3 5 3 8 and Telegram:+1 (581) 2 8 6 - 8 0 9 2 Thank you for your time reading as it will be of help.
27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

Online crypto investment can seem like a promising opportunity, but it's crucial to recognize that there are no guarantees. My experience serves as a stark reminder of this reality. I was drawn in by the allure of high returns and the persuasive marketing tactics employed by various brokers. Their polished presentations and testimonials made it seem easy to profit from cryptocurrency trading. Everything appeared to be legitimate. I received enticing messages about the potential for substantial gains, and the brokers seemed knowledgeable and professional. Driven by excitement and the fear of missing out, I invested a significant amount of my savings. The promise of quick profits overshadowed the red flags I should have noticed. I trusted these brokers without conducting proper research, which was a major mistake. As time went on, I realized that the promised returns were nothing but illusions. My attempts to withdraw funds were met with endless excuses and delays. It became painfully clear that I had fallen victim. The reality hit hard: my hard-earned money was gone, I lost my peace of mind and sanity. In my desperation, I sought help from a company called DEFTRECOUP. That was the turning point for me as I had a good conversation and eventually filed a complaint via DEFTRECOUP COM. They were quite delicate and ensured I got out of the most difficult situation of my life in one piece.
04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

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

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY... A few months ago, I made a huge mistake. I invested in what seemed like a legitimate crypto opportunity, only to find out I’d been scammed. I lost a significant amount of money, and the scam platform vanished overnight. I felt completely lost.I had heard of Fastfund Recovery and decided to reach out, even though I was skeptical. From the first conversation, they made me feel heard and understood. They explained the recovery process clearly and kept me updated every step of the way.Within weeks, Fastfund Recovery successfully to recovered my lost funds—something I honestly didn’t think was possible. Their team was professional, transparent, and genuinely caring. I can’t thank them enough for turning a nightmare into a hopeful outcome. If you’re in a similar situation, don’t hesitate to contact them. They truly deliver on their promises. Gmail::: fastfundrecovery8(@)gmail com .....Whatsapp ::: 1::807::::500::::7554
19.12.24 17:07 rebeccabenjamin

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27.12.24 00:21 swiftdream

I lost about $475,000.00 USD to a fake cryptocurrency trading platform a few weeks back after I got lured into the trading platform with the intent of earning a 15% profit daily trading on the platform. It was a hell of a time for me as I could hardly pay my bills and got me ruined financially. I had to confide in a close friend of mine who then introduced me to this crypto recovery team with the best recovery SWIFTDREAM i contacted them and they were able to completely recover my stolen digital assets with ease. Their service was superb, and my problems were solved in swift action, It only took them 48 hours to investigate and track down those scammers and my funds were returned to me. I strongly recommend this team to anyone going through a similar situation with their investment or fund theft to look up this team for the best appropriate solution to avoid losing huge funds to these scammers. Send complaint to Email: info [email protected]
31.12.24 04:53 Annette_Phillips

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04.01.25 04:56 florencestella

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04.01.25 04:57 florencestella

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06.01.25 19:09 michaeljordan15

We now live in a world where most business transactions are conducted through Bitcoin and cryptocurrency. With the rapid growth of digital currencies, everyone seems eager to get involved in Bitcoin and cryptocurrency investments. This surge in interest has unfortunately led to the rise of many fraudulent platforms designed to exploit unsuspecting individuals. People are often promised massive profits, only to lose huge sums of money when they realize the platform they invested in was a scam. contact with WhatsApp: +1 (443) 859 - 2886 Email @ digitaltechguard.com Telegram: digitaltechguardrecovery.com website link:: https://digitaltechguard.com This was exactly what happened to me five months ago. I was excited about the opportunity to invest in Bitcoin, hoping to earn a steady return of 20%. I found a platform that seemed legitimate and made my investment, eagerly anticipating the day when I would be able to withdraw my earnings. When the withdrawal day arrived, however, I encountered an issue. My bank account was not credited, despite seeing my balance and the supposed profits in my account on the platform. At first, I assumed it was just a technical glitch. I thought, "Maybe it’s a delay in the system, and everything will be sorted out soon." However, when I tried to contact customer support, the line was either disconnected or completely unresponsive. My doubts started to grow, but I wanted to give them the benefit of the doubt and waited throughout the day to see if the situation would resolve itself. But by the end of the day, I realized something was terribly wrong. I had been swindled, and my hard-earned money was gone. The realization hit me hard. I had fallen victim to one of the many fraudulent Bitcoin platforms that promise high returns and disappear once they have your money. I knew I had to act quickly to try and recover what I had lost. I started searching online for any possible solutions, reading reviews and recommendations from others who had faced similar situations. That’s when I came across many positive reviews about Digital Tech Guard Recovery. After reading about their success stories, I decided to reach out and use their services. I can honestly say that Digital Tech Guard Recovery exceeded all my expectations. Their team was professional, efficient, and transparent throughout the process. Within a short time, they helped me recover a significant portion of my lost funds, which I thought was impossible. I am incredibly grateful to Digital Tech Guard Recovery for their dedication and expertise in helping me get my money back. If you’ve been scammed like I was, don’t lose hope. There are solutions, and Digital Tech Guard Recovery is truly one of the best. Thank you, Digital Tech Guard Recovery! You guys are the best. Good luck to everyone trying to navigate this challenging space. Stay safe.
18.01.25 12:41 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
18.01.25 12:41 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
20.01.25 15:39 patricialovick86

How To Recover Your Bitcoin Without Falling Victim To Scams: A Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected]
22.01.25 21:43 DoraJaimes23

Recovery expert. I lost my bitcoin to fake blockchain impostors on Facebook, they contacted me as blockchain official support and i fell stupidly for their mischievous act, this made them gain access into my blockchain wallet whereby 7.0938 btc was stolen from my wallet in total .I was almost in a comma and dumbfounded because this was all my savings i relied on . Then I made a research online and found a recovery expert , with the contact address- { RECOVERYHACKER101 (@) GMAIL . COM }... I wrote directly to the specialist explaining my loss. Hence, he helped me recover a significant part of my investment just after 2 days he helped me launch the recovery program , and the culprits were identified as well , all thanks to his expertise . I hope I have been able to help someone as well . Reach out to the recovery specialist to recover you lost funds from any form of online scam Thanks
23.01.25 02:36 [email protected]

After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3
23.01.25 02:37 [email protected]

After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3
23.01.25 14:20 nellymargaret

DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT
23.01.25 14:20 nellymargaret

DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT I had tried to secure my Bitcoin wallet, maybe a bit too aggressively, enabling every security feature imaginable: two-factor authentication, biometric verification, intricate passwords-the whole shebang. I wanted to make it impossible for anybody to get to my money. I tried to make this impregnable fortress of security and ended up locking myself out of my wallet with $700,000 in Bitcoin. It wasn't until I tried to access my wallet that I realized the trap I had set for myself. I was greeted with an endless series of security checks-passwords, codes, facial recognition, and more. I could remember parts of my multi-layered security setup but not enough to actually get in. In fact, my money was behind this digital fortress, and the more I tried to fix it, the worse it seemed to get. I kept tripping over my own layers of protection, unable to find a way back in. Panic quickly set in when I realized I had made it almost impossible for myself to access my own money. That is when I called DUNAMIS CYBER SOLUTION From that very first call, they reassured me that I wasn't the first person to make this kind of mistake and certainly wouldn't be the last. They listened attentively to my explanation and got to work straight away. Their team methodically began to untangle my overly complicated setup. Patience and expertise managed to crack each layer of security step by step until they had restored access to my wallet. [email protected] +13433030545 [email protected]
26.01.25 03:54 [email protected]

Losing access to my crypto wallet account was one of the most stressful experiences ever. After spending countless hours building up my portfolio, I suddenly found myself locked out of my account without access. To make matters worse, the email address I had linked to my wallet was no longer active. When I tried reaching out, I received an error message stating that the domain was no longer in use, leaving me in complete confusion and panic. It was as though everything I had worked so hard for was gone, and I had no idea how to get it back. The hardest part wasn’t just the loss of access it was the feeling of helplessness. Crypto transactions are often irreversible, and since my wallet held significant investments, the thought that my hard-earned money could be lost forever was incredibly disheartening. I spent hours scouring forums and searching for ways to recover my funds, but most of the advice seemed either too vague or too complicated to be of any real help. With no support from the wallet provider and my email account out of reach, I was left feeling like I had no way to fix the situation.That’s when I found out about Trust Geeks Hack Expert . I was hesitant at first, but after reading about their expertise in recovering lost crypto wallets, I decided to give them a try. I reached out to their team, and from the very beginning, they were professional, understanding, and empathetic to my situation. They quickly assured me that there was a way to recover my wallet, and they got to work immediately.Thanks to Trust Geeks Hack Expert , my wallet and funds were recovered, and I couldn’t be more grateful. The process wasn’t easy, but their team guided me through each step with precision and care. The sense of relief I felt when I regained access to my crypto wallet and saw my funds safely back in place was indescribable. If you find yourself in a similar situation, I highly recommend reaching out to Trust Geeks Hack Expert. contact Them through EMAIL: [email protected] + WEBSITE. HTTPS://TRUSTGEEKSHACKEXPERT.COM + TELE GRAM: TRUSTGEEKSHACKEXPERT
28.01.25 21:48 [email protected]

It’s unfortunate that many people have become victims of scams, and some are facing challenges accessing their Bitcoin wallets. However, there's excellent news! With Chris Wang, you can count on top-notch service that guarantees results in hacking. We have successfully helped both individuals and organizations recover lost files, passwords, funds, and more. If you need assistance, don’t hesitate—check out recoverypro247 on Google Mail! What specific methods does Chris Wang use to recover lost funds and passwords? Are there any guarantees regarding the success rate of the recovery services offered? What are the initial steps to begin the recovery process with recoverypro247? this things i tend to ask
02.02.25 20:53 Michael9090

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00:04 Jannetjeersten

TECH CYBER FORCE RECOVERY quickly took action, filing my case and working tirelessly on my behalf. Within just four days, I received the surprising news that my 40,000 CAD had been successfully refunded and deposited back into my bank account. I was overjoyed and relieved to see the money returned, especially after the stressful experience. Thanks to TECH CYBER FORCE RECOVERY’s professionalism and dedication, I was able to recover my funds. This experience taught me an important lesson about being cautious with online investments and the importance of seeking expert help when dealing with scams. I am truly grateful to EMAIL: support(@)techcyberforcerecovery(.)com OR WhatsApp: +.1.5.6.1.7.2.6.3.6.9.7 for their assistance, which allowed me to reclaim my money and end the holiday season on a much brighter note.