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Методы построения RAG систем

Процесс Retrieval-Augmented Generation (RAG) представляет собой довольно сложную систему, состоящую из множества компонентов. Вопрос о том, как определить существующие методы RAG и их оптимальные комбинации для выявления лучших практик, в настоящий момент остается наиболее актуальным. В этой статье я хочу поделиться своим опытом относительно реализации подходов и практик в области RAG систем, который реализует систематический подход к решению этой проблемы.

Типовые задачи процессов RAG систем

  • Классификация запросов,

  • Деление на фрагменты

  • Векторизация данных

  • Поиск,

  • Переранжирование,

  • Обобщение данных .

  • Генерация ответа

Каждый из этих этапов играет важную роль в обеспечении точности и эффективности системы. Например, классификация запросов помогает определить, нужно ли вообще выполнять поиск, что может значительно сократить время обработки. Переранжирование и переупаковка документов улучшают релевантность результатов, а обобщение помогает устранить избыточность и улучшить качество ответов.

На мой взгляд, ключевая сложность RAG заключается в том, что каждый этап требует тщательной настройки и выбора оптимальных методов. Например, как выбрать размер фрагментов для поиска? Какую эмбеддинг модель использовать? Эти вопросы требуют не только теоретического анализа, но и практических экспериментов.

Попробуем рассмотреть все этапы разработки участвующие в структуре обработке данных RAG систем

Типовая карта разработки RAG систем
Типовая карта разработки RAG систем

Как видно из визуализации необходимых шагов разработки, задачи делятся на три уровня уровень подготовки, обработки и классификации запроса, уровень подготовки, разделения, векторизации и хранения документов и уровень переранжирования, генерации и обобщения ответа. Схематично все этапы можно представить в виде схемы:

795191fdd1c90ee1e6576929d10862c9.png

Рассмотрим все этапы поочередно.

Классификация запросов

Классификация запросов — это первый шаг, который позволяет определить, нужен ли поиск вообще. Не все запросы требуют дополнительного поиска, особенно если LLM (Large Language Model) модель уже обладает достаточными знаниями. Например, если запрос основан исключительно на информации, предоставленной пользователем, поиск может быть излишним.

Определяем тип запроса:

  • Фактический поиск (например, «Сколько стоит построить дом?»)

  • Мнение или оценка («Что лучше кирпич или каркас?»)

  • Рассуждение или синтез информации («Где лучше выбирать землю для строительства»)

  • Личный или неформальный запрос («Как ты думаешь лучше построить дом или жить в квартире?»)

Выбираем стратегию поиска:

  • Если запрос требует фактической информации, задействуется поиск в базе знаний или векторный поиск.

  • Если запрос сложный, комбинируется несколько источников информации.

  • Если ответ можно дать без поиска, LLM модель отвечает напрямую.

Производим фильтрацию и модерацию:

  • Проверка на запрещённый контент, токсичность, конфиденциальные данные.

  • Обнаружение нерелевантных или потенциально вредных запросов.

Методы классификации запросов

  • Лингвистический анализ (разбор структуры предложения, ключевых слов).

  • ML-модели классификации (обученные на размеченных данных, например, BERT или GPT).

  • Правила и эвристики (например, наличие вопросительных слов или ключевых фраз).

Пример классификации запросов с использованием библиотеки transformers и модели BERT

import torch
from transformers import pipeline

# Загружаем предобученную модель для классификации
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Возможные классы запросов
labels = ["фактический поиск", "мнение", "рассуждение", "личный вопрос", "неопределённый"]

# Функция классификации запроса
def classify_query(query):
    result = classifier(query, candidate_labels=labels)
    return result["labels"][0], result["scores"][0]

# Примеры запросов
queries = [
    "Сколько стоит построить дом?",
    "Как ты думаешь, погрузчик лучше, чем экскаватор?",
    "Объясни принципы работы ДВС.",
    "Стоит ли мне переехать в другой город?",
    "Просто хочу поговорить."
]

# Классифицируем запросы
for query in queries:
    label, score = classify_query(query)
    print(f"Запрос: {query}\nКлассификация: {label} (уверенность: {score:.2f})\n")

Результат выполнения:

Запрос: Сколько стоит построить дом?
Классификация: фактический поиск (уверенность: 0.24)

Запрос: Как ты думаешь, погрузчик лучше, чем экскаватор?
Классификация: личный вопрос (уверенность: 0.28)

Запрос: Объясни принципы работы ДВС.
Классификация: рассуждение (уверенность: 0.34)

Запрос: Стоит ли мне переехать в другой город?
Классификация: мнение (уверенность: 0.30)

Запрос: Просто хочу поговорить.
Классификация: рассуждение (уверенность: 0.26)

На мой взгляд автоматизация этого процесса с помощью классификатора — это лучшее решение. Однако важно учитывать, что классификатор должен быть обучен на разнообразных данных, чтобы избежать ошибок в реальных сценариях.

Разделение на части

Разделение документов на фрагменты — это критически важный этап, который влияет на точность поиска. В основе лежит три уровня фрагментации: на уровне токенов, семантическая фрагментация и фрагментация на уровне предложений. На мой взгляд, фрагментация на уровне предложений является наиболее сбалансированным подходом, так как она сохраняет семантику текста, оставаясь при этом достаточно простой для реализации.

В RAG разделение документов на фрагменты (chunking) — один из кючевых этапов, который влияет на эффективность поиска и генерации ответов. В этом процессе определяется, какие части текста будут использоваться для векторного представления и поиска в дальнейшем.
Зачем делить?

  1. Что бы оптимизировать поиск, поскольку векторные поисковые системы ( FAISS, Chroma, Weaviate) работают эффективнее с короткими фрагментами.

  2. Чтобы снизить поисковый шум, поскольку при индексации слишком больших кусков текста, поиск может выдавать нерелевантные результаты.

  3. Чтобы обеспечить контекстную релевантность, поскольку разбиение помогает извлекать только нужную информацию и не перегружать LLM лишними данными. Как делить?

  4. Кусками фиксированными по размеру (Fixed-length chunking) Разделение текста на куски фиксированной длины, например, по 512 или 1024 токена. Плюсы: ✔ Простая реализация. ✔ Хорошо работает с длинными документами. Минусы: ✖ Может разделять связанный контекст. ✖ Потеря смысла при разрыве на границе.

  5. Семантическими кусками (Semantic chunking) Использует модели NLP (например, BERT) для разбиения по смысловым блокам (абзацы, заголовки, смысловые единицы). Плюсы: ✔ Сохраняет логическую целостность. ✔ Улучшает точность поиска. Минусы: ✖ Более сложная реализация. ✖ Может приводить к неравномерным размерам фрагментов.

  6. По структурным элементам (Structural chunking) Использует разбиение по логическим единицам документа (заголовки, списки, таблицы). Плюсы: ✔ Хорошо подходит для технических документов, статей и справочников. ✔ Сохраняет контекст. Минусы: ✖ Требует предварительной обработки документов. ✖ Не всегда применимо к неструктурированным данным.

Пример фиксированного разделения по 512 токенов

from transformers import AutoTokenizer

# Загружаем токенизатор (используем BERT)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

def chunk_text(text, max_tokens=512):
    tokens = tokenizer.encode(text, add_special_tokens=False)
    chunks = [tokens[i:i+max_tokens] for i in range(0, len(tokens), max_tokens)]
    return [tokenizer.decode(chunk) for chunk in chunks]

# Тестовый текст
text = "Иску́сственный интелле́кт (англ. artificial intelligence; AI) в самом широком смысле — это интеллект, демонстрируемый машинами, в частности компьютерными системами. Это область исследований в области компьютерных наук, которая разрабатывает и изучает методы и программное обеспечение, позволяющие машинам воспринимать окружающую среду и использовать обучение и интеллект для выполнения действий, которые максимально увеличивают их шансы на достижение поставленных целей[1]. Такие машины можно назвать искусственным интеллектом. Некоторые из наиболее известных приложений искусственного интеллекта включают в себя передовые поисковые системы (например, Google Search, Bing, Яндекс); рекомендательные системы (используемые на YouTube, Amazon и Netflix); взаимодействие посредством человеческой речи (например, Google Assistant, Siri, Alexa, Алиса); автономные транспортные средства (например, Waymo); генеративные и творческие инструменты (например, ChatGPT, Apple Intelligence и искусство искусственного интеллекта); а также сверхчеловеческую игру и анализ в стратегических играх (например, шахматы и го). Однако многие приложения искусственного интеллекта не воспринимаются как искусственный интеллект: «Многие передовые разработки искусственного интеллекта проникли в общие приложения, часто не называясь искусственным интеллектом, потому что как только что-то становится достаточно полезным и достаточно распространённым, его больше не называют искусственным интеллектом»[2][3]. Алан Тьюринг был первым человеком, который провёл масштабные исследования в области, которую он назвал машинным интеллектом[4]. Искусственный интеллект был основан как академическая дисциплина в 1956 году[5] теми, кого сейчас считают отцами-основателями искусственного интеллекта: Джоном Маккарти, Марвином Мински, Натаниэлем Рочестером[англ.] и Клодом Шенноном[6][7]. Эта область пережила несколько циклов оптимизма[8], за которыми последовали периоды разочарования и потери финансирования, известные как зима искусственного интеллекта[9]. Финансирование и интерес значительно возросли после 2012 года, когда глубокое обучение превзошло все предыдущие методы искусственного интеллекта[10], а также после 2017 года с появлением архитектуры Transformer[11]. Это привело к буму искусственного интеллекта в начале 2020-х годов, когда компании, университеты и лаборатории, в подавляющем большинстве базирующиеся в Соединённых Штатах, стали пионерами значительных достижений в области искусственного интеллекта[12]. Растущее использование искусственного интеллекта в XXI веке влияет на общественный и экономический сдвиг в сторону большей автоматизации, принятия решений на основе данных и интеграции систем искусственного интеллекта в различные секторы экономики и сферы жизни, влияя на рынки труда, здравоохранение, государственное управление, промышленность, образование, пропаганду и дезинформацию. Это поднимает вопросы о долгосрочных эффектах, этических последствиях и рисках искусственного интеллекта, побуждая к обсуждениям о политике регулирования, направленной на обеспечение безопасности и преимуществ этой технологии. Различные направления исследований искусственного интеллекта сосредоточены вокруг определённых целей и использования определённых инструментов. Традиционные цели исследований искусственного интеллекта включают рассуждение, представление знаний, планирование, обучение, обработку естественного языка, восприятие и поддержку робототехники[13]. Общий интеллект, или же сильный, — способность выполнять любую задачу, которую может выполнить человек, по крайней мере на равном уровне — входит в число долгосрочных целей данной области[14]. Для достижения этих целей исследователи искусственного интеллекта адаптировали и интегрировали широкий спектр методов, включая поиск и математическую оптимизацию, формальную логику, искусственные нейронные сети, а также методы, основанные на статистике, исследовании операций и экономике[13]. Искусственный интеллект также опирается на психологию, лингвистику, философию, нейронауку и другие области[15]."

# Разбиваем на куски по 512 токенов
chunks = chunk_text(text)

for i, chunk in enumerate(chunks):
    print(f"Фрагмент {i+1}:\n{chunk}\n")

Результат выполнения:

Фрагмент 1: искусственный интеллект ( англ. artificial intelligence ; ai ) в самом широком смысле — это интеллект, демонстрируемыи машинами, в частности компьютерными системами. это область исследовании в области компьютерных наук, которая разрабатывает и изучает методы и программное обеспечение, позволяющие машинам воспринимать окружающую среду и использовать обучение и интеллект для выполнения деиствии, которые максимально увеличивают их шансы на достижение поставленных целеи [ 1 ]. такие машины можно назвать искусственным интеллектом. некоторые из наиболее известных приложении искусственного интеллекта включают в себя пере

Фрагмент 2: ##довые поисковые системы ( например, google search, bing, яндекс ) ; рекомендательные системы ( используемые на youtube, amazon и netflix ) ; взаимодействие посредством человеческой речи ( например, google assistant, siri, alexa, Алиса ) ; автономные транспортные средства ( например, waymo ) ; генеративные и творческие инструменты ( например, chatgpt, apple intelligence и искусство искусственного интеллекта ) ; а также сверхчеловеческую игру и анализ в стратегических играх ( например, шахматы и го ). однако многие приложения искусственного интеллекта не воспринимаются как искусственный интеллект: « многие передовые разработки искусственного интеллекта проникли в общие приложен

Фрагмент 3: ##ия, часто не называясь искусственным интеллектом, потому что как только что - то становится достаточно полезным и достаточно распространенным, его больше не называют искусственным интеллектом » [ 2 ] [ 3 ]. алан тьюринг был первым человеком, который провел масштабные исследования в области, которую он назвал машинным интеллектом [ 4 ]. искусственный интеллект был основан как академическая дисциплина в 1956 году [ 5 ] теми, кого сеичас считают отцами - основателями искусственного интеллекта : джоном маккарти, марвином мински, натаниэлем рочестером [ англ. ] и клодом шенноном [ 6 ] [ 7 ]. эта область пережила несколько цикл

Пример семантического разделения с использованием spaCy для разбиения текста на смысловые блоки:

import spacy

# Загружаем модель NLP
nlp = spacy.load("ru_core_news_sm")  

def semantic_chunking(text, max_sentences=3):
 
    doc = nlp(text)
    sentences = [sent.text for sent in doc.sents]
    
    # Группируем предложения по max_sentences
    chunks = []
    for i in range(0, len(sentences), max_sentences):
        chunk = " ".join(sentences[i:i+max_sentences])
        chunks.append(chunk)
    
    return chunks

# Пример текста
text = "Иску́сственный интелле́кт (англ. artificial intelligence; AI) в самом широком смысле — это интеллект, демонстрируемый машинами, в частности компьютерными системами. Это область исследований в области компьютерных наук, которая разрабатывает и изучает методы и программное обеспечение, позволяющие машинам воспринимать окружающую среду и использовать обучение и интеллект для выполнения действий, которые максимально увеличивают их шансы на достижение поставленных целей[1]. Такие машины можно назвать искусственным интеллектом. Некоторые из наиболее известных приложений искусственного интеллекта включают в себя передовые поисковые системы (например, Google Search, Bing, Яндекс); рекомендательные системы (используемые на YouTube, Amazon и Netflix); взаимодействие посредством человеческой речи (например, Google Assistant, Siri, Alexa, Алиса); автономные транспортные средства (например, Waymo); генеративные и творческие инструменты (например, ChatGPT, Apple Intelligence и искусство искусственного интеллекта); а также сверхчеловеческую игру и анализ в стратегических играх (например, шахматы и го). Однако многие приложения искусственного интеллекта не воспринимаются как искусственный интеллект: «Многие передовые разработки искусственного интеллекта проникли в общие приложения, часто не называясь искусственным интеллектом, потому что как только что-то становится достаточно полезным и достаточно распространённым, его больше не называют искусственным интеллектом»[2][3]. Алан Тьюринг был первым человеком, который провёл масштабные исследования в области, которую он назвал машинным интеллектом[4]. Искусственный интеллект был основан как академическая дисциплина в 1956 году[5] теми, кого сейчас считают отцами-основателями искусственного интеллекта: Джоном Маккарти, Марвином Мински, Натаниэлем Рочестером[англ.] и Клодом Шенноном[6][7]. Эта область пережила несколько циклов оптимизма[8], за которыми последовали периоды разочарования и потери финансирования, известные как зима искусственного интеллекта[9]. Финансирование и интерес значительно возросли после 2012 года, когда глубокое обучение превзошло все предыдущие методы искусственного интеллекта[10], а также после 2017 года с появлением архитектуры Transformer[11]. Это привело к буму искусственного интеллекта в начале 2020-х годов, когда компании, университеты и лаборатории, в подавляющем большинстве базирующиеся в Соединённых Штатах, стали пионерами значительных достижений в области искусственного интеллекта[12]. Растущее использование искусственного интеллекта в XXI веке влияет на общественный и экономический сдвиг в сторону большей автоматизации, принятия решений на основе данных и интеграции систем искусственного интеллекта в различные секторы экономики и сферы жизни, влияя на рынки труда, здравоохранение, государственное управление, промышленность, образование, пропаганду и дезинформацию. Это поднимает вопросы о долгосрочных эффектах, этических последствиях и рисках искусственного интеллекта, побуждая к обсуждениям о политике регулирования, направленной на обеспечение безопасности и преимуществ этой технологии. Различные направления исследований искусственного интеллекта сосредоточены вокруг определённых целей и использования определённых инструментов. Традиционные цели исследований искусственного интеллекта включают рассуждение, представление знаний, планирование, обучение, обработку естественного языка, восприятие и поддержку робототехники[13]. Общий интеллект, или же сильный, — способность выполнять любую задачу, которую может выполнить человек, по крайней мере на равном уровне — входит в число долгосрочных целей данной области[14]. Для достижения этих целей исследователи искусственного интеллекта адаптировали и интегрировали широкий спектр методов, включая поиск и математическую оптимизацию, формальную логику, искусственные нейронные сети, а также методы, основанные на статистике, исследовании операций и экономике[13]. Искусственный интеллект также опирается на психологию, лингвистику, философию, нейронауку и другие области[15]."


# Разделяем на семантические фрагменты
chunks = semantic_chunking(text)

# Вывод результатов
for i, chunk in enumerate(chunks):
    print(f"🔹 Фрагмент {i+1}:\n{chunk}\n")

Результат выполнения:

Фрагмент 1:
Иску́сственный интелле́кт (англ. artificial intelligence; AI) в самом широком смысле — это интеллект, демонстрируемый машинами, в частности компьютерными системами. Это область исследований в области компьютерных наук, которая разрабатывает и изучает методы и программное обеспечение, позволяющие машинам воспринимать окружающую среду и использовать обучение и интеллект для выполнения действий, которые максимально увеличивают их шансы на достижение поставленных целей[1]. Такие машины можно назвать искусственным интеллектом.

Фрагмент 2:
Некоторые из наиболее известных приложений искусственного интеллекта включают в себя передовые поисковые системы (например, Google Search, Bing, Яндекс); рекомендательные системы (используемые на YouTube, Amazon и Netflix); взаимодействие посредством человеческой речи (например, Google Assistant, Siri, Alexa, Алиса); автономные транспортные средства (например, Waymo); генеративные и творческие инструменты (например, ChatGPT, Apple Intelligence и искусство искусственного интеллекта); а также сверхчеловеческую игру и анализ в стратегических играх (например, шахматы и го). Однако многие приложения искусственного интеллекта не воспринимаются как искусственный интеллект: «Многие передовые разработки искусственного интеллекта проникли в общие приложения, часто не называясь искусственным интеллектом, потому что как только что-то становится достаточно полезным и достаточно распространённым, его больше не называют искусственным интеллектом»[2][3]. Алан Тьюринг был первым человеком, который провёл масштабные исследования в области, которую он назвал машинным интеллектом[4].

Фрагмент 3:
Искусственный интеллект был основан как академическая дисциплина в 1956 году[5] теми, кого сейчас считают отцами-основателями искусственного интеллекта: Джоном Маккарти, Марвином Мински, Натаниэлем Рочестером[англ.] и Клодом Шенноном[6][7]. Эта область пережила несколько циклов оптимизма[8], за которыми последовали периоды разочарования и потери финансирования, известные как зима искусственного интеллекта[9]. Финансирование и интерес значительно возросли после 2012 года, когда глубокое обучение превзошло все предыдущие методы искусственного интеллекта[10], а также после 2017 года с появлением архитектуры Transformer[11].

Разделение на фрагменты, критически влияет на качество поиска и генерации. Выбор стратегии зависит от типа документов и задач системы.

  • Фиксированные размеры — подходят для больших текстов.

  • Семантическое разбиение — даёт более осмысленные куски.

  • Структурное разбиение — полезно для технических данных. Лучший подход — комбинировать методы в зависимости от контекста!

Выбираем эмбеддинг модель

Embedding модель выполняет основную функцию в поиске релевантных документов. Опыт показывает, что важно сочетать высокую производительность с небольшим размером модели. Важно также отметить, что выбор модели может зависеть от конкретной задачи и доступных ресурсов.

Как это работает?

Модель преобразует текст в многомерное векторное представление. Эти векторы затем используются для поиска похожих фрагментов в базе данных.

При выборе модели важно определить:

Язык и специфику текстовых данных(домен)

  • Если система работает на английском → OpenAI ada-002, BGE, SBERT

  • Для русского → DeepPavlov/RuBERT, sbert_large_nlu_ru

  • Для кодовых или медицинских данных → CodeBERT, BioBERT

Оптимальный размер векторов и скорость

  • Короткие вектора (384–512) быстрее, но менее точны.

  • Длинные вектора (1024+) дают точный поиск, но требуют больше памяти.

Способность к извлечению смысловых связей

  • Косинусное сходство лучше работает с SBERT, BGE

  • Для длинных текстов лучше подходят E5 или Cohere

Совместимость с базами данных

  • Если используется FAISS/Chroma → SBERT, BGE, ada-002

  • Если используется Milvus/Weaviate → OpenAI, Cohere

Сравнительная таблица моделей векторного представления
Сравнительная таблица моделей векторного представления

Пример векторизации данных

from sentence_transformers import SentenceTransformer

# Загружаем модель
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

# Примеры фрагментов текста
texts = [
    "RAG использует внешнюю базу знаний для генерации ответов.",
    "Модели встраивания помогают искать релевантную информацию.",
    "Глубокие нейронные сети улучшают обработку естественного языка."
]

# Встраиваем текст в вектора
embeddings = model.encode(texts)

# Выводим размер вектора
print(f"Размер вектора: {len(embeddings[0])}")
print(f"Пример вектора: {embeddings[0][:5]}")  # Выведем первые 5 значений

Результат выполнения:

Размер вектора: 384

Пример вектора: [-0.00326376 0.07672146 0.01198601 -0.01142005 -0.04499646]

Выбираем векторную базу данных

Поскольку векторная база данных отвечает за хранение и быстрый поиск релевантных фрагментов информации. Выбор подходящей базы данных влияет на скорость работы, точность поиска и масштабируемость системы.

З что отвечает векторная база данных?

  • Хранение текстовых фрагментов в виде многомерных векторов.

  • Поиск наиболее похожих векторов по запросу.

  • Использование индексов для быстрого поиска (ANN — Approximate Nearest Neighbors).

Я постарался выделить основные критерии при выборе

  1. Размер данных и масштабируемость Небольшой обьем данных → FAISS, Chroma Если нужен масштабируемый поиск → Weaviate, Milvus, Qdrant

  2. Скорость поиска Если важна быстрая обработка → FAISS, ScaNN (Google) Если важен онлайн-доступ и REST API → Weaviate, Pinecone

  3. Поддержка гибридного поиска (векторный + классический full-text) Weaviate, Qdrant, Vespa позволяют комбинировать векторный и текстовый поиск.

  4. Простота интеграции FAISS, Chroma — легко использовать в Python. Pinecone, Weaviate, Qdrant — удобны для облачных решений.

Сравнительная таблица векторных баз данных
Сравнительная таблица векторных баз данных

Пример поиска схожих векторов

import faiss
import numpy as np
from sentence_transformers import SentenceTransformer

# Загружаем модель встраивания
model = SentenceTransformer("all-MiniLM-L6-v2")

# Исходные текстовые фрагменты
texts = [
    "Как работает RAG?",
    "Векторные базы данных хранят embeddings.",
    "Машинное обучение улучшает поиск информации.",
    "Глубокие нейросети используются в NLP."
]

# Преобразуем текст в векторы
embeddings = model.encode(texts)
dimension = embeddings.shape[1]  # Определяем размерность векторов

# Создаём индекс FAISS
index = faiss.IndexFlatL2(dimension)
index.add(np.array(embeddings))  # Добавляем векторы в базу

# Запрос (тоже преобразуем в вектор)
query = "Как работают векторные базы?"
query_embedding = model.encode([query])

# Ищем 2 ближайших вектора
D, I = index.search(np.array(query_embedding), k=2)

# Выводим результаты
print("Найденные фрагменты:")
for idx in I[0]:
    print(f"- {texts[idx]}")

Результат выполнения:

Найденные фрагменты:

  • Как работает RAG?

  • Машинное обучение улучшает поиск информации.

Организуем Поиск и переранжирование

Методы поиска, такие как генерация запросов, значительно улучшают релевантность результатов. Однако хочу отметить, что эти методы могут быть ресурсоемкими, что важно учитывать в реальных приложениях.

  1. Поиск (Retrieval) На этом этапе система выполняет векторный поиск или гибридный поиск (комбинация векторного и классического текстового поиска).

Методы поиска:

  • Векторный поиск (ANN – Approximate Nearest Neighbors): сравнение эмбеддингов запроса и базы (FAISS, Weaviate, Pinecone).

  • Классический поиск (BM25): поиск по ключевым словам (Elasticsearch, Weaviate, Qdrant).
    Гибридный поиск (BM25 + ANN): объединение двух методов для повышения точности.

  1. Переранжирование (Re-ranking) После поиска полученные документы ранжируются по релевантности. Простые методы (BM25) могут возвращать нерелевантные результаты, поэтому используется доранжировка (re-ranking) с помощью нейросетей.

Модели ранжирования:

  • Cross-Encoder (SBERT) – дообученная модель для определения наиболее релевантных фрагментов.

  • Cohere Rerank – облачная модель для улучшения поиска.

  • OpenAI reranking (GPT) – доранжировка с помощью GPT.

Сравнение подходов к поиску и ранжированию
Сравнение подходов к поиску и ранжированию

Пример поиска и переранжирования

import faiss
import numpy as np
from sentence_transformers import SentenceTransformer, CrossEncoder

# Загружаем модели
retrieval_model = SentenceTransformer("all-MiniLM-L6-v2")  # Для поиска
reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")  # Для переранжирования


# Исходные текстовые фрагменты
documents = [
    "LLM могут генерировать ответы на основе найденных данных.",
    "Поиск информации возможен через BM25 и векторный поиск.",
    "Глубокие нейросети помогают анализировать текст.",
    "RAG использует векторные базы данных для поиска."
]

# Преобразуем документы в эмбеддинги
doc_embeddings = retrieval_model.encode(documents)
dimension = doc_embeddings.shape[1]

# Создаём FAISS индекс
index = faiss.IndexFlatL2(dimension)
index.add(np.array(doc_embeddings))

# Запрос пользователя
query = "Как работает поиск в RAG?"
query_embedding = retrieval_model.encode([query])

# Поиск 3 ближайших фрагментов
D, I = index.search(np.array(query_embedding), k=3)
retrieved_docs = [documents[idx] for idx in I[0]]

# Переранжировка найденных документов
scores = reranker.predict([(query, doc) for doc in retrieved_docs])
ranked_docs = [doc for _, doc in sorted(zip(scores, retrieved_docs), reverse=True)]

# Вывод результатов
print("Найденные документы (после переранжировки):")
for doc in ranked_docs:
    print(f"- {doc}")

Пример выполнения:

Найденные документы (после переранжировки):

RAG использует векторные базы данных для поиска.

Поиск информации возможен через BM25 и векторный поиск.

Глубокие нейросети помогают анализировать текст.

Реализуем резюмирование и генерацию ответа

Резюмирование извлеченных документов — это этап, который помогает устранить избыточность и улучшить качество ответов. Рекомендуется сочетать экстрактивные и генеративные подходы, такой подход обеспечивает высокую точность и эффективность.

После того, как система извлекла фрагменты из нашей базы знаний и провела их ранжирование, следует обработка текста для выделения ключевых идей:

В процессе обработки данных происходит удаление несущественных деталей: Например, могут быть убраны лишние слова или избыточная информация, не влияющая на ответ. Также может быть выполнено предобразование текста, что может включать переформулировку или упрощение фрагментов, чтобы сделать информацию более доступной для восприятия

Далее с помощью Подготовка контекста для генерации:
После извлечения релевантных документов и фрагментов, эти данные формируют контекст, который используется для генерации ответа.

Контекст включает как информацию из извлеченных документов, так и сам запрос пользователя. Важно, чтобы система правильно сопоставила запрос с нужными фрагментами из документов.

В некоторых случаях этот контекст дополнительно структурируется или очищается, чтобы исключить лишние или неподобающие данные, которые могут мешать генерации.генеративных моделей создается ответ на основе обработанных фрагментов.

Генеративная модель может взять информацию из множества фрагментов и «сжать» её в лаконичный и точный ответ. Лучше всего для таких задачподходят те модели , которые способны связно генерировать текст на основе предоставленного контекста. Также модели могут использовать механизмы внимания (attention) для «запоминания» ключевых частей контекста, необходимого для корректного ответа.

Последним частью обобщения является согласование. Важно, чтобы все данные в ответе были логично скомбинированы и не противоречили друг другу. Реализация должна отвечать принципам референтности, когда модели должны следить, чтобы ссылки на факты были точными, а также согласованности, когда ответ должен быть логично выстроен, чтобы не происходило резких скачков мыслей.

Модели для резюмирования в RAG-системах

T5 (Text-to-Text Transfer Transformer) - Модель, которая обучена на множестве текстовых задач и может выполнять задачи генерации текста, включая резюмирование.
Хорошо подходит для генерации резюме текста и формирования сжато сформулированного ответа.

BART (Bidirectional and Auto-Regressive Transformers) - Модель для сжимающего и абстрактного резюмирования. Работает как энкодер-декодер, извлекая информацию из текста и генерируя логичный, сжато изложенный ответ.

Преимущество следует отдавать моделям способным генерировать ответы с использованием предоставленного контекста. Например GPT Они могут не только осуществлять резюмирование, но и выполнять сложные генеративные задачи, включая диалоговые интерфейсы.

Пример простой суммаризации

from transformers import pipeline

# Загружаем модель для резюмирования (BART)
summarizer = pipeline("summarization", model="sberbank-ai/rugpt3small_sum")

# Пример текста
text = """
В Retrieval-Augmented Generation (RAG) используется две ключевые технологии: извлечение информации и генерация ответа. 
Система сначала находит релевантные фрагменты из базы данных, а затем генерирует на основе этих фрагментов связанный ответ. 
Процесс резюмирования помогает сделать ответы более лаконичными, снижая объем ненужной информации и представляя только основные факты.
"""

# Генерация резюме
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)

# Вывод резюме
print("Обобщенный  текст:", summary[0]['summary_text'])

Результат выполенния:

Обобщенный текст: В Retrieval-Augmented Generation (RAG) используется извлечение информации и генерация ответа. Резюмирование помогает снизить объем информации и представить только основные факты.

При выборе стратегий резюмирования в RAG-системах следует опираться на их особенности, при использовании сжимающего резюмирования (Extractive Summarization) система выбирает существенные фрагменты из исходного текста, не изменяя их, и объединяет их для формирования ответа. Такой подход проще, но ограничивает гибкость.

В случае применения абстрактного резюмирование (Abstractive Summarization) модель переформулирует исходные данные своими словами, что дает больше гибкости и возможность создавать более связные и логичные ответы. Это сложнее, но результат часто более точен и полезен.

Выбор стратегий построения RAG систем лежит между максиамльной производительностью включает все модули для достижения максимальной точности и сбалансировной эффективностью, данная стратегия оптимизирует производительность и эффективность, исключая некоторые ресурсоемкие методы.

На мой взгляд, выбор стратегии зависит от конкретных требований проекта. Например, если важна скорость обработки, можно пожертвовать некоторой точностью ради эффективности. Однако в задачах, где точность критически важна, лучше использовать все модули.

В этой статье я обратил внимание на несколько важных идей:

  1. Важность построения модульной конструкции: Оптимизация каждого компонента по отдельности позволяет создавать гибкие и легко настраиваемые системы. Это особенно важно в сложных проектах, где требования могут меняться со временем.

  2. Систематический подход к практической реализации: приведённый опыт демонстрирует, как тщательное тестирование на общепризнанных наборах данных может обеспечить надежность результатов. Данные примеры реализации могут пригодится, при выборе и анализе реализации этапов RAG .

  3. Существующие практические ограничения: Несмотря на все преимущества RAG, существуют проблемы, такие как обобщение на частные данные, производительность в реальном времени и интеграция многомодальных данных. Эти вопросы требуют дополнительного изучения при построении подобных систем .

На мой взгляд предложенные методы и стратегии могут значительно улучшить качество и эффективность систем RAG. Однако важно помнить, что каждая задача уникальна, и успешное внедрение RAG требует не только следования лучшим практикам, но и адаптации к конкретным условиям.

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  • 07.09.23 16:24 CherryTeam

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

  • 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.

  • 24.06.24 04:31 tashandiarisha

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  • 26.06.24 18:46 Jacobethannn098

    LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER

  • 26.06.24 18:46 Jacobethannn098

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  • 04.07.24 04:49 ZionNaomi

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  • 13.07.24 21:13 michaelharrell825

    In 2020, amidst the economic fallout of the pandemic, I found myself unexpectedly unemployed and turned to Forex trading in hopes of stabilizing my finances. Like many, I was drawn in by the promise of quick returns offered by various Forex robots, signals, and trading advisers. However, most of these products turned out to be disappointing, with claims that were far from reality. Looking back, I realize I should have been more cautious, but the allure of financial security clouded my judgment during those uncertain times. Amidst these disappointments, Profit Forex emerged as a standout. Not only did they provide reliable service, but they also delivered tangible results—a rarity in an industry often plagued by exaggerated claims. The positive reviews from other users validated my own experience, highlighting their commitment to delivering genuine outcomes and emphasizing sound financial practices. My journey with Profit Forex led to a net profit of $11,500, a significant achievement given the challenges I faced. However, my optimism was short-lived when I encountered obstacles trying to withdraw funds from my trading account. Despite repeated attempts, I found myself unable to access my money, leaving me frustrated and uncertain about my financial future. Fortunately, my fortunes changed when I discovered PRO WIZARD GIlBERT RECOVERY. Their reputation for recovering funds from fraudulent schemes gave me hope in reclaiming what was rightfully mine. With a mixture of desperation and cautious optimism, I reached out to them for assistance. PRO WIZARD GIlBERT RECOVERY impressed me from the start with their professionalism and deep understanding of financial disputes. They took a methodical approach, using advanced techniques to track down the scammers responsible for withholding my funds. Throughout the process, their communication was clear and reassuring, providing much-needed support during a stressful period. Thanks to PRO WIZARD GIlBERT RECOVERY's expertise and unwavering dedication, I finally achieved a resolution to my ordeal. They successfully traced and retrieved my funds, restoring a sense of justice and relief. Their intervention not only recovered my money but also renewed my faith in ethical financial services. Reflecting on my experience, I've learned invaluable lessons about the importance of due diligence and discernment in navigating the Forex market. While setbacks are inevitable, partnering with reputable recovery specialists like PRO WIZARD GIlBERT RECOVERY can make a profound difference. Their integrity and effectiveness have left an indelible mark on me, guiding my future decisions and reinforcing the value of trustworthy partnerships in achieving financial goals. I wholeheartedly recommend PRO WIZARD GIlBERT RECOVERY to anyone grappling with financial fraud or disputes. Their expertise and commitment to client satisfaction are unparalleled, offering a beacon of hope in challenging times. Thank you, PRO WIZARD GIlBERT RECOVERY, for your invaluable assistance in reclaiming what was rightfully mine. Your service not only recovered my funds but also restored my confidence in navigating the complexities of financial markets with greater caution and awareness. Email: prowizardgilbertrecovery(@)engineer.com Homepage: https://prowizardgilbertrecovery.xyz WhatsApp: +1 (516) 347‑9592

  • 17.07.24 02:26 thompsonrickey

    In the vast and often treacherous realm of online investments, I was entangled in a web of deceit that cost me nearly  $45,000. It all started innocuously enough with an enticing Instagram profile promising lucrative returns through cryptocurrency investment. Initially, everything seemed promising—communications were smooth, and assurances were plentiful. However, as time passed, my optimism turned to suspicion. Withdrawal requests were met with delays and excuses. The once-responsive "investor" vanished into thin air, leaving me stranded with dwindling hopes and a sinking feeling in my gut. It became painfully clear that I had been duped by a sophisticated scheme designed to exploit trust and naivety. Desperate to recover my funds, I turned to online forums where I discovered numerous testimonials advocating for Muyern Trust Hacker. With nothing to lose, I contacted them, recounting my ordeal with a mixture of skepticism and hope. Their swift response and professional demeanor immediately reassured me that I had found a lifeline amidst the chaos. Muyern Trust Hacker wasted no time in taking action. They meticulously gathered evidence, navigated legal complexities, and deployed their expertise to expedite recovery. In what felt like a whirlwind of activity, although the passage of time was a blur amidst my anxiety, they achieved the seemingly impossible—my stolen funds were returned. The relief I felt was overwhelming. Muyern Trust Hacker not only restored my financial losses but also restored my faith in justice. Their commitment to integrity and their relentless pursuit of resolution were nothing short of remarkable. They proved themselves as recovery specialists and guardians against digital fraud, offering hope to victims like me who had been ensnared by deception. My gratitude knows no bounds for Muyern Trust Hacker. Reach them at muyerntrusted @ m a i l - m e . c o m AND Tele gram @ muyerntrusthackertech

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  • 27.08.24 12:50 James889900

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  • 02.09.24 20:24 [email protected]

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  • 06.09.24 01:35 Celinagarcia

    HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you ​​​​​​have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.

  • 06.09.24 01:44 Celinagarcia

    HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you ​​​​​​have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.

  • 16.09.24 00:10 marcusaustin

    Bitcoin Recovery Services: Restoring Lost Cryptocurrency If you've lost access to your cryptocurrency and unable to make a withdrawal, I highly recommend iBolt Cyber Hacker Bitcoin Recovery Services. Their team is skilled, professional, and efficient in recovering lost Bitcoin. They provide clear communication, maintain high security standards, and work quickly to resolve issues. Facing the stress of lost cryptocurrency, iBolt Cyber Hacker is a trusted service that will help you regain access to your funds securely and reliably. Highly recommended! Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /

  • 16.09.24 00:11 marcusaustin

    Bitcoin Recovery Services: Restoring Lost Cryptocurrency If you've lost access to your cryptocurrency and unable to make a withdrawal, I highly recommend iBolt Cyber Hacker Bitcoin Recovery Services. Their team is skilled, professional, and efficient in recovering lost Bitcoin. They provide clear communication, maintain high security standards, and work quickly to resolve issues. Facing the stress of lost cryptocurrency, iBolt Cyber Hacker is a trusted service that will help you regain access to your funds securely and reliably. Highly recommended! Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /

  • 23.09.24 18:56 matthewshimself

    At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.

  • 01.10.24 14:54 Sinewclaudia

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  • 31.10.24 00:13 ytre89

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    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.

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  • 27.11.24 09:10 Michal Novotny

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  • 04.12.24 22:24 andreygagloev

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  • 12.12.24 00:35 amandagregory

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  • 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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  • 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

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    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

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  • 23.01.25 02:37 [email protected]

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  • 23.01.25 14:20 nellymargaret

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  • 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

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    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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  • 06.02.25 19:42 Marta Golomb

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  • 08.02.25 05:45 [email protected]

    I'm incredibly grateful that I did enough research to recover my stolen cryptocurrency. When I first fell victim to a scam, I felt hopeless and lost, unsure if I'd ever see my funds again. A few months ago, I was approached by someone on Telegram who claimed to have a lucrative investment opportunity in cryptocurrencies. They promised huge returns and played on my emotions, making it seem like a can't-miss chance. I was so eager to make my money grow that I didn't fully vet the situation, and unfortunately, I ended up falling for the scam. They guided me to invest a significant amount of money, and soon after, I realized I had been duped. The scammers blocked me, and my funds were gone. I felt devastated. All of my savings had been wiped out in what seemed like an instant, and the feeling of being taken advantage of was crushing. I spent days researching how to recover my stolen cryptocurrency but found the process to be overwhelming and complicated. I was starting to lose hope when I came across Trust Geeks Hack Expert. At first, I was skeptical about reaching out to a cryptocurrency recovery company, but after reading testimonials and researching their reputation, I decided to give them a try. I contacted Trust Geeks Hack Expert Website: www://trustgeekshackexpert.com/, and I was immediately reassured by their professionalism and expertise. They took the time to listen to my situation, and they were honest about what could and could not be done. What stood out to me was their deep understanding of cryptocurrency fraud and the recovery process. They were able to track down the scammers and initiate the recovery of my stolen funds, step by step. Thanks to Trust Geeks Hack Expert, I was able to get back a significant portion of the cryptocurrency I had lost. Their team was responsive, transparent, and diligent in their efforts. I was kept informed throughout the entire process, and they made sure I felt supported every step of the way. I truly can't thank them enough for their dedication and for restoring my faith in the possibility of recovery after such a devastating loss. I will definitely recommend Trust Geeks Hack Expert to anyone who has fallen victim to a cryptocurrency scam. TeleGram: Trustgeekshackexpert & what's A p p  +1 7 1 9 4 9 2 2 6 9 3

  • 08.02.25 05:46 [email protected]

    I'm incredibly grateful that I did enough research to recover my stolen cryptocurrency. When I first fell victim to a scam, I felt hopeless and lost, unsure if I'd ever see my funds again. A few months ago, I was approached by someone on Telegram who claimed to have a lucrative investment opportunity in cryptocurrencies. They promised huge returns and played on my emotions, making it seem like a can't-miss chance. I was so eager to make my money grow that I didn't fully vet the situation, and unfortunately, I ended up falling for the scam. They guided me to invest a significant amount of money, and soon after, I realized I had been duped. The scammers blocked me, and my funds were gone. I felt devastated. All of my savings had been wiped out in what seemed like an instant, and the feeling of being taken advantage of was crushing. I spent days researching how to recover my stolen cryptocurrency but found the process to be overwhelming and complicated. I was starting to lose hope when I came across Trust Geeks Hack Expert. At first, I was skeptical about reaching out to a cryptocurrency recovery company, but after reading testimonials and researching their reputation, I decided to give them a try. I contacted Trust Geeks Hack Expert Website: www://trustgeekshackexpert.com/, and I was immediately reassured by their professionalism and expertise. They took the time to listen to my situation, and they were honest about what could and could not be done. What stood out to me was their deep understanding of cryptocurrency fraud and the recovery process. They were able to track down the scammers and initiate the recovery of my stolen funds, step by step. Thanks to Trust Geeks Hack Expert, I was able to get back a significant portion of the cryptocurrency I had lost. Their team was responsive, transparent, and diligent in their efforts. I was kept informed throughout the entire process, and they made sure I felt supported every step of the way. I truly can't thank them enough for their dedication and for restoring my faith in the possibility of recovery after such a devastating loss. I will definitely recommend Trust Geeks Hack Expert to anyone who has fallen victim to a cryptocurrency scam. TeleGram: Trustgeekshackexpert & what's A p p  +1 7 1 9 4 9 2 2 6 9 3

  • 10.02.25 21:22 sulabhakuchchal

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  • 04:24 heyemiliohutchinson

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