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[Перевод] Гуглить баги — это нормально. Как AI Debugger освоил этот навык и сам исправляет ошибки

315502f9379fc61481ea35ac17d35706.jpg

Автоматический дебаг с помощью языковых моделей уже не новость, и разработчики используют LLM‑модели и среды разработки с интегрированным ИИ, чтобы анализировать код и предлагать исправления. Но что если встроить в этот процесс ещё один мощный инструмент — поиск в интернете?

В этой статье мы рассмотрим необычный подход: автоматический дебаггер, который не просто анализирует код с помощью нейросети, но и при необходимости отправляет сформулированные моделью поисковые запросы в интернете. Если модель не уверена в исправлении, она делает то же, что и человек, — ищет похожие ошибки и готовые решения в сети.

Код на GitHub

Если вы хотите посмотреть всю магию в действии, загляните в мой репозиторий GitHub — там хранится полная реализация.

Как всё организовано? Вот дерево файлов — оно подскажет, что к чему:

AI-Debugger
│   ├── default_config.py      # Настройки по умолчанию
├── requirements.txt           # Список зависимостей Python для проекта
└── src/
    ├── cli/
    │   ├── main.py            # Точка входа в интерфейс командной строки
    ├── core/
    │   ├── debugger.py        # Главная логика отладки с помощью LLM
    │   ├── llm.py             # Абстрактный базовый класс для взаимодействия с LLM
    │   ├── llm_factory.py     # Фабрика создания экземпляров LLM
    │   ├── utils.py           # Утилитарные функции
    │   │                      # (например, вычисление различий в коде)
    ├── internet/
    │   ├── search.py          # Модуль для выполнения интернет-поиска
    ├── llms/
    │   ├── gemini_llm.py      # Реализация взаимодействия с Google Gemini
    │   ├── huggingface_llm.py # Реализация взаимодействия с Hugging Face
    │   ├── openai_llm.py      # Реализация взаимодействия с OpenAI
    ├── prompts/
    │   ├── query_prompts.py   # Шаблоны промтов для поисковых задач
    └───┴── system_prompts.py  # Системные промты для LLM

Обзор проекта

Как выглядит работа этого бесконечного цикла отладки ИИ? Представим процесс визуально.

c8e8d15f0747991b6bcc52079ceecee5.jpeg

Бесконечный ИИ‑дебаггер начинает с обнаружения потенциальных проблем и проверки наличия ошибок. Если ошибок не обнаружено, процесс завершается. Если ошибка найдена, дебаггер оценивает, требуется ли использование внешней информации. При необходимости он выполняет поиск через такие источники, как Google или Mozilla, извлекает релевантные данные и комбинирует их с внутренними входными данными.

Собранные данные передаются в выбранную LLM (например, HuggingFace, OpenAI, Gemini или локальную модель), которая генерирует ответ для устранения ошибки. Система применяет это решение к коду и снова проверяет наличие ошибок. Если проблема сохраняется, процесс повторяется до её устранения.

Цикл повторяется до тех пор, пока не будут исправлены все проблемы. Такая схема делает процесс максимально эффективным: внешние данные и искусственный интеллект работают в тандеме.

Установка модуля

Начнём с установки. Сначала клонируем репозиторий:

git clone https://github.com/FareedKhan-dev/ai-debugger.git
cd ai-debugger

Установим все необходимые зависимости этой командой:

pip install -r requirements.txt

Импорт библиотек

Давайте импортируем все нужные библиотеки:

import os  # Для взаимодействия с операционной системой
import subprocess  # Позволяет запускать новые процессы
import time  # Предоставляет функции для работы со временем
import re  # Поддержка операций с регулярными выражениями
import transformers  # Библиотека Hugging Face для языковых моделей (LLM)
import torch  # Для тензорных вычислений и глубокого обучения
import logging  # Инструменты для логирования
from scrapling import Fetcher  # Веб-скрейпинг

Получение структуры дерева проекта

Первое, с чего начинается процесс отладки, — это знакомство с проектом. Нужно показать LLM, что у нас есть, и как это организовано. Для этого создадим функцию, которая генерирует структуру дерева каталогов. Давайте напишем её.

def get_dir_tree(start_path, indent=""):
    """
    Рекурсивно создаёт строковое представление структуры дерева каталогов.

    Args:
        start_path (str): Путь к начальной директории.
        indent (str): Отступ текущего уровня (используется для рекурсивных вызовов).

    Returns:
        str: Структура дерева каталогов в виде строки.
    """
    # Инициализация пустой строки для хранения структуры дерева каталогов
    tree_structure = ""

    # Попытка получить список всех элементов в директории
    try:
        items = os.listdir(start_path)
    except PermissionError:
        # Обработка ошибки доступа с указанием ограничения
        return f"{indent}[Доступ запрещён]\n"

    # Перебор всех элементов в текущей директории
    for index, item in enumerate(items):
        # Формирование полного пути к каждому элементу
        item_path = os.path.join(start_path, item)

        # Проверка, является ли текущий элемент последним, для корректного оформления дерева
        is_last_item = index == len(items) - 1

        # Определение префикса для текущего элемента (оформление ветвей дерева)
        prefix = "└── " if is_last_item else "├── "

        # Добавление текущего элемента в структуру дерева с соответствующим отступом
        tree_structure += f"{indent}{prefix}{item}\n"

        # Если текущий элемент — директория, рекурсивно получить её структуру
        if os.path.isdir(item_path):
            # Корректировка отступа для следующего уровня дерева
            new_indent = indent + ("    " if is_last_item else "│   ")
            tree_structure += get_dir_tree(item_path, new_indent)

    return tree_structure

Функция get_dir_tree() — наш проводник: она принимает один аргумент, корневую директорию, и возвращает чёткую структуру всех вложенных папок и файлов. Теперь можно легко визуализировать, как устроен проект. Давайте протестируем, вызвав её для тестовой папки. Вот результат:

# Указываем директорию, структуру которой хотим отобразить
start_directory = "project_code"

# Получаем структуру дерева для указанной директории
directory_tree = get_dir_tree(start_directory)

# Выводим полученную структуру дерева каталогов
print(directory_tree)
### ВЫВОД ###

project_code
├── requirements.txt
├── main.py
└── some_folder/
    ├── sub_folder/
        ...
...

Чтение содержимого

Что дальше? Разумеется, следующий шаг — открыть файлы и понять, что в них внутри. Лишь имея на руках содержимое самих файлов, LLM сможет понять код, который необходимо анализировать и отлаживать. Создадим функцию для выполнения этой задачи.

def read_code_files(directory, files_to_debug=None):
    """
    Считывает содержимое файлов с кодом на Python из указанной директории и возвращает его в виде строки.

    Args:
        directory (str): Директория, содержащая файлы с кодом.
        files_to_debug (list, optional): Список конкретных файлов для отладки. По умолчанию — None.

    Returns:
        str: Строка, содержащая содержимое всех файлов с кодом.
    """
    # Инициализация пустой строки для хранения содержимого всех файлов
    all_code = ""

    # Если список файлов не указан, загружаем все Python-файлы из директории
    if not files_to_debug:
        files_to_debug = [f for f in os.listdir(directory) if f.endswith(".py")]

    # Перебор списка файлов для отладки
    for code_filename in files_to_debug:
        try:
            # Открытие файла для чтения
            with open(os.path.join(directory, code_filename), "r") as file:
                # Добавление имени файла в виде комментария для наглядности
                all_code += f"# {code_filename}\n"
                # Чтение содержимого файла
                code = file.read()
                # Добавление содержимого файла к общей строке
                all_code += code + "\n"
                # Добавление разделителя для удобства восприятия между файлами
                all_code += "_________________\n\n"
        except Exception as e:
            # Обработка ошибок при чтении конкретного файла
            print(f"Ошибка при чтении {code_filename}: {e}")

    # Возврат объединённого содержимого всех файлов
    return all_code

Функция read_code_files() принимает два аргумента: каталог, где находятся файлы кода, и список файлов для отладки, если они переданы в аргументах командной строки. Если же конкретные файлы не указаны, функция будет считывать все Python‑файлы из указанного каталога.

Тестируем. Предположим, что в каталоге проекта два файла: main.py и utils.py.

# Указываем папку с кодом
code_directory = "project_folder"

# Получаем содержимое файлов с кодом
code_content = read_code_files(code_directory)
print(code_content)
### ВЫВОД ###

# main.py
def main():
    print("Здравствуй, свет!")

if __name__ == "__main__":
    main()
_________________

# utils.py
def add(a, b):
    return a + b
_________________

Как видно, файлы были успешно загружены и представлены в виде одной строки. Теперь LLM сможет разобраться в нашем коде.

Логирование ошибок

Следующим этапом мы займёмся поиском ошибок. Нам нужно выполнить каждый Python‑файл, чтобы проверить наличие ошибок. Для этого мы используем питонский модуль subprocess, который позволяет запускать файлы и захватывать их вывод. Эта функция поможет нам точно определить, где и какие ошибки присутствуют в коде, а также предоставит ключевую информацию для отладки.

def run_code_and_capture_errors(directory, files_to_debug=None):
    """
    Выполняет Python-файлы в указанной директории, фиксируя их вывод или ошибки.

    Args:
        directory (str): Директория, где находятся Python-файлы.
        files_to_debug (list, optional): Список файлов для выполнения. Если None, выполняются все Python-файлы в директории.

    Returns:
        str: Отформатированная строка с результатами выполнения для каждого файла.
    """
    results = []  # Список для хранения результатов выполнения

    try:
        # Если список файлов не указан, выбираем все Python-файлы в директории
        if files_to_debug is None:
            files_to_debug = [f for f in os.listdir(directory) if f.endswith('.py')]

        # Перебор каждого файла и его выполнение
        for code_filename in files_to_debug:
            results.append(f"# {code_filename}")
            try:
                # Запуск Python-файла через subprocess с захватом вывода и ошибок
                result = subprocess.run(
                    ["python", os.path.join(directory, code_filename)],
                    capture_output=True,
                    text=True,
                    check=True,
                )
                # Добавление успешного вывода (stdout)
                if result.stdout.strip():
                    results.append(result.stdout.strip())
                else:
                    results.append("ошибок нет")
            except subprocess.CalledProcessError as e:
                # Добавление сообщения об ошибке (stderr)
                if e.stderr.strip():
                    results.append(f"Произошла ошибка: {e.stderr.strip()}")
                else:
                    results.append("Неизвестная ошибка")

    except Exception as e:
        # Обработка неожиданных ошибок (например, неверная директория)
        results.append(f"Произошла ошибка: {str(e)}")

    # Возврат результатов в формате строки
    return "\n\n".join(results)

Функция run_code_and_capture_errors() принимает два аргумента: каталог (directory) и необязательный список имён файлов Python (files_to_debug). Она выполняет каждый файл, захватывая либо вывод, либо ошибки, и возвращает форматированную строку. Результаты возвращаются в формате, где имя файла предваряется символом #, а далее следуют детали вывода или ошибки.

Давайте протестируем эту функцию на каталоге проекта, который содержит только один скрипт test_error.py, содержащий преднамеренную ошибку.

# test_error.py с ошибкой
def test():
    a = 10
    b = "20"
    print (a + b)

test()

Вызовем этот метод для запуска нашего Python‑кода.

# Указываем директорию
code_directory = "project_dor"

# Запуск кода и захват ошибок
error_output = run_code_and_capture_error(code_directory)
print(error_output)
### ВЫВОД ###

# test_error.py
Traceback (most recent call last):
  File "../project_dir/test_error.py", line 3, in test
    print (a + b)
           ~~^~~
TypeError: неподдерживаемые операнды для +: 'int' и 'str'

Системный промт для отладки

Теперь, когда мы можем сообщить модели, какие ошибки существуют, необходимо объяснить ей, как их исправить. Здесь нам и пригодится системный промт — своего рода руководство для LLM.

DEBUG_SYSTEM_PROMPT = """
Вы — эксперт по Python, специализирующийся на обработке ошибок и защитном программировании.
Проанализируйте все предоставленные файлы кода, а также информацию, найденную в интернете.
Исправьте ошибки и улучшите код, усилив его защитные механизмы. Используйте строго следующий формат:

# имя_файла_кода
```python  
# исправленный код
```  

Пропускайте файлы, в которых ошибок не обнаружено.
Применяйте лучшие практики и интегрируйте релевантные данные из поиска.
"""

Добавляем взаимодействие с LLM

Нам нужно создать функцию для LLM, которая будет генерировать код на основе системного и юзер‑промта. Давайте её разработаем.

def HF_llm(model_name, system_prompt, user_prompt):
    """
    Генерирует ответ с использованием языковой модели Hugging Face на основе заданных системного и юзер-промта.

    Args:
    model_name (str): Имя или путь к предобученной языковой модели (например, 'gpt-3', 't5-base').
    system_prompt (str): Промт, задающий роль и поведение модели.
    user_prompt (str): Входные данные пользователя, на которые нужно сгенерировать ответ.

    Returns:
    str: Сгенерированный ответ модели.
    """
    
    # Формируем массив, содержащий системный и юзер-промт
    messages = [
        {"role": "system", "content": system_prompt},  # Системная инструкция или контекст
        {"role": "user", "content": user_prompt},  # Юзер-промт
    ]

    # Инициализируем пайплайн модели Hugging Face для генерации текста
    pipeline = transformers.pipeline(
        "text-generation",  # Задача: генерация текста
        model=model_name,  # Имя модели или путь к ней
        model_kwargs={"torch_dtype": torch.bfloat16},  # Используем bfloat16 для повышения эффективности
        device_map="auto",  # Автоматический выбор устройства (CPU или GPU)
    )

    # Генерируем ответ модели на основе входных данных
    outputs = pipeline(messages)

    # Возвращаем только сгенерированный текст, без метаданных
    return outputs[0]["generated_text"][-1]['content']

Сейчас мы сосредоточимся на LLM, размещённой на Hugging Face: это открытая платформа, на которой можно легко найти и подключить подходящую модель. В то же время GitHub‑репозиторий AIDebugger поддерживает разные модели, в частности OpenAI, Gemini и локальные.

Исправление кода

После того как LLM предоставит исправления, их нужно внести в кодовую базу. Для этого мы создадим функцию, которая заменит проблемные участки.

def update_code_from_llm_response(llm_response):
    """
    Извлекает код из ответа языковой модели и обновляет соответствующие файлы.

    Функция выполняет два основных действия:
    1. Извлекает фрагменты кода Python из ответа языковой модели.
    2. Обновляет соответствующие файлы, заменяя их содержимое.

    Аргументы:
        llm_response (str): Текстовый ответ языковой модели.

    Возвращает:
        None: Функция напрямую обновляет файлы, не возвращая значений.
    """
    # Регулярное выражение для поиска имен файлов и фрагментов кода внутри markdown-блоков
    pattern = r"#\s(\S+)\n```python\n(.*?)\n```"

    # Находим все совпадения в ответе модели (имя файла и соответствующий код)
    matches = re.findall(pattern, llm_response, re.DOTALL)

    # Создаем словарь, сопоставляющий имена файлов с извлеченным кодом
    code_changes = {filename: code.strip() for filename, code in matches}

    # Обновляем содержимое файлов на основе извлеченного кода
    for filename, code in code_changes.items():
        # Полный путь к файлу
        file_path = os.path.join(self.code_dir, filename)

        # Открываем файл в режиме записи и заменяем его содержимое новым кодом
        with open(file_path, "w") as file:
            file.write(code)

Теперь настроены все компоненты: мы можем выявлять ошибки, спрашивать LLM об их исправлении и на основе ответов обновлять файлы исходного кода. Запустим наши функции и посмотрим, как дебаггер исправит код.

Исправляем небольшую кодовую базу

У нас есть небольшая кодовая база, состоящая всего из двух файлов, один из которых содержит ошибку.

# code1.py
import code2

def greet(name):
    print("Привет, " + name)

greet("Алиса")

result = code2.add(10, "5")  # Эта строка — причина ошибки TypeError 
print(result)

# code2.py
def add(a, b):
    return a + b  # В этом месте будет вызвана TypeError

Итак, в файле code1.py вызывается функция add() из подключённого модуля code2, в которую передаются два аргумента: целое число 10 и строка "5". Поскольку эти аргументы имеют разные типы, а функция add() в code2.py не выполняет проверку типов и просто пытается сложить переданные значения, возникает ошибка TypeError. Ошибка происходит внутри функции add() в code2.py, так как операция сложения между числом и строкой является недопустимой без явного приведения типов.

Сначала генерируем юзер‑промт (USER_PROMPT), объединяющий в одну программную строку следующие данные: структуру папок (get_dir_tree(codebase)), файлы исходного кода (read_code_files(codebase)) и логи возникших при выполнении ошибок (run_code_and_capture_error(codebase)).

# Указываем каталог с кодом
codebase = "project_code"

# Получаем структуру каталогов
directory_tree = get_dir_tree(codebase)

# Читаем содержимое файлов с кодом
code_content = read_code_files(codebase)

# Запускаем код и фиксируем ошибки
error_output = run_code_and_capture_error(codebase)

# Формируем юзер-промт для LLMs
USER_PROMPT = f'''Структура файлового дерева проекта:
                   {directory_tree}
                    
                   Содержимое файлов:
                   {code_content}

                   Обнаруженные ошибки:
                   {error_output}
               '''

Вот как будет выглядеть этот юзер‑промт:

Структура файлового дерева проекта:
                    
project_code
├── code1.py
├── code2.py


Содержимое файлов:

# code1.py
import code2

def greet(name):
    print("Привет, " + name)

greet("Алиса")

result = code2.add(10, "5")  # Эта строка — причина ошибки TypeError 
print(result)

# code2.py
def add(a, b):
    return a + b  # В этом месте будет вызвана TypeError

Обнаруженные ошибки:

TypeError: неподдерживаемые операнды для +: 'int' и 'str'

Теперь, наконец, передаём сгенерированный промт в языковую модель Hugging Face (HF_llm) для анализа и генерации исправлений. Модель определит ошибки в нашем коде и предложит способы их устранения.

# Вызываем функцию HF_llm с системным и пользовательским промтами
llm_response = HF_llm(
    model_name="meta-llama/Meta-Llama-3.1-3B-Instruct",  # Пример модели
    system_prompt=DEBUG_SYSTEM_PROMPT,
    user_prompt=USER_PROMPT
)

# Выводим ответ для просмотра исправлений
print(llm_response)


### ВЫВОД ###
code1.py
import code2
...

Мы выбрали модель Llama-3.2–3B*, которая оптимизирована для запуска даже на бюджетных видеокартах благодаря небольшому количеству параметров — 3 миллиарда. Несмотря на компактность, она поддерживает контекстное окно объёмом 128K, что делает её подходящей для анализа длинных фрагментов исходного кода.

Запустим функцию обновления файлов кода и посмотрим, как изменился файл code1.py.

# Обновляем код на основе ответа LLM
update_code_from_llm_response(llm_response)
Файл code1.py был обновлён — взглянем, как он выглядит после внесения исправлений:
# code1.py
import code2

def greet(name):
    print("Привет, " + name)

greet("Алиса")

result = code2.add(10, 5) # Исправлена ошибка типов
print(result)

Ура, ошибка типа устранена. Но заметим, что пока мы не применяли механизм бесконечного цикла, а значит, LLM не будет повторно корректировать код до полного их истребления.

Создание бесконечного цикла отладки

Итак, давайте разработаем этот бесконечный цикл, который будет работать до тех пор, пока в нашем коде не останется ошибок.

def infinite_debug_loop(codebase_dir, model_name, system_prompt):
    """
    Запускает бесконечный цикл отладки, в котором LLM исправляет код до полного устранения ошибок.

    Args:
    codebase_dir (str): Директория с кодом для отладки.
    model_name (str): Название модели Hugging Face для генерации исправлений.
    system_prompt (str): Системные инструкции для LLM.
    
    Returns:
    None: Функция напрямую обновляет кодовую базу.
    """
    iteration = 1
    while True:
        # Шаг 1: Получаем структуру файлов и содержимое кода
        directory_tree = get_dir_tree(codebase_dir)
        code_content = read_code_files(codebase_dir)

        # Шаг 2: Запускаем код и фиксируем ошибки
        error_output = run_code_and_capture_errors(codebase_dir)

        # Если ошибок нет, завершаем цикл
        if "Error occurred" not in error_output and "Traceback" not in error_output:
            print("Ошибок в коде не найдено, отладка завершена.")
            break

        # Шаг 3: Формируем запрос для LLM
        user_prompt = f'''Структура файлов проекта:
                          {directory_tree}

                          Содержимое файлов:
                          {code_content}

                          Обнаруженные ошибки:
                          {error_output}
                       '''

        # Шаг 4: Отправляем запрос LLM для исправления кода
        print(f"Итерация {iteration}: Отправка запроса в LLM для исправлений.")
        llm_response = HF_llm(model_name=model_name, system_prompt=system_prompt, user_prompt=user_prompt)

        # Шаг 5: Обновляем код на основе ответа LLM
        update_code_from_llm_response(llm_response)

        # Шаг 6: Небольшая пауза перед следующей итерацией
        iteration += 1
        print(f"Итерация {iteration}: Код обновлён. Переход к следующей итерации...\n")
        time.sleep(2)  # Опционально: можно изменить время ожидания

Пройдёмся ещё раз по коду. В начале каждой итерации применяется функция get_dir_tree(), которая сканирует структуру каталога, создавая текстовое представление проекта. Затем метод read_code_files() извлекает содержимое файлов, чтобы их можно было передать в юзер‑промт. Код проверяемого проекта запускается функцией run_code_and_capture_errors(), которая проверяет, содержатся ли в выводе ключевые слова, сигнализирующие об ошибка: Error occurred либо Traceback. Если ошибок больше не проявляется, то цикл завершается.

Приведённый код демонстрирует простой бесконечный цикл, который работает до тех пор, пока в нашем коде не останется ошибок. Однако есть и немного расширенная версия в GitHub‑репозитории AI Debugger, где обрабатываются более сложные сценарии и ситуации.

Тестирование цикла

Давайте добавим новый, чуть более сложный сценарий кода. Этот проект будет состоять из трёх файлов, каждый из которых содержит различные типы ошибок.

# calculator.py
def add(a, b):
    return a + b
def subtract(a, b):
  return a - b
def multiply(a,b):
    return a * b
def divide(a,b):
   return a/b


# main.py
import calculator
def main():
    x = 10
    y = "5"
    z = 0
    result_add = calculator.add(x, y)
    print(f"Сложение: {result_add}")
    result_sub = calculator.subtract(x, y)
    print(f"Вычитание: {result_sub}")
    result_mul = calculator.multiply(x,y)
    print(f"Умножение: {result_mul}")
    result_div = calculator.divide(x,z)
    print(f"Деление: {result_div}")
if __name__ == "__main__":
    main()


# utils.py
def greet(name):
    message = "Привет, " + name
  return message

Ошибки, которые мы намереваемся обнаружить:

  • TypeError: функции add(), subtract() и multiply(), где ожидаются целые числа, вызываются с аргументами строкового типа.

  • ZeroDivisionError: ошибка деления на ноль в функции divide(), так как z = 0.

  • IndentationError: Оператор return неправильно выровнен по отступу.

Сценарий подготовлен, вызовем функцию infinite_debug_loop(), чтобы она занялась исправлением:

# Указываем директорию с кодом
codebase_dir = "complex_project_dir"

# Задаём название модели и системный промпт для LLM
model_name = "meta-llama/Meta-Llama-3.1-3B-Instruct"  # или другая модель HF

system_prompt = DEBUG_SYSTEM_PROMPT

# Запускаем бесконечный цикл отладки для исправления кода
infinite_debug_loop(codebase_dir, model_name, system_prompt)

Этот фрагмент кода задаёт директорию проекта, выбирает модель LLM и её системный промт, а затем вызывает функцию infinite_debug_loop(). Что происходит дальше? Запускается процесс отладки, в ходе которого система сканирует проект, обнаруживает ошибки, исправляет их с помощью LLM и повторяет цикл, пока все проблемы не будут устранены.

После запуска вы увидите результат каждой итерации цикла: какие ошибки найдены, какой ответ дала LLM (включая обновлённые блоки кода) и какие изменения внесены в файлы. По завершении цикла код должен быть исправлен.

Итерация 1: Отправка запроса в LLM для исправлений...
Итерация 1: Код обновлён. Переход к следующей итерации...

Итерация 2: Отправка запроса в LLM для исправлений...
Итерация 2: Код обновлён. Переход к следующей итерации...

Ошибок в коде не найдено, отладка завершена.

На всё про всё потребовалось две итерации. А теперь взглянем на файлы после исправлений и убедимся, что ошибки действительно устранены.

# calculator.py
def add(a, b):
    return a + b
def subtract(a, b):
    return a - b
def multiply(a,b):
    return a * b
def divide(a,b):
   if b == 0:
       return "Деление на ноль невозможно"
   return a/b

# main.py
import calculator
def main():
    x = 10
    y = 5
    z = 0

    result_add = calculator.add(x, y)
    print(f"Сложение: {result_add}")

    result_sub = calculator.subtract(x, y)
    print(f"Вычитание: {result_sub}")

    result_mul = calculator.multiply(x,y)
    print(f"Умножение: {result_mul}")

    result_div = calculator.divide(x,z)
    print(f"Деление: {result_div}")

if __name__ == "__main__":
    main()

# utils.py
def greet(name):
    message = "Привет, " + name
    return message

Как видно, проблемы вроде TypeError из файла main.py, ZeroDivisionError из того же файла и даже IndentationError из utils.py благополучно ушли в прошлое и наш бесконечный цикл справился с этим нелёгким сценарием. Шах и мат, баги.

Поиск в интернете

Иногда одной лишь LLM может не хватить, чтобы справиться с ошибкой. Здесь на помощь приходит интернет — незаменимый инструмент в поиске решений. Почему бы не встроить в цикл возможность обращения к поисковикам, как это делаем мы, когда гуглим ошибки?

Первый шаг — преобразовать юзер‑промт в Google‑запрос. Для этого можно использовать ту же функцию LLM, передав ей тщательно составленный системный промт, направленный на превращение описанной ошибки в однострочный запрос для поисковика. Создадим системный промт для LLM, который будет заточен на это преобразование, и посмотрим, как он преобразует детализированную ошибку в запрос для Google.

QUERY_SYSTEM_PROMPT = """
    Вы высококвалифицированный ассистент для разработчиков программного обеспечения, специализирующийся на поиске и устранении ошибок в коде. Ваша задача — составить **однострочный поисковый запрос в Google** на основе переданного фрагмента кода и связанного с ним сообщения об ошибке. Запрос должен соответствовать следующим правилам:

    1. Включать название языка программирования или фреймворка (если известно).
    2. Содержать ключевые детали из сообщения об ошибке.
    3. Упоминать любые релевантные функции, методы или библиотеки из кода.
    4. Быть лаконичным, точным и оформленным в виде **одной строки**.

    **Формат входных данных:**
    1. **Фрагмент кода:** (например, Python-код, вызывающий исключение)
    2. **Сообщение об ошибке:** (например, `TypeError: неподдерживаемые операнды для +: 'int' и 'str'`)

    **Формат выходных данных:**
    Строка в точности в следующем формате:
    `search_query: "<ваш поисковый запрос в Google>"`
    """

Пример запроса, созданного моделью LLM, размером 3B, для одной из наших ошибок:

 ### НАША ОШИБКА
TypeError: неподдерживаемые операнды для +: 'int' и 'str'

# Вызов функции HF_llm с системным и пользовательским промтами
llm_response = HF_llm(
    model_name="meta-llama/Meta-Llama-3.1-3B-Instruct",  # Пример модели
    system_prompt=QUERY_SYSTEM_PROMPT,
    user_prompt=USER_PROMPT
)

print(llm_response)

### ВЫВОД ###
search_query: python "TypeError" convert int to string addition

Выглядит корректно: python "TypeError" convert int to string addition. Затем мы передадим это в поисковую систему, ведь велика вероятность, что интернет выдаст решение, которого нет в LLM‑модели.

Для получения результатов поиска Google мы будем использовать библиотеку scrapling. С её помощью создадим функцию для парсинга результатов поиска:

async def internet_search(search_query):
    """
    Асинхронный поиск в интернете с использованием Google, возвращает первые найденные ссылки.

    Args:
         search_query (str): Поисковый запрос для Google.

    Returns:
         list: Список первых найденных URL.
    """
    search_query = f'"{search_query}"'
    search_url = f"https://www.google.com/search?q={search_query}"

    fetcher = StealthyFetcher()

    try:
        page = await fetcher.async_fetch(search_url)
        all_urls = page.css("#search a::attr(href)")

        # Фильтруем список, оставляя только ссылки, начинающиеся с https
        all_urls = [url for url in all_urls if url.startswith("https")]

        return all_urls
    except Exception as e:
        print(f"Произошла ошибка: {e}")

Эта функция асинхронная, ведь интернет не всегда торопится с ответами. Она извлекает URL‑адреса из верхних результатов Google и возвращает их в виде списка. Далее мы сможем получить содержимое этих URL‑адресов с помощью нескольких дополнительных функций scrapling.

async def fetch_internet_content(search_query, num_urls = 5):
    """
    Асинхронно выполняет поиск в интернете и собирает содержимое веб-страниц по заданному запросу.

    Проверяет возможность выполнения интернет-поиска, получает URL-адреса и извлекает текстовое содержимое с нескольких веб-страниц, обрабатывая возможные ошибки.

    Args:
        search_query (str): Поисковый запрос для поиска веб-страниц.
        num_urls (int): Максимальное количество URL-адресов для загрузки (default: 5)

    Returns:
        str: Объединенный текст, полученный с веб-страниц,
              или пустую строку, если поиск отключен/результаты не найдены.
    """
    # Проверяем, включен ли интернет-поиск
    if not self.enable_internet_search:
        return ""

    # Выполняем интернет-поиск для получения URL-адресов
    urls = await internet_search(search_query)
    
    # Возвращаем пустую строку, если не найдено ни одного URL
    if not urls:
        return ""

    # Инициализируем парсер без автоматического сопоставления
    fetcher = Fetcher(auto_match=False)
    
    # Переменная для хранения объединенного содержимого веб-страниц
    combined_content = ""

    # Перебираем первые num_urls URL-адресов
    for url in urls[:num_urls]:
        try:
            # Загружаем страницу с заголовками, маскирующими бота
            page = fetcher.get(url, stealthy_headers=True)
            
            # Извлекаем текстовое содержимое, игнорируя теги <script> и <style>
            content = page.get_all_text(ignore_tags=("script", "style"))
            
            # Добавляем текст с заголовком URL-источника для отслеживания
            combined_content += f"### СОДЕРЖИМОЕ С: {url}\n{content}\n\n"
        
        except Exception as e:
            # Логируем ошибки при загрузке URL, не прерывая выполнение
            logging.error(f"Ошибка при загрузке {url}: {e}")

    return combined_content

Так как ссылок может быть много, разумно ограничить их количество, взяв несколько первых результатов. После эти данные нужно будет добавить в юзер‑промт.

Цикл отладки с использованием интернет-поиска

Давайте наконец‑то усовершенствуем наш бесконечный цикл отладки, добавив в него суперсилу — интернет‑поиск. Если одна и та же ошибка повторяется пять или более раз подряд, система будет искать решения в сети.

def infinite_debug_loop_with_internet(codebase_dir, 
                                      model_name,
                                      system_prompt,
                                      max_attempts=10):
    """
    Запускает расширенный цикл отладки, который использует интернет-поиск 
    для устранения повторяющихся ошибок.

    Args:
    codebase_dir (str): Каталог с Python-кодом, который нужно отладить.
    model_name (str): Название модели Hugging Face, используемой для исправления ошибок.
    system_prompt (str): Системные инструкции для языковой модели (LLM).
    max_attempts (int): Максимальное число попыток перед обращением к интернету.    

    Returns:
    None: Функция напрямую обновляет кодовую базу.
    """
    iteration = 1
    error_tracking = {}  # Отслеживание ошибок на протяжении итераций

    while True:
        # Шаг 1: Получаем структуру файлов и содержимое кода
        directory_tree = get_dir_tree(codebase_dir)
        code_content = read_code_files(codebase_dir)

        # Шаг 2: Запускаем код и фиксируем ошибки
        error_output = run_code_and_capture_errors(codebase_dir)

        # Если ошибок нет, завершаем цикл
        if "Error occurred" not in error_output and "Traceback" not in error_output:
            print("Ошибок в коде не найдено, отладка завершена.")
            break

        # Track error persistence
        if error_output in error_tracking:
            error_tracking[error_output] += 1
        else:
            error_tracking[error_output] = 1

        # Формируем пользовательский запрос, включая возможные результаты интернет-поиска
        user_prompt = Структура файлов проекта:
                          {directory_tree}

                          Содержимое файлов:
                          {code_content}

                          Обнаруженные ошибки:
                          {error_output}
                       '''

        # Если ошибка повторяется более max_attempts раз, обращаемся к интернет-поиску
        if error_tracking[error_output] > max_attempts:
            # Генерируем поисковый запрос для ошибки
            search_query_prompt = f" Фрагмент кода:\n{code_content}\n\nСообщение об ошибке:\n{error_output}"
            search_query_response = HF_llm(
                model_name=model_name, 
                system_prompt=QUERY_SYSTEM_PROMPT, 
                user_prompt=search_query_prompt
            )
            
            # Извлекаем поисковый запрос
            search_query = search_query_response.split(": ")[-1].strip('"')
            
            # Получаем содержимое с найденных веб-страниц
            internet_content = await fetch_internet_content(search_query)
            
            # Добавляем найденную информацию к юзер-промту
            user_prompt += f"\n\nРезультаты интернет-поиска:\n{internet_content}"

        # Step 4: Call the LLM to generate fixes
        print(f"Итерация {iteration}: Отправка запроса в LLM для исправлений.")
        llm_response = HF_llm(
            model_name=model_name, 
            system_prompt=system_prompt, 
            user_prompt=user_prompt
        )

        # Шаг 5: Обновление кода на основе ответа LLM
        update_code_from_llm_response(llm_response)

        # Сбрасываем счетчик для ошибки, если LLM предложила исправление
        error_tracking[error_output] = 0

        # Шаг 6: Продолжаем отладку
        iteration += 1
        print(f"Итерация {iteration}: Код обновлён. Переход к следующей итерации...\n")
        time.sleep(2)  # Опциональная пауза между итерациями

Итак, функция infinite_debug_loop_with_internet_search() пытается по кругу исправить ошибки с помощью языковой модели LLM, но, если «локальные» исправления не помогают и количество попыток для одной и той же ошибки превысило max_attempts, она начинает искать решения в интернете. Для этого применяется динамическое генерирование запросов.

Тестирование на GitHub

Мой репозиторий на GitHub предлагает более детализированное решение, которое поддерживает различные типы LLM, обрабатывает логи и предоставляет другие полезные функции. Здесь мы рассмотрим общий процесс. Так, протестируем репозиторий на более сложной кодовой базе и посмотрим, как он справляется.

python -m src.cli.main \
 --llm_type huggingface \
 --huggingface_model "meta-llama/Llama-3.2-3B-Instruct" \
 --code_dir "ai-debugger" \
 --huggingface_device "cuda" \
 --max_attempts 10 \
 --enable_internet_search True \
 --num_search_urls 5 \
 --internet_search_threshold 5

Этот скрипт, доступный в репозитории, предоставляет гибкие настройки: к примеру, можно включать или отключать интернет‑поиск или задавать параметры для моделей. Я использовал его на своей кодовой базе, содержащей сложные скрипты для обучения трансформеров, и в коде действительно обнаружились ошибки. Запустим процесс и посмотрим, как начинается отладка:

f8f1ba74d572d7aff2b1a20677308543.gif
## Вывод в консоли

2025-01-23 19:46:54,070 - INFO - HuggingFace-модель инициализирована
2025-01-23 19:46:54,070 - INFO - Файлы не переданы. Загружаем все файлы

2025-01-23 19:46:54,076 - INFO - Попытка 1...

2025-01-23 19:46:59,339 - INFO - Загружается модель LLaMA-3.1-3B-instruct
2025-01-23 19:46:59,358 - INFO - ИИ внес следующие изменения:

Файл: code2.py
  + from caleb import transform
  + return x + z
  ...
  + except Exception as e:
  + print("Произошла непредвиденная ошибка: ", str(e))

File: code3.py
  - from llmc import transformer_module
  + from code2 import transformer_module_
  + try:
  - print(parms.round())
  ...
  + print(llm.cab("Обучены следующие параметры:", parms)
  + except Exception as e:
  + print("Произошла непредвиденная ошибка: ", str(e))

...

2025-01-23 19:46:59,581 - INFO - Все ошибки исправлены.

После долгого ожидания, почти 34 итераций, система смогла устранить множество ошибок в моём коде, одновременно записывая детальные логи об удалённых или добавленных фрагментах. Эти логи сохраняются в директории проекта, который вы отлаживаете.


Дебаггинг с помощью LLM, который ещё и гуглит ошибки, скорее экспериментальный, так как его сложно применять в реальных проектах, особенно в крупных. Но более важная проблема здесь — безопасность: в интернете полно сомнительных советов, и почти ничто не мешает модели «починить» ваш код так, что он станет напичканным уязвимостями. На деле куда эффективнее использовать среды разработки с интегрированными LLM, чем пытаться автоматизировать гугление ошибок таким неординарным способом.

Что скажете по поводу этой реализации? Добавили бы вы в неё что‑то ещё?

* Продукт Llama принадлежит организации Meta, которая признана в России террористической и запрещена.

Источник

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  • 30.07.26 00:04 Ahmed

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

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  • 23.08.26 20:02 leslieyee

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  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 05.10.26 21:26 [email protected]

    I Invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 17:44 Darrell1717

    I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com

  • 11.10.26 03:04 Patricia014

    Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 13.10.26 14:14 lisalisa22

    Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices

  • 13.10.26 14:14 lisalisa22

    Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices

  • 17.10.26 12:58 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 17.10.26 12:58 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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  • 25.10.26 14:35 lydiasmitth456

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.10.26 14:35 lydiasmitth456

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 27.10.26 01:58 KATHLEEN

    Cyberspacetechexpert focuses on recovering stolen cryptocurrency, particularly USDT and bitcoin. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $74,000 was taken from me in January. Thanks to the best female hacker Gracias, I recovered the entire amount in just 14 hours. Her prompt response and effective methods relieved my financial burden. Her commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email Cyberspacetechexpert@gmail .com. or speak to her on What App at + 44 7476618364.

  • 27.10.26 02:22 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 27.10.26 02:22 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 27.10.26 02:25 KATHLEEN

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