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Подбор гиперпараметров RAG-системы с помощью Optuna

Как гласит народная мудрость: “Плох тот датасаентист, который не хочет свалить все на Optuna’у.

RAG-система это такой персональный карманный поисковик (а-ля Гугл или Яндекс), который может искать по локальным документам вашего кровавого энтерпрайза :)

Если чуть более по-научному: Retrieval Augmented Generation (RAG) — это технология, которая использует большие языковые модели (LLM) для генерации ответа на вопрос с учётом переданного ей контекста. Всесторонний рассказ о RAG-системах выходит за рамки этой статьи, поэтому вот тут вы сможете ознакомиться с соновами: https://habr.com/ru/articles/779526/ и https://habr.com/ru/companies/raft/articles/791034/

Под капотом у RAG-системы можно найти несколько различных компонентов. Причем набор и структура этих компонентов может серьезно различаться в зависимости от задачи и выбранного подхода. И каждый их этих компонентов обладает собственным набором параметров. И весь этот зоопарк надо как-то настраивать, потому что от этого сильно зависит качество поиска.

Можно делать это вручную. [Спойлер] Долго, утомительно и не эффективно [/Спойлер] :) А можно попробовать свалить эту задачу на Optuna’у (а че она бездельничает :) Чем мы и займемся в этой статье.

Для примера мы рассмотрим очень простой вариант RAG-системы, но достаточный для понимания концепции.

Задача

Но для начала формализуем задачу, т.к. от этого сильно зависит архитектура системы.

Итак, наша RAG-система должна выполнять следующие функции:

  • Поиск в различной технической документации: ТЗ, инструкции, регламенты и пр.

  • Поддержка только трех форматов документов: DOCX, PDF и TXT.

  • В одном документе могут быть ответы на многие вопросы. Но ответ на любой вопрос содержится только в одном документе, в одной его части (например, абзаце).
    Т.е. не может быть ситуации, что ответ на один и тот же вопрос можно найти в разных файлах.

  • Язык документации, преимущественно, - русский. Исключения - имена собственные на английском.

  • Работа в закрытом контуре (т.е. никаких OpenAI и прочих товарищей).

  • Работа на одной GPU A100 40Мб.

Архитектура

Архитектура нашей RAG-системы будет состоять из трех основных компонентов и двух процессов.

Начнем с процессов:

1. Парсинг документов

2df22c32063a7c39ab4bebf35a0daa16.png

Этапы:

  • Загружаем в “систему” документ(ы).

  • Документ разбивается на чанки (небольшие куски текста) по определенной логике.

  • Каждый чанк посредством bi-encoder’а конвертируется в вектор (он же эмбеддинг).

  • Вектор вместе с самим чанком (и другой сопутствующей информацией) сохраняется в векторной БД.

По фэн-шую тут еще должен быть процесс удаления документа, но для нашего эксперимента он сейчас не нужен.

2. Ответ на вопрос

479207c40d1591ec8b3a85c1dd672400.png

Этапы:

  • Пользователь формулирует вопрос.

  • Вопрос конвертируется в вектор посредством bi-encoder’а (причем того же самого, который используется для кодирования чанков).

  • По сформированному вектору ищем в БД похожие вектора. Возвращаем топ-N похожих векторов (а также связанные с ними чанки).

  • Формируем промт для LLM. Для этого склеиваем воедино: системный промт, вопрос пользователя и чанки.

  • Отправляем промт в LLM и получаем ответ.

Эти два процесс взаимосвязаны, поэтому и тестироваться они будут вместе.

Компоненты

В этой архитектуре можно выделить три основных компонента:

  1. Bi-encoder - производит кодирование строки в вектор.
    Для чего это нужно? Две строки закодированные в вектор можно сравнивать между собой посредством косинусного расстояния и сказать насколько они похожи. Таким способом, например, можно приблизительно подобрать ответы на вопрос.

  2. Векторная БД. Нужна для хранения векторов. А еще она может очень быстро находить (и возвращать) близкие вектора по косинусному (и некоторым другим) расстоянию.
    В вектора мы будем кодировать чанки документов и вопросы пользователей. И по косиносному расстоянию будем искать наиболее похожие на вопрос чанки. И уже эти чанки будем скармливать LLM.
    Зачем это нужно? Почему сразу не скормить все чанки в LLM? Причины две:

    • У LLM есть ограниченный контекст и все чанки в нее тупо не влезут.

    • Чем больше текста вы скормите LLM, тем дольше она будет генерировать ответ. Поэтому лучше подавать в нее какой-то минимально необходимый объем данных.

    Следовательно, нам нужно предварительно отобрать ограниченное количество чанков. Что мы и будем делать посредством косинусного расстояния.
    В качестве векторной БД будем использовать Qdrant.

  3. LLM - сердце нашей системы - занимается “осмысливанием” вопроса и генерирует ответа на него. Для теста будем использовать недавно разинутую Llama 3 (а точнее затюненую версию IlyaGusev/saiga_llama3_8b). Отвечает неплохо, работает ооочень быстро и влазит на одну карточку. В общем, то что нам нужно :)

На второй картинке вы можете увидеть (затенен) еще один компонент - Cross-encoder (еще его называют re-ranker). В этом решении он не используется, но он часто встречается в других решениях. Его основная функция - дополнительная фильтрация отобранных чанков. Cross-encoder типа умнее bi-энкодера, но работает заметно дольше bi-энкодера. А если у вас миллионы векторов - это может быть существенно снизить производительность системы. Поэтому поступают так: с помощью косинусного расстояния отбирают, например, топ-100 чанков. Их скармливают сross-encoder’у, который отбирает из них 3-5 чанков, которые уже поступают в LLM.

Или еще более хитрый вариант: просят LLM переформулировать вопрос пользователя 2 раза. По каждому из получившихся 3 вопросов ищут чанки в БД (например, по 30 чанков на вопрос). Затем cross-encoder отбирает из общего списка чанков 3-5 лучших.

Более подробно можете почитать тут: https://www.sbert.net/examples/applications/cross-encoder/README.html

З.Ы. К сожалению, для русского языка существует только один приличный cross-encoder: PitKoro/cross-encoder-ru-msmarco-passage

Тесты

Чтобы оценить качество работы RAG-системы необходимо подготовить тестовые вопросы и ответы к ним. И лучше чтобы в их составлении участвовали конечные пользователи RAG-системы (или заказчики), поскольку ваше представление о “прекрасном” может отличаться от необходимого. Вопросов нужно порядка 20-30 на 5-10 файлов.

В результате у вас должна получится примерно такая таблица:

#

Вопрос

Правильный ответ

Контекст

Файл

№ страницы

Где:

  • Вопрос - вопрос по содержимому файла.

  • Правильный ответ - что мы хотели бы видеть в качестве ответа на соответствующий вопрос.

  • Контекст - цельный кусок текста (например, абзац), из которого сформулирован правильный ответ.

  • Файл - название файла, в котором содержится ответ.

  • № страницы - номер страницы, на которой находится контекст.

Вопросы желательно подбирать так, чтобы протестировать различные варианты ответов:

  • Простые факты из документов.

  • Вопросы на суммаризацию.

  • Описания процессов.

  • Перечисления фактов.

  • Вопросы с условием.

  • Числа, даты, имена собственные.

  • И пр.

Теория это хорошо, но нам сейчас нужно на чем-то экспериментировать :)

Ru RAG Test Dataset

Специально для этой статьи я собрал датасет для тестирования RAG-системы: https://github.com/slivka83/ru_rag_test_dataset

Датасет основан на датасете RuBQ. В этом датасете есть все нужные нам столбцы (кроме страниц - в вебе и txt их нет :). Всего 923 вопроса.

f540d6ea7cf77f50f592727f85edba63.png

Из RuBQ я отобрал только те вопросы, ответ на который есть только в одном месте одной статьи. Но там есть и вопросы, ответы на которые встречаются в нескольких статьях/абзацах. Если они вам нужны можете скачать их самостоятельно - код я приложил.

З.Ы. Если что, датасет не идеальный, но может служить отправной точкой для вашего собственного теста:

  • Ответы на вопросы очень простые (если не сказать примитивные :)

  • Возможно википедия (особенно английская) не очень подходят для оценки RAG-системы, поскольку LLM зачастую обучаются и на ее текстах. И иногда может быть не понятно откуда модель взяла ответ - из чанков или из своих собственных знаний.

  • Встречаются ошибки в правильных ответах.

Оценка

Учитывая задачу, мы хотим в результате тестирования получить ответ на три вопроса:

  1. Найден ли правильный файл?

  2. Найден ли правильный контекст?

  3. Оценить ответ LLM.

Метрик для оценки ответов LLM довольно много: BERTScore, BLEURT, METEOR и пр. И все они довольно мудреные. Но если посмотреть на ответы в наших тестовых вопросах - они очень просты. Буквально слово или два. А LLM бывают довольно “разговорчивы”. Соответственно, чтобы оценить ответ LLM нам достаточно (для нашего случая) просто определить, содержится ли правильное слово в ответе LLM. Для этого идеально подходит метрика Rouge-1, которая просто сравнивает униграммы.

Есть даже вариант оценивать ответы LLM также с помощью LLM: подают в LLM вопрос и ответ и просят оценить (от 1 до 10) насколько ответ корректный.

Правильность контекста мы будем оценивать по пересечению. Т.е. будем искать, какой наибольший кусок контекста содержится в отобранных чанках.

Файл мы будем оценивать просто по факту его нахождения: вернула ли нам БД нужный файл (к каждому чанку у нас будет привязано название файла из которого он взят).

Код

Далее рассмотрим отдельные функции, из которых будет состоять наш код.

Весь код вы можете найти здесь: https://github.com/slivka83/rag_optuna_optimization

Чанки

from langchain_community.document_loaders import TextLoader, Docx2txtLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

def file_to_chunks(file_name, sep, chunk_size, chunk_overlap):
    file_ext = file_name.split('.')[-1]
    file_path = f'{TEST_FOLDER_PATH}/{file_name}'
    
    # Загружаем содержимое файла 
    if file_ext == 'txt':
        loader = TextLoader(file_path, encoding='utf-8')
    elif file_ext == 'docx':
        loader = Docx2txtLoader(file_path)
    elif file_ext == 'pdf':
        loader = PyPDFLoader(file_path)
    else:
        return
    file = loader.load()
    content = file[0].page_content
    
    # Разбиваем текст на чанки
    text_splitter = RecursiveCharacterTextSplitter(
        separators = sep,
        chunk_size = chunk_size,
        chunk_overlap = chunk_overlap,
        length_function = len,
        is_separator_regex = False,
        add_start_index = False
    )
    chunks = text_splitter.split_text(content)
    
    return chunks

Функция file_to_chunks, принимает название файла, загружает его и разбивает на чанки с помощью библиотеки langchain. И эти чанки возвращает.

И здесь у нас появляются первые гиперпараметры:

  • sep - разделитель по которому мы будем шинковать файл.

  • chunk_size - размер чанков (в символах).

  • chunk_overlap - с каким перехлестом будут делаться чанки.

Bi-encoder

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling, Transformer

# Подгружаем bi-encoder 
def get_bi_encoder(bi_encoder_name):
    raw_model = Transformer(model_name_or_path=f'{bi_encoder_name}')

    # Вытаскиваем размер векторов
    bi_encoder_dim = raw_model.get_word_embedding_dimension()
    
    pooling_model = Pooling(
        bi_encoder_dim,
        pooling_mode_cls_token = False,
        pooling_mode_mean_tokens = True
    )
    bi_encoder = SentenceTransformer(
        modules = [raw_model, pooling_model],
        device = 'cuda' # помещаем его на GPU
    )
    
    return bi_encoder, bi_encoder_dim

# Формируем из строки вектор
def str_to_vec(bi_encoder, text):
    embeddings = bi_encoder.encode(
        text,
        convert_to_tensor = True,
        show_progress_bar = False
    )
    return embeddings

Здесь у нас две функции:

  • get_bi_encoder - загружает и возвращает bi-encoder по его имени.

  • str_to_vec - конвертирует строку в вектор с помощью bi-encoder’а.

Тут стоит поподробнее остановиться на двух важных свойствах bi-encoder’а:

  • Сколько текста он может скушать за раз - остальное будет отброшено.
    Если у вас длинные чанки и/или длинные вопросы, то вам, возможно, стоит подобрать bi-encoder с бОльшей длиной контекста.

  • Какого размера вектора он возвращает. При прочих равных, чем больше длина вектора, тем больше информации в нем можно закодировать.

Оба этих параметра “зашиты” в bi-энкодер и оба важны для RAG-системы. Поэтому у нас сам bi-энкодер будет гиперпараметром. Т.е. мы будем пробовать разные bi-энкодеры и смотреть какой из них лучше себя покажет.

Есть даже лидерборд bi-энкодеров, в котором вы можете подобрать нужный вам: https://github.com/avidale/encodechka

Qdrant

import uuid

from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams, PointStruct


# Создаем подключение к векторной БД
qdrant_client = QdrantClient('my_qdrant_server.ru', port=6333)   

# Помещаем чанки и доп. информаицю в векторую БД
def save_chunks(bi_encoder, chunks, file_name):
    # Конвертируем чанки в векитора
    chunk_embeddings = str_to_vec(bi_encoder, chunks)

    # Содаем объект(ы) для БД
    points = []
    for i in range(len(chunk_embeddings)):
        point = PointStruct(
            id=str(uuid.uuid4()), # генерируем GUID
            vector = chunk_embeddings[i], 
            payload={'file': file_name, 'chunk': chunks[i]}
        )
        points.append(point)
    
    # Сохраняем вектора в БД
    operation_info = qdrant_client.upsert(
        collection_name = COLL_NAME,
        wait = True,
        points = points
    )
    
    return operation_info

def files_to_vecdb(files, bi_encoder, vec_size, sep, chunk_size, chunk_overlap):    
    # Удаляем и заново создаем коллекцию
    qdrant_client.delete_collection(collection_name=COLL_NAME)
    qdrant_client.create_collection(
        collection_name = COLL_NAME,
        vectors_config = VectorParams(size=vec_size, distance=Distance.COSINE),
    )
    
    # Каждый файл по одному...
    for file_name in files:
        # делим на чанки ...
        chunks = file_to_chunks(file_name, sep, chunk_size, chunk_overlap)
        # помещаем чанки в векторную БД
        operation_status = save_chunks(bi_encoder, chunks, file_name)

Здесь мы:

  • Создаем подключение к векторной БД Qdrant.
    З.Ы. Процесс установки выходит за рамки статьи, но вы можете ознакомится с ним в официальной документации: https://qdrant.tech/documentation/guides/installation/

  • Объявляем две функции:

    • save_chunks - конвертирует переданные чанки в эмбединги и помещает их в Qdrant.

    • files_to_vecdb - делает две вещи:

      • Удаляет и вновь создает коллекцию в БД Qdrant, в которой мы будем складировать чанки.
        З.Ы. Коллекция это аналог таблицы в реляционной БД.

      • Последовательно перебирает переданные файлы. Каждый из которых делит на чанки и кладет в БД Qdrant.

Обратите внимание:

  • Коллекцию мы создаем такого же размера, какого размера вектора возвращает bi-encoder.

  • Вместе с вектором мы будем хранить сам чанк, из которого он сформирован и название файла, из которого он взят.

Да, мы будет при каждом прогоне теста заново разбивать все файлы на чанки и создавать из них коллекцию. Но не стоит об этом переживать. На фоне скорости генерации ответа LLM это происходит почти мгновенно :)

Поиск векторов

def vec_search(bi_encoder, query, n_top_cos):
    # Кодируем запрос в вектор
    query_emb = str_to_vec(bi_encoder, query)

    # Поиск в БД
    search_result = qdrant_client.search(
        collection_name = COLL_NAME,
        query_vector = query_emb,
        limit = n_top_cos,
        with_vectors = False
    )
    
    top_chunks = [x.payload['chunk'] for x in search_result]
    top_files = list(set([x.payload['file'] for x in search_result]))
    
    return top_chunks, top_files

Функция vec_search сначала кодирует вопрос в вектор, а затем ищет по косиносному расстоянию наиболее похожие вектора в БД Qdrant. Возвращает топ-N векторов: а точнее содержимое чанков (и названия файлов) привязанных к отобранным векторам.

LLM

Сначала загрузим саму LLM:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = 'IlyaGusev/saiga_llama3_8b'

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype = torch.bfloat16,
    device_map = "auto"
)

Как мы помним, в качестве подопытной LLM’и у нас будет использоваться новомодная Llama3.

Далее определим функцию, которая будет скармливать LLM промт и обрабатывать ответ:

def get_llm_answer(query, chunks_join, max_new_tokens, temperature, top_p, top_k):
    user_prompt = '''Используй только следующий контекст, чтобы очень кратко ответить на вопрос в конце.
    Не пытайся выдумывать ответ.
    Контекст:
    ===========
    {chunks_join}
    ===========
    Вопрос:
    ===========
    {query}'''.format(chunks_join=chunks_join, query=query)
    
    SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
    RESPONSE_TEMPLATE = "<|im_start|>assistant\n"
    
    prompt = f'''<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n{RESPONSE_TEMPLATE}'''
    
    def generate(model, tokenizer, prompt):
        data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
        data = {k: v.to(model.device) for k, v in data.items()}
        output_ids = model.generate(
            **data,
            bos_token_id=128000,
            eos_token_id=128001,
            pad_token_id=128001,
            do_sample=True,
            max_new_tokens=max_new_tokens,
            no_repeat_ngram_size=15,
            repetition_penalty=1.1,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p 
        )[0]
        output_ids = output_ids[len(data["input_ids"][0]) :]
        output = tokenizer.decode(output_ids, skip_special_tokens=True)
        return output.strip()
    
    response = generate(model, tokenizer, prompt)
    
    return response

Прибавилось гиперпараметров и все они относятся к LLM:

  • max_new_tokens - максимальное количество токенов, которое будет сгенерировано LLM (не считая токены в промте).

  • temperature - определяет насколько “творческим” будет ответ LLM. Чем выше значение, тем выше “творчество”.

  • top_p - также отвечает за степень детерминированности модели. Чем больше значение, тем более разнообразными будут ответы. Меньшие значения будут давать более точные и фактические ответы.
    З.Ы. Рекомендуется изменить либо температуру, либо top_p, но не оба сразу.

  • top_k - ограничивает количество вариантов, которые модель рассматривает при генерации следующего токена.

З.Ы. Здесь напрашивается в качестве гиперпараметра системный промт. Точнее различные варианты его формулировки. Но пока оставим его статичным.

Т.к. ответы у нас состоят из одного-двух слов - просим ламу отвечать очень кратко.

Оценка

Оценкой ответа LLM (от 0 до 100) будут заниматься две функции. Одна будет лемматизировать текст, а вторая непосредственно оценивать ответ посредством метрики Rouge-1.

import re
import json
from rouge import Rouge
from pymorphy2 import MorphAnalyzer

f = open('stopwords-ru.json', encoding='utf-8')
stop_words = json.load(f)
#print(stop_words)

morph = MorphAnalyzer()
patterns = "[«»°!#$%&'()*+,./:;<=>?@[\]^_`{|}~—\"\-]+"

def lemmatize(string):
    clear = re.sub(patterns, ' ', string)
    tokens = []
    for token in clear.split():
        if token:
            token = token.strip()
            token = morph.normal_forms(token)[0]
            if token not in stop_words:
                tokens.append(token)
    tokens = ' '.join(tokens)
    return tokens

def get_llm_score(answer, answer_true):
    answer = lemmatize(answer)
    answer_true = lemmatize(answer_true)
    if len(answer) == 0:
        answer = '-'
        
    rouge = Rouge()
    scores = rouge.get_scores(answer, answer_true)[0]
    rouge_1 = round(scores['rouge-1']['r']*100, 2)
    
    return rouge_1

Оценка контекста (от 0 до 100) будет также производится метрикой Rouge. Точнее одной из ее версий (Rouge-L), которая измеряет максимальную общую длину между двумя строками:

def get_context_score(chunks_join, context):
    rouge = Rouge()
    scores = rouge.get_scores(chunks_join, context)[0]
    score = round(scores['rouge-l']['r'] * 100)
    return score

Найден ли нужный файл (0 или 1) мы будет определять просто по отобранным чанкам: к каждому из них привязано название файла (из которого он взят). Код сравнения вы найдете в следующем разделе.

Запуск одного теста

Все части пазла готовы и теперь нужно собрать их вместе и один раз прогнать через них все строки тестовых вопросов:

def run_one_test(df, encoder_name, sep, chunk_size, chunk_overlap, n_top_cos, max_new_tokens, temperature, top_p, top_k):
    try:
        bi_encoder, vec_size = get_bi_encoder(encoder_name)

        files = df['Файл'].unique()
        files_to_vecdb(files, bi_encoder, vec_size, sep, chunk_size, chunk_overlap)

        result = []
        for i, row in df.iterrows():
            query = row['Вопрос']
            answer_true = row['Правильный ответ']
            file_name = row['Файл']
            context = row['Контекст']

            top_chunks, top_files = vec_search(bi_encoder, query, n_top_cos)
            row['top_files'] = top_files
            row['top_chunks'] = top_chunks
            top_chunks_join = '\n'.join(top_chunks) # объединяем чанки

            answer = get_llm_answer(query, top_chunks_join, max_new_tokens, temperature, top_p, top_k)
            row['Ответ'] = answer

            row['file_score'] = int(file_name in top_files)
            row['context_score'] = get_context_score(top_chunks_join, context)
            row['llm_score'] = get_llm_score(answer, answer_true)

            result.append(row)

        result = pd.DataFrame(result)
        result = result.sort_values(by=['llm_score','context_score','file_score'], ascending=False)
        result = result.reset_index(drop=True)

        score = result['llm_score'].mean()

        return result, score
    
    except:
        return None, 0

Здесь мы:

  • Подгружаем bi-encoder

  • Все файлы из тестового датасета разбиваем на чанки, конвертируем в вектора и помещаем в Qdrant.

  • Для каждого вопроса из теста:

    • Конвертируем вопрос в вектор.

    • Ищем похожие (на вопрос) чанки в БД Qdrant.

    • Объединяем чанки и формируем из них промт для LLM.
      З.Ы. Способ объединения чанков также может быть гиперпараметром.

    • Скармливаем промт LLM и получаем ответ.

    • Оцениваем ответ LLM, найденный контекст и файл.

    • Сохраняем оценки и служебную информацию в датасете.

Возвращаем датасет и оценку. Общая оценка всего датасета это просто усредненная оценка ответа LLM. Для нашего случая этого пока достаточно.

С оценкой можно поиграться. Например можно усреднить оценку файла, контекста и LLM. Или можно усреднить только оценки LLM и контекста и умножить все это на оценку файла (если файл найден неправильно, то вся оценка занулится). В общем простор для фантазии большой.

Обратите внимание, что код внутри функции run_one_test мы обернули в try-except. Это нужно обязательно сделать на случай, если оптуна захочет передать в RAG-систему слишком жирные параметры. От которых модель просто упадет. В этом случаем мы не прекращаем обучение, а просто возвращаем скор 0. Оптуна быстро выучит границы дозволенного и суваться за их пределы почти не будет.

Запуск Optuna'ы

Вот мы и подобрались к вишенке на торте.

Сначала определим loss-функцию, которую будет максимизировать опутна:

import optuna

def objective(trial):
    global best_score, best_result
    
    encoder_name = trial.suggest_categorical('encoder_name', ['cointegrated/rubert-tiny2',
                                                              'kazzand/ru-longformer-large-4096',
                                                              'cointegrated/LaBSE-en-ru'])
    sep = trial.suggest_categorical('sep', ['.',' ','\n'])
    
    chunk_size = trial.suggest_int('chunk_size', 100, 2000)
    chunk_overlap = trial.suggest_int('chunk_overlap', 50, 600)
    n_top_cos = trial.suggest_int('n_top_cos', 1, 8)
    
    max_new_tokens = trial.suggest_int('max_new_tokens', 100, 1600)
    temperature = trial.suggest_float('temperature', 0.01, 0.99)
    top_p = trial.suggest_float('top_p', 0.01, 0.99)
    top_k = trial.suggest_int('top_k', 10, 150)
    
    result, score = run_one_test(
        TEST_DF,
        encoder_name,
        sep, chunk_size, chunk_overlap, n_top_cos,
        max_new_tokens, temperature, top_p, top_k
    )
    
    if score > best_score:
        best_score = score
        best_score_tag = ' <--'
        best_result = result
    else:
        best_score_tag = ''       

    print(f'{score:.2f}', best_score_tag)
    
    return score

optuna.logging.set_verbosity(optuna.logging.WARNING)

Здесь мы:

  • Определяем все наши гиперпараметры и задаем диапазон для их перебора.

  • Скармливаем функции run_one_test параметры текущего цикла и получаем в виде ответа датасет с результатами и оценку.

  • Сравниваем полученный скор с лучшим значением и если оно превосходит заменяем его и сохраняем таблицу с ответами (в глобальной переменной).

  • Возвращаем скор оптуне.

В качестве разделителя для чанков мы используем только один символ (точку, запятую или пробел). Но в функцию RecursiveCharacterTextSplitter можно передать сразу последовательность символов. Примерно так: ['/n', '.', ',', ' ']. Тогда, она сначала попробует первый знак, затем перейдет ко второму и т.д. Так можно получить чанки более равномерной длины. Ну а возможности комбинаций этих символов оставляют большой простор для фантазии :)

Ну и запустим наконец оптуну:

import pandas as pd

COLL_NAME = 'optuna_test_llama3_1'
TEST_FILE_PATH = 'ru_rag_test_dataset.pkl'
TEST_FOLDER_PATH = 'Тест_RuBQ'

TEST_DF = pd.read_pickle(TEST_FILE_PATH)[::15]
print('Кол-во строк:', TEST_DF.shape[0])


best_score = 0
best_result = None

study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=5000, timeout=60*60*24)

Здесь мы:

  • Определяем глобальные переменные: название коллекции в БД Qdrant в которой будет хранить чанки, пути до датасета с вопросами и путь в папке, в которой лежат файлы.

  • Подгружаем тестовые вопросы. При этом отбираем каждый 15 вопрос, чтобы уменьшить общее их количество и сократить время на один тест. В итоге у нас получается 62 вопроса (из 923), чего вполне достаточно для оценки.

  • Запускаем оптуну и ставим ей задачу максимизировать скор.

Ну и смотрим как увеличивается скор:

0.00 
42.60  <--
0.00 
29.01 
0.00 
46.47  <--
30.48 
40.38 
38.78 
32.56 
39.10 
38.94 
43.59 
42.87 
59.62  <--
64.42  <--
62.18 
65.70  <--
58.81 
65.38 
...

Как гласит еще одна народная мудрость, можно бесконечно смотреть на три вещи: как горит огонь, как течет вода и как падает лосс :)

З.Ы. Если хотите прогонять много примеров, то можете воспользоваться техникой Pruners, чтобы сократить время обучения: https://t.me/ds_private_sharing/76

Результат

Я гонял оптуну 24 часа и провел 482 теста. Лучший скор - 77.95%. Если сравнить его с медианным (69.72%) или средним скором (66.56%), то результат лучше примерно на 10%.

Глянем на распределение скоров:

a1c8f2cd16fc9d0810f312b4873cbcee.png

Нулевых скоров почти нет.

Посмотрим на лучшие гиперпараметры:

{'encoder_name': 'cointegrated/LaBSE-en-ru',
 'sep': '\n',
 'chunk_size': 1977,
 'chunk_overlap': 61,
 'n_top_cos': 8,
 'max_new_tokens': 302,
 'temperature': 0.6095279086616815,
 'top_p': 0.1871688966686985,
 'top_k': 129}

Тут мы явно уперлись в chunk_size и n_top_cos. Можно еще раз запустить подбор, увеличив эти параметры.

Посмотрим на лучшие ответы:

acaedf56fe56baabd04009c842f42ca9.png

Выглядит неплохо. Теперь посмотрим на худшие ответы:

c37d4ec134c8ab132b7071f8a0e9c9a4.png

Можно заметить, что на некоторые вопросы ответы даны правильно. Т.е. в самом “правильном ответе” содержится либо неправильный ответ, либо ответ который можно трактовать двояко. А это значит, что скор у лучшей “модели” еще выше.

Также не нашли три раза нужный файл. Можно еще добавить разнообразных bi-encider’ов для перебора.

Может показаться, что лучший результат случаен. Но давайте посмотрим на топ лучших скоров:

d9ec9f7b47cc4c9850aadbf7386a3fc1.png

Видно, что большинство лучших значений обладают примерно схожими гиперпараметрами. А значит результат вполне закономерен.

З.Ы. Дополнительно я вручную запустил тестирование с лучшими найденными гшиперпараметрами. Скор был примерно равен лучшему скору оптуны.

По фэн-шую еще можно выделить из общего теста отдельный кусок (холдаут) и на нем прогнать модель с лучшими параметры. Для финальной оценки.

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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.

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  • 17.07.24 02:26 thompsonrickey

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  • 06.09.24 01:35 Celinagarcia

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  • 06.09.24 01:44 Celinagarcia

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  • 23.09.24 18:56 matthewshimself

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  • 02.10.24 22:27 Emily Hunter

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  • 31.10.24 00:13 ytre89

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  • 12.11.24 00:50 TERESA

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  • 21.11.24 08:02 Emily Hunter

    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

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    DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT

  • 23.01.25 14:20 nellymargaret

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

  • 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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