Ограничение длины текста в OpenAI: количество слов для генерации видео

import re

from src.types import LanguageType


class TextUtils:
    END_SENTENCE_CHARS = (".", "!", "?", ",", ";", "。", "\n")

    RATIO = {
        "en": 1.4,
        "ru": 1.6,
        "es": 1.6,
        "de": 1.6,
        "zh": 2.0,
        "ja": 2.0,
        "ko": 2.0,
    }

    @staticmethod
    def get_tokens_per_word(language: LanguageType) -> float:
        value = TextUtils.RATIO.get(language)

        if not value:
            raise ValueError(f"Unsupported language: {language}")

        return value

    @staticmethod
    def split_by_tokens(text: str, max_tokens: int, overlap_tokens: int = 0):
        """
        Разбивает текст на куски по max_tokens токенов с перекрытием overlap_tokens.
        Использует tiktoken (cl100k_base).
        """
        if max_tokens <= 0:
            raise ValueError("max_tokens must be > 0")
        if overlap_tokens < 0:
            raise ValueError("overlap_tokens must be >= 0")

        import tiktoken

        enc = tiktoken.get_encoding("cl100k_base")
        tokens = enc.encode(text)

        chunks = []
        start = 0
        n = len(tokens)

        while start < n:
            end = min(start + max_tokens, n)
            chunk_tokens = tokens[start:end]
            chunks.append(enc.decode(chunk_tokens))
            if end >= n:
                break
            start = end - overlap_tokens if overlap_tokens > 0 else end

        return chunks

    @staticmethod
    def group_text_into_sentences(
        text: str, max_words_length: int | None = 20
    ) -> list[list[dict]]:
        new_sent_split_pattern = "|".join(map(re.escape, TextUtils.END_SENTENCE_CHARS))
        raw = [w.strip() for w in re.split(new_sent_split_pattern, text) if w.strip()]

        replacements = {"#": "", "---": ""}

        raw = [{"word": TextUtils.replace(w, replacements)} for w in raw if w.strip()]

        return TextUtils.group_words_dict_into_sentences(raw, max_words_length)

    @staticmethod
    def replace(value: str, replacements: dict) -> str:
        for old, new in replacements.items():
            value = value.replace(old, new)

        return value

    @staticmethod
    def group_words_dict_into_sentences(
        words: list[dict],
        max_words_length: int | None = 20,
    ) -> list[list[dict]]:
        sentences = []
        current_sentence = []

        for _, word in enumerate(words):
            current_sentence.append(word)

            has_end_char = (
                word.get("word", "").strip().endswith(TextUtils.END_SENTENCE_CHARS)
            )
            current_sentence_words = "".join(
                [w.get("word", "") for w in current_sentence if w.get("word")]
            )
            is_length_exceeded = (
                max_words_length and len(current_sentence_words) >= max_words_length
            )

            should_split = has_end_char or is_length_exceeded

            # Create new sentence every max_words_per_line words or at punctuation
            if should_split:
                sentences.append(current_sentence)
                current_sentence = []

        # Add remaining words if any
        if current_sentence:
            sentences.append(current_sentence)

        return sentences

from src.text.text_utils import TextUtils
from src.types import LanguageType

class ModelApiConfig:
    @staticmethod
    def get_tokens_limit_for_words(
        words_amount: int, lang: LanguageType, temperature: float = 0.5
    ):
        tokens_to_generate = int(
            TextUtils.get_tokens_per_word(lang) * words_amount * 1.3
        )

        return {
            "max_completion_tokens": tokens_to_generate,
            # "max_tokens": tokens_to_generate,
            "temperature": temperature,
        }

# agent example with setting of those parameters
def get_agent(self, words_to_generate: int = 300, language: LanguageType = "en"):
    return AtomicAgent[
        VisualSubtitlesMakerInputSchema,
        VisualSubtitlesMakerOutputSchema,
    ](
        config=AgentConfig(
            client=instructor.from_openai(self.client, mode=instructor.Mode.JSON),
            model=Config.gpt_model(),
            model_api_parameters=ModelApiConfig.get_tokens_limit_for_words(
                words_to_generate, language
            ),
            system_prompt_generator=SystemPromptGenerator(
                background=[
                    """Ты — режиссёр-постановщик кинематографичного фильма.""",
                    """На основе истории из субтитров ты должен создать атмосферные и визуально разнообразные сцены.""",
                ],
                steps=[
                    """Для каждой сцены сделай одно визуальное описание:
                - Ключевое действие/объект сцены
                - Обстановка (место, атмосфера, время)
                - Эмоции/настроение
                """,
                ],
                output_instructions=[
                    """Пиши в стиле промта для генерации изображений (cinematic).""",
                    """Не добавляй слайды с призывами подписаться, лайкать или комментировать.""",
                ],
            ),
        )
    )