#app.py
import gradio as gr
from context.db_loader import SQLiteContextLoader
from models.gpt4o import ModelClient
import constants
from helpers import load_json_file


class NeuroRavenApp:
    def __init__(self):
        self.system_prompt = None
        self.current_theme = None
        self.client = ModelClient()
        self.context_loader = SQLiteContextLoader()
        self.conversation_themes = self.get_conversation_themes()
        if constants.NO_CONTEXT not in self.conversation_themes:
            self.conversation_themes.append(constants.NO_CONTEXT)

    @staticmethod
    def get_conversation_themes():
        themes = load_json_file(constants.CONVERSATION_THEMES_JSON)
        return themes

    def handle_query(self, message, history, context_theme, should_save):
        # Сохранение сообщения в базу данных, если чекбокс активирован
        if should_save and isinstance(self.context_loader, SQLiteContextLoader):
            if context_theme == constants.NO_CONTEXT:
                return constants.ERROR_CANNOT_SAVE_MESSAGE
            self.context_loader.save_message(context_theme, message)
            self.current_theme = None
            return constants.MESSAGE_SAVED_TO_CONTEXT

        if context_theme != self.current_theme:
            context = self.context_loader.load_context(context_theme)
            if context:
                self.system_prompt = constants.ANSWER_USING_CONTEXT_PROMPT.format(context=context)
            else:
                self.system_prompt = constants.DEFAULT_SYSTEM_PROMPT
            self.current_theme = context_theme

        response = self.client.get_response(
            message=message,
            history=history,
            sys_prompt=self.system_prompt
        )
        return response

    def launch_app(self):
        gr.ChatInterface(
            fn=self.handle_query,
            chatbot=gr.Chatbot(height=500),
            textbox=gr.Textbox(
                placeholder=constants.INPUT_PLACEHOLDER,
                container=False,
                scale=7
            ),
            title="NeuroRaven",
            theme="soft",
            retry_btn=None,
            clear_btn=constants.CLEAR_BUTTON_LABEL,
            additional_inputs=[
                gr.Dropdown(
                    self.conversation_themes,
                    label=constants.SELECT_CONTEXT_THEME_LABEL,
                    value=constants.NO_CONTEXT
                ),
                gr.Checkbox(
                    label=constants.SAVE_MESSAGE_LABEL,
                    value=False
                )
            ],
            css="footer{display:none !important}"
        ).launch(server_name=constants.SERVER_NAME, server_port=constants.SERVER_PORT)


if __name__ == "__main__":
    app = NeuroRavenApp()
    app.launch_app()

#db_loader.py
import sqlite3
from context.base_loader import ContextLoader
import constants


class SQLiteContextLoader(ContextLoader):
    def __init__(self):
        self.db_path = constants.DATABASE_PATH
        self._initialize_database()

    def _initialize_database(self):
        """Создает таблицу в базе данных, если она не существует."""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS contexts (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                theme TEXT NOT NULL,
                date_added DATE DEFAULT (date('now')),
                text TEXT NOT NULL
            )
        ''')
        conn.commit()
        conn.close()

    def load_context(self, context_theme):
        if context_theme == constants.NO_CONTEXT:
            return ""

        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        query = "SELECT text FROM contexts WHERE theme = ?"
        cursor.execute(query, (context_theme,))
        rows = cursor.fetchall()
        conn.close()

        all_text = "\n".join(row[0] for row in rows)
        return all_text

    def save_message(self, context_theme, message):
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        query = "INSERT INTO contexts (theme, text) VALUES (?, ?)"
        cursor.execute(query, (context_theme, message))
        conn.commit()
        conn.close()


#base_loader.py
from abc import ABC, abstractmethod


class ContextLoader(ABC):
    @abstractmethod
    def load_context(self, context_theme):
        pass

#conversation_themes.json
[
    "DiKaskoInsuranceProcess",
    "DiKaskoInsuranceResumeProcess",
    "Общее",
    "Скрипты"
]

Я хочу добавить новую тему conversation_theme, которая будет использовать не обычную базу данных, а векторную. Суть в том что у меня есть очень большие документы и мне надо встраивать их в контекст
Подскажи пожалуйста как мне это реализовать