"""

This is the initialization file for the RAG (Retrieval-Augmented Generation) application,
designed to provide context-aware responses by combining document embeddings with large
language model (LLM) capabilities. The application is modular, scalable, and maintains
a clear separation of concerns across its components.

Modules:
    api: Exposes endpoints for document upload and querying the system.
    config: Manages application settings and environment variables.
    core: Implements embedding generation, LLM integration, and workflow orchestration.
    models: Defines schemas for API request validation and response structuring.
    service: Provides document management and vector database interaction services.
    exception: Contains custom exceptions for handling application-specific errors.
    utils: Offers utility functions for common operations and data manipulation.
    logger: Implements centralized logging with customizable levels.
    constants: Stores application-wide constants for consistency and maintainability.

Features:
    - **Retrieval-Augmented Generation**: Combines document embeddings with LLMs to deliver accurate, context-aware answers.
    - **Modular Design**: Ensures scalability, maintainability, and ease of testing.
    - **Error Handling and Logging**: Enhances debugging and monitoring with structured logs and custom exceptions.
    - **Seamless Integration**: Connects document management, vector database, and LLM workflows efficiently.
    - **User-Friendly API**: Simplifies user interaction with the application's core functionalities.

This package serves as the backbone of the RAG application, ensuring a seamless pipeline
from document ingestion to intelligent query resolution.
"""
