| """ | |
| 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. | |
| """ | |