AgenticRAG

Introduction

AgenticRAG is a comprehensive library for building flexible Retrieval-Augmented Generation (RAG) systems. It provides a modular architecture that allows developers to customize each component according to their specific requirements while maintaining compatibility across the system.

This library solves several key challenges in building RAG applications: - Managing diverse data types (text, tables, databases) through a unified interface - Simplifying data ingestion from various sources - Providing flexible retrieval mechanisms adapted to different data types - Supporting multiple downstream tasks using retrieved context - Enabling easy customization at any level of the stack

Architecture Overview

The AgenticRAG system follows a modular, layered architecture where each component has specific responsibilities:

1. Data Storage Layer: Stores maintain structured representations of various data types
2. Data Ingestion Layer: Loaders and Connectors handle importing data into stores
3. Data Retrieval Layer: Retrievers access relevant information based on queries
4. Task Execution Layer: Tasks perform operations using retrieved context
5. Orchestration Layer: RAGAgent coordinates all components to fulfill user requests

This design follows key principles: - Separation of concerns: Each component handles distinct responsibilities - Extensibility: Custom implementations can be created for any component - Loose coupling: Components interact through well-defined interfaces - Progressive disclosure: Simple use cases are easy, while complex customization is possible