Agentic AI: Data Ingestion and Retrieval

Welcome to the Agentic AI module. This project focuses on the foundational techniques required to build robust, production-grade AI agents capable of handling complex data workflows.

🚀 Core Focus

This module covers the end-to-end lifecycle of data in an Agentic AI system:

Ingestion: Advanced chunking, metadata enrichment, and multimodal data handling.
Retrieval: Hybrid search, reranking, query transformation, and graph-based retrieval.
Agentic RAG: Building loops and reasoning steps into the retrieval process.

📖 In-Depth Guide

For a comprehensive lesson from first principles, including implementation details and advanced RAG variants, please refer to the master document:

Agentic AI: Data Ingestion and Retrieval Techniques

Interactive Reinforced Learning

Parallelisation Deep Dives and Interactive Learning

Interactive Guide to Parallelisation - Ingestion and Retrieval: Interactive guide to parallelisation in agentic AI, focusing on ingestion and retrieval.
Deep Dive into Parallelisation in Agentic Engineering: Masterclass on fan-out/fan-in, map-reduce, and concurrent execution patterns for high-performance agentic systems.

Chunking Deep Dives and Interactive Learning

Chunking in Agentic AI — Interactive Learning: Interactive guide to chunking in agentic AI, focusing on strategies, tradeoffs, and enterprise patterns.
Deep Dive into Chunking in Agentic AI: Masterclass on chunking strategies, tradeoffs, and enterprise patterns in agentic AI.

🏛 Architecture Insights

Explore the high-level and detailed architectural patterns for scaling agentic systems:

Agentic AI Architecture Overview: Interactive roadmap of the agentic orchestration plane and system components.
Enterprise Multi-Cloud Agentic Architecture: Deep-dive into high-throughput, cross-cloud designs across AWS, Azure, and GCP.