RAG

RAG (Advanced)

This level focuses on handling complex queries, scaling retrieval systems, and optimizing search efficiency for large-scale applications.

Key Concepts and Activities

1. Multi-Hop Retrieval

   • Description: Handling complex queries that require retrieving multiple documents iteratively.

   • Reason: Multi-hop retrieval enables better understanding and synthesis of information across documents.

   • Example Task: Implement a retrieval system that first retrieves a broad topic, then retrieves specific details.

     • Example Input: "How did Einstein's theories influence quantum mechanics?"

     • Expected Output:

       • First Retrieval: "Einstein's theory of relativity introduced the concept of spacetime..."

       • Second Retrieval: "Quantum mechanics and general relativity are connected through..."

       • Final Response: "Einstein's work laid the foundation for..."

2. Optimized Vector Search & Scalability

   • Description: Handling millions of documents efficiently using Approximate Nearest Neighbors (ANN) techniques like HNSW.

   • Reason: Large-scale retrieval requires optimized search techniques to ensure fast and accurate results.

   • Example Task: Implement an ANN-based retrieval system using FAISS with HNSW indexing for high-speed search over millions of documents.