DISTRIBUTED AI AND ML SYSTEMS / CAP04

Capstone Project: Enterprise Distributed RAG & Multi-Modal Vector Platform

Architect an enterprise-scale distributed retrieval-augmented generation (RAG) platform: hybrid dense-sparse search, Reciprocal Rank Fusion, graph knowledge integration, and multi-vector reranking.

1 topic600 min estimatedPublished

Retrieval-Augmented Generation (RAG) is the definitive enterprise architecture for grounding generative models on private, dynamic corporate corpora without model fine-tuning.

However, naive vector search implementations suffer severe production failures:

  • Lexical Blindness: Dense vector embeddings struggle with exact entity IDs, SKU numbers, code symbols, and acronyms.
  • Lost in the Middle: Large context windows dilute LLM attention, causing models to hallucinate or miss relevant nuggets buried deep inside retrieved chunks.
  • Scale and Latency Bottlenecks: Linear scans over millions of high-dimensional vectors (e.g., 1536-dim or 3072-dim) exceed memory and latency budgets.

This Capstone guides learners through architecting a production-scale Distributed RAG & Vector Platform featuring hybrid dense-sparse indexing, Reciprocal Rank Fusion (RRF), cross-encoder re-ranking, and contextual document chunking.

What you will learn

  • Construct a hybrid search pipeline combining dense vector embeddings (HNSW) and sparse lexical inverted indexes (BM25) fused via Reciprocal Rank Fusion (RRF)
  • Design distributed vector sharding and partition pruning using IVF-PQ (Inverted File Product Quantization) across multi-node clusters
  • Implement two-stage retrieval with cross-encoder rerankers and contextual compression to maximize generation faithfulness and context relevance
  • Formulate mathematical models for Recall@K, Mean Reciprocal Rank (MRR), and Vector Quantization reconstruction error
STAGE TOPICS

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Step 94Capstone Project: Enterprise Distributed RAG & Multi-Modal Vector PlatformArchitect an enterprise-scale distributed retrieval-augmented generation (RAG) platform: hybrid dense-sparse search, Reciprocal Rank Fusion, graph knowledge integration, and multi-vector reranking.60 min ↗