Vector Database Engineering
for AI
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Vector Database Engineering for AI
$0
It's free
Most RAG tutorials work fine on 500 documents in a notebook. But once you index 10 million vectors, standard HNSW graphs consume hundreds of gigabytes of RAM, P99 latency spikes past two seconds, and semantic recall drops as noise fills the context window.
Vector Database Engineering for AI is a practical, code-first guide to designing, indexing, and scaling high-throughput retrieval systems for LLMs, RAG pipelines, and autonomous AI agents.
Written for software engineers, backend architects, and machine learning practitioners, this book skips the high-level fluff and breaks down the exact math, memory trade-offs, and data structures required to build production-grade vector search.
What You Will Learn:Vector Math & Embedding Mechanics: Dense vs. sparse representations, asymmetric models, dimensional economics, and L2 normalization to prevent embedding collapse.Nearest-Neighbor Search Dynamics: Cosine similarity, Euclidean distance, and Maximum Inner Product Search (MIPS). Ground-truth kNN vs. the recall-latency frontier in Approximate Nearest Neighbor (ANN) search.Indexing Algorithms Deep Dive: Internal mechanics of HNSW, IVF, Product Quantization (PQ), Scalar Quantization (SQ), and DiskANN. How to reduce RAM usage by up to 80% while preserving recall.Vector DB Internals & Storage: Segment management, sharding, compaction, WAL execution, and zero-downtime reindexing via alias swaps.Ingestion & Chunking Pipelines: Evaluating 7 chunking strategies (semantic, sliding window, parent-child, document-aware) alongside content hashing, idempotency, and dead-letter queues.Hybrid Search & Filtering: Iterative pre-filtering vs. post-filtering overhead, BM25 integration, and rank fusion algorithms (Reciprocal Rank Fusion and Weighted Fusion).Reranking & Late Interaction: Multi-vector retrieval, ColBERT token-level interaction, and Cross-Encoder reranking to maximize context precision under strict latency budgets.RAG & Agentic Architectures: State-machine retrieval using LangGraph, HyDE (Hypothetical Document Embeddings), GraphRAG, and adaptive query decomposition.Ecosystem Benchmarks: Production comparisons across FAISS, Qdrant, Milvus, Weaviate, Pinecone, Chroma, pgvector, Elasticsearch, and LanceDB.Billion-Scale System Design: Tiered memory/disk architectures, multi-tenant isolation, latency budgets, degradation ladders, and automated evaluation metrics.
Vector Database Engineering for AI is a practical, code-first guide to designing, indexing, and scaling high-throughput retrieval systems for LLMs, RAG pipelines, and autonomous AI agents.
Written for software engineers, backend architects, and machine learning practitioners, this book skips the high-level fluff and breaks down the exact math, memory trade-offs, and data structures required to build production-grade vector search.
What You Will Learn:Vector Math & Embedding Mechanics: Dense vs. sparse representations, asymmetric models, dimensional economics, and L2 normalization to prevent embedding collapse.Nearest-Neighbor Search Dynamics: Cosine similarity, Euclidean distance, and Maximum Inner Product Search (MIPS). Ground-truth kNN vs. the recall-latency frontier in Approximate Nearest Neighbor (ANN) search.Indexing Algorithms Deep Dive: Internal mechanics of HNSW, IVF, Product Quantization (PQ), Scalar Quantization (SQ), and DiskANN. How to reduce RAM usage by up to 80% while preserving recall.Vector DB Internals & Storage: Segment management, sharding, compaction, WAL execution, and zero-downtime reindexing via alias swaps.Ingestion & Chunking Pipelines: Evaluating 7 chunking strategies (semantic, sliding window, parent-child, document-aware) alongside content hashing, idempotency, and dead-letter queues.Hybrid Search & Filtering: Iterative pre-filtering vs. post-filtering overhead, BM25 integration, and rank fusion algorithms (Reciprocal Rank Fusion and Weighted Fusion).Reranking & Late Interaction: Multi-vector retrieval, ColBERT token-level interaction, and Cross-Encoder reranking to maximize context precision under strict latency budgets.RAG & Agentic Architectures: State-machine retrieval using LangGraph, HyDE (Hypothetical Document Embeddings), GraphRAG, and adaptive query decomposition.Ecosystem Benchmarks: Production comparisons across FAISS, Qdrant, Milvus, Weaviate, Pinecone, Chroma, pgvector, Elasticsearch, and LanceDB.Billion-Scale System Design: Tiered memory/disk architectures, multi-tenant isolation, latency budgets, degradation ladders, and automated evaluation metrics.
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