HNSW

← Previous Chapter: Part 2: Ingestion & Atomic Catalog Chunking | Series Hub | Next Chapter: Part 4: Active RAG & Strict Tool Calling → Prerequisite: Read Part 2: Data Ingestion & E-commerce Chunking: Bringing Product Catalogs to AI to understand the Atomic Chunking model and vector point schema. Answer-first: Hybrid search in Qdrant fuses dense semantic embeddings with sparse lexical tokens via Reciprocal Rank Fusion, boosting Top-10 catalog retrieval recall from 78.2% to 96.8%. Executing payload index pre-filtering directly within HNSW graph traversals enforces strict brand, category, and price boundaries in sub-2ms, while scalar quantization reduces cluster RAM consumption by 75% without sacrificing product discovery relevance. ...

Building a Custom Golang Vector Database Engine with HNSW Answer-first: Building a custom Go vector database engine with Hierarchical Navigable Small World (HNSW) graphs enables high-throughput vector similarity indexing, memory-mapped SIMD distance calculations, and fast ANN retrieval. This guide analyzes custom HNSW indexing in pure Go, providing microbenchmarks of pure Go AVX2/AVX-512 vector distance unrolling against Rust-based Qdrant and C++ Faiss across 1M 768-dimensional embeddings. Building a custom Go vector database engine with HNSW combines 256-bit SIMD AVX2 loop unrolling, off-heap mmap zero-GC slab memory, and Product Quantization (PQ-32) to get high recall at low latency while cutting vector RAM footprint dramatically. This post covers: ...