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IVF

5 articles · Page 1 of 1
Lakehouse Research Base
Lakehouse Research Base
Sep 23, 2026 · Databases

Milvus 3.x: From Vector Database to Lake-Native AI Retrieval Engine

This article provides a comprehensive analysis of Milvus 3.x's evolution from a vector database to a Lake-Native retrieval engine, covering its architecture, External Collection for zero-copy indexing on data lakes, Storage V3, ANN indexes (HNSW, IVF, DiskANN), hybrid search, and production RAG architectures with performance tuning and troubleshooting guidance.

ANN IndexDiskANNHNSW
0 likes · 37 min read
Milvus 3.x: From Vector Database to Lake-Native AI Retrieval Engine
AI Architect Hub
AI Architect Hub
Apr 27, 2026 · Artificial Intelligence

Why HNSW Can Speed Up Search 50× Compared to Brute‑Force? A Hands‑On Guide to Building Vector Indexes

The article explains why brute‑force vector search is painfully slow, introduces Flat, IVF, and HNSW index structures, compares their speed, memory and accuracy, shows common pitfalls, provides production‑grade Python code, and presents benchmark results that demonstrate HNSW’s superior speed‑accuracy trade‑off.

AIFAISSHNSW
0 likes · 12 min read
Why HNSW Can Speed Up Search 50× Compared to Brute‑Force? A Hands‑On Guide to Building Vector Indexes
AI Engineer Programming
AI Engineer Programming
Apr 26, 2026 · Artificial Intelligence

From Bag‑of‑Words to Semantics: How Embeddings Turn Meaning into Numbers (Part 2)

The article explains how embedding techniques encode semantic information into numeric vectors, covering Word2Vec and GloVe fundamentals, BERT anisotropy, SimCSE contrastive learning, alignment and uniformity metrics, ANN index structures such as HNSW, IVF and PQ, Matryoshka representation learning, practical deployment challenges, and evaluation best practices.

ANNBERTEmbedding
0 likes · 23 min read
From Bag‑of‑Words to Semantics: How Embeddings Turn Meaning into Numbers (Part 2)
DeepHub IMBA
DeepHub IMBA
Apr 11, 2026 · Artificial Intelligence

Understanding Vector Similarity Search: Flat Index, IVF, and HNSW

This article explains why vector databases are needed for semantic search of unstructured data and provides a detailed, step‑by‑step comparison of three core vector similarity algorithms—cosine similarity, Flat Index, IVF, and HNSW—highlighting their trade‑offs in accuracy and speed.

HNSWIVFVector Search
0 likes · 10 min read
Understanding Vector Similarity Search: Flat Index, IVF, and HNSW
Big Data Technology Tribe
Big Data Technology Tribe
Feb 26, 2026 · Databases

How optimize_indices Improves Query Performance in Lance

The article explains the purpose and inner workings of Lance's optimize_indices function, detailing how it incorporates newly appended data into existing indexes, merges delta indexes, and manages partition adjustments to maintain fast vector and scalar query performance without full re‑training.

IVFLanceoptimize_indices
0 likes · 8 min read
How optimize_indices Improves Query Performance in Lance