What is Vector Database | Internal Implementation of Vector DataBase

Ever wondered how vector databases power lightning-fast semantic search, recommendation engines, and generative AI? It's not magic, it's brilliant engineering! In this deep-dive video, we'll peel back the layers and explore the core algorithms that make modern vector search possible.

We're going beyond the surface-level "what" and diving deep into the "how." You'll learn the internal implementation secrets behind the most popular vector indexing and compression techniques used by databases like Pinecone, Weaviate, Milvus, and Chroma.

WHAT YOU'LL LEARN IN THIS VIDEO:
We break down the four pillars of efficient vector search:

1️⃣Inverted File Index (IVF / IVF-ADC): Discover how vector databases create a "table of contents" for your data by clustering vectors. We'll explain how this drastically narrows down the search space from millions of vectors to just a handful.

2️⃣ Binary Space Partitioning (Trees): We'll explore the classic tree-based approach (used in libraries like Annoy) that recursively splits the vector space, making search an efficient path-finding problem.

3️⃣ Hierarchical Navigable Small Worlds (HNSW): The powerhouse behind many modern vector databases. We'll demystify its multi-layered graph structure that allows for incredibly fast and accurate Approximate Nearest Neighbor (ANN) searches. Understand why it's the go-to for high-performance applications.

4️⃣ Product Quantization (PQ): Learn the clever compression technique that shrinks massive, high-dimensional vectors into tiny codes. We'll show you how PQ dramatically reduces memory and storage costs without sacrificing too much accuracy.


00:00 Introduction to Vector Database
03:54 Exact Nearest Neighbor
10:33 Approximate nearest neighbor
13:05 Clustering /Inverted File Index
23:06 The Decision Tree Method (Binary Space Partitioning)
42:20 Hierarchical Navigable Small Worlds - HNSW
52:10 The Compression Method (Product Quantization - PQ)
01:15:56: Compare different algorithm for Vector DB
01:17:00 Different Vector Database
01:23:10 Why SQL and noSQL DB is not used for Vector DB


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