Vector Databases
17 posts, 2024 to 2026
2026
- Hybrid Search: Combining Vector Similarity with Metadata Filters and Keyword Search (opens on Medium)
- Distributed Systems Concerns: Replication, Consistency, and Query Routing (opens on Medium)
- Sharding and Partitioning at Scale: Splitting Billions of Vectors Across Machines (opens on Medium)
- Storage Architecture: Memory, Disk, and Memory-Mapped Layouts at Billion-Vector Scale (opens on Medium)
- Indexing Deep Dive: DiskANN and Disk-Resident Graphs (opens on Medium)
- Vector Compression and Quantisation: Fitting Billions of Vectors in Memory (opens on Medium)
- Indexing Deep Dive: IVF and IVF-PQ (opens on Medium)
- Indexing Deep Dive: HNSW, Under the Hood (opens on Medium)
- The Curse of Dimensionality: Why Exact Nearest-Neighbor Search Does Not Scale (opens on Medium)
- Inside the Encoder: Tokens, Attention, and How a Transformer Actually Works (opens on Medium)
- Distance Metrics and Similarity Search: Cosine, Euclidean, and Dot Product (opens on Medium)
- Scaling Contrastive Training: Batch Size, GPU Gathering, and Gradient Caching (opens on Medium)
- How Embedding Models Actually Learn: The Math Behind Contrastive Training (opens on Medium)
- Embeddings 101: How Text, Images, and Audio Become Vectors (opens on Medium)
- What Is a Vector Database? Origins, Use Cases, and How It Differs from Relational and NoSQL Systems (opens on Medium)
- Embeddings & Vector Databases: Architecture and Trade-offs (opens on Medium)