Category · 23 articles
Deep dives into how vector databases work. Covers indexing structures like HNSW and IVF, approximate nearest neighbor search, product quantization, similarity metrics, and fast semantic retrieval at scale.
Pinecone, Weaviate, Milvus, and Qdrant are the four most widely used vector databases for production AI applications in 2026. This is a side-by-side comparison of their architecture, performance, filtering, hybrid search, cost, and developer experience so you can pick the right one for your stack.
pgvector or Pinecone? This honest comparison covers setup, performance, cost, filtering, hybrid search, and the real question most teams miss: when does your Postgres setup stop being enough?
A complete Qdrant tutorial for 2026. Covers Docker setup, creating collections, inserting vectors with Python, similarity search, metadata filtering, hybrid search, and payload indexing. Includes working code for every step.
Qdrant and Weaviate are both strong open-source vector databases, but they are built for different teams. This side-by-side comparison covers architecture, API style, filtering, hybrid search, multi-tenancy, cost, and a clear recommendation for each use case.
pgvector adds vector similarity search directly to PostgreSQL. This guide covers installation, creating vector columns, HNSW and IVFFlat indexes, cosine and L2 distance queries, filtering, Python integration, performance tuning, and when to migrate to a dedicated vector database.
Pinecone and Qdrant are the two most popular vector databases for production RAG applications in 2026. This is a side-by-side comparison of their architecture, performance, filtering, hybrid search, cost, and developer experience so you can pick the right one for your stack.
Every vector database comparison ends with 'it depends on your use case.' This one does not. A practical framework that maps your scale, budget, and infrastructure to a clear recommendation across Pinecone, Qdrant, Weaviate, Milvus, pgvector, and Chroma.
The vector database is the retrieval engine behind every RAG system. This guide covers HNSW indexing, how cosine similarity works, pgvector vs purpose-built databases, real cost numbers at scale, and a decision framework for picking between Pinecone, Qdrant, Weaviate, Milvus, and Chroma in 2026.
A research-backed, step-by-step walkthrough of everything that happens inside a vector database from the moment a query request arrives to the moment results are returned. Covers API parsing, filter strategy selection, ANN index traversal, multi-segment merge, distributed shard coordination, payload fetch, scoring, and response serialization.
A research-backed guide to vector indexing: what it is, why it exists, how the three-way tradeoff between recall, latency, and memory shapes every index decision, how to select the right index type for your workload, how to measure index quality, and the operational factors that affect index performance in production.
A research-backed explanation of Product Quantization (PQ) for vector compression. Learn how subspace decomposition reduces vector storage by 32 to 64 times, how codebooks are trained, how asymmetric distance computation preserves search speed, how to tune M and nbits, and when to use PQ with IVF or HNSW.
A research-backed, diagram-rich explanation of the HNSW (Hierarchical Navigable Small World) algorithm. Learn how the layered graph is built, how query traversal works layer by layer, how the three key parameters M, ef_construction, and ef_search control the recall-latency tradeoff, and how to tune HNSW for production vector search workloads.
A research-backed technical explanation of the Inverted File Index (IVF) for vector similarity search. Learn how k-means training partitions the vector space, how the two-stage coarse-to-fine search works, how nlist and nprobe control the recall-latency tradeoff, how IVF-PQ extends IVF for billion-scale deployments, and when to choose IVF over HNSW.
A research-backed, mathematically precise explanation of cosine similarity vs Euclidean distance for vector search. Learn the geometry behind each metric, when each is the right choice, how dot product relates to both, and what happens when you pick the wrong metric for your embedding model.
A research-backed, technically precise explanation of exact nearest neighbor search versus approximate nearest neighbor (ANN) algorithms. Learn why exact search fails at scale, how ANN achieves massive speedups with bounded accuracy loss, how recall is measured, and how to tune ANN parameters for your production workload.
A research-backed, step-by-step explanation of how similarity search works inside a vector database. Covers distance computation, candidate retrieval from ANN indexes, score normalization, metadata filtering, post-processing, and reranking with working Python code examples.
A technical deep dive into how vector databases work under the hood. Learn how the ingestion pipeline, storage layer, ANN index structures, similarity metrics, query lifecycle, metadata filtering, and distributed architecture fit together inside production vector database systems.
A research-backed comparison of dedicated vector databases and Elasticsearch for AI search workloads. Learn how their architectures differ, what the benchmarks say about latency and indexing speed, where each system wins, and a practical decision framework for your stack in 2026.
A research-backed comparison of vector databases and traditional relational databases. Learn how their data models, index structures, query languages, and scalability patterns differ, when to use each, and how pgvector bridges both worlds for teams already running PostgreSQL.
A research-backed deep dive into why B-tree and hash indexes cannot handle high-dimensional vector search. Learn about the curse of dimensionality, why traditional structures break down above 10 to 15 dimensions, and how HNSW and IVF were designed from first principles to solve the problem.
A research-backed guide to dense and sparse vectors in machine learning. Learn how each representation works, when to use BM25 versus embeddings, how SPLADE bridges both worlds, and why hybrid search combining dense and sparse retrieval consistently outperforms either method alone.
A complete beginner guide to vector databases. Learn how they store high-dimensional embeddings, how similarity search works, how they differ from SQL databases, and why every serious AI application needs one.
A research-backed, step-by-step guide to semantic search. Learn how it differs from keyword search, how the full pipeline works from chunking to reranking, what makes it fail, and how to build a working semantic search system with Python code examples.