Database
Vector databases
How vector databases power semantic search and RAG — pgvector vs. dedicated engines, HNSW vs. IVF indexing, and hybrid search.
A vector database stores high-dimensional embeddings and answers approximate-nearest-neighbour queries: given a query vector, return the most semantically similar stored vectors fast. They are the storage layer underneath almost every RAG system, semantic search feature, and recommendation engine shipping in 2026, and the category has consolidated from a 2023 land-grab into a few durable choices.
The defining decision for most teams is pgvector-on-Postgres versus a dedicated engine (Pinecone, Weaviate, Qdrant, Milvus). Notifire tracks the releases that move that line: pgvector's HNSW and quantization support, the dedicated engines' move toward hybrid (dense + sparse/BM25) retrieval, and the indexing trade-offs — HNSW's recall-versus-memory profile against IVF's faster build and smaller footprint — that determine cost at scale.
Latest briefings on Vector databases
Data
Smarter AI Models Still Lack Context
New AI models consistently achieve higher benchmark scores, yet they often fail in real-world applications by hallucinating or mishandling queries. This gap highlights that raw intelligence isn't enough; models require specific, real-time context to perform reliably and reason effectively in production environments.
Taranpreet Singh ·
Infra
AI Retrieval Is Now Systems Problem
Scaling AI applications is revealing the limits of simple vector search. Production systems now require a complex retrieval layer that combines keyword matching, semantic search, ranking, and real-time data. This shift treats AI retrieval as a complex systems problem, not just a tooling one.
Ashish Kale ·
Infra
Why Your AI Search Needs More Than Vectors
Simple vector search is no longer enough for production AI. Companies are now building hybrid systems that combine it with ranking and personalization to deliver more relevant and useful results.
Ashish Kale ·
Frequently asked questions
What is a vector database?
A database optimised for storing embeddings — numeric vectors that capture the meaning of text, images, or audio — and retrieving the closest matches to a query vector via approximate nearest-neighbour (ANN) search. It's the retrieval layer behind semantic search, RAG, and recommendation systems, where the goal is finding things similar in meaning rather than exact keyword matches.
Should I use pgvector or a dedicated vector database?
If you already run Postgres and your workload is moderate — roughly under 100M vectors and under a few hundred QPS — pgvector with an HNSW index is usually the right call: one database, one backup, one operations team, plus transactional consistency with your relational data. Above that scale, or when you need very high QPS and advanced filtering, a dedicated engine like Qdrant, Milvus, or Pinecone starts to earn its keep.
What's the difference between HNSW and IVF indexes?
HNSW (Hierarchical Navigable Small World) is a graph index with excellent recall and low query latency, but it builds slowly and holds the whole graph in memory. IVF (inverted file) clusters vectors and searches only the nearest clusters — faster to build and lighter on memory, at some cost to recall. HNSW is the default for most quality-sensitive workloads; IVF (often with product quantization) wins when memory or index-build time dominates.
What is hybrid search and why does it matter?
Hybrid search combines dense vector similarity with sparse keyword scoring (BM25 or SPLADE), then fuses the rankings — commonly with reciprocal rank fusion. Pure vector search misses exact-match needs like product SKUs, error codes, or rare proper nouns, while keyword search misses paraphrase. Fusing both consistently beats either alone, which is why every major engine now ships hybrid retrieval as a first-class feature.