High-throughput vector search, HNSW index graph tuning, SIMD acceleration, and pgvector vs dedicated Qdrant clusters.
Build retrieval-augmented generation that never leaves your network. A reference architecture for air-gapped Kubernetes: signed model supply chain, NVIDIA GPU Operator, vLLM serving, pgvector with permission-aware retrieval, and default-deny networking.
Scale semantic RAG to 10M+ embeddings: HNSW graph skip-list mathematics, pgvector 0.7 memory bottlenecks, Qdrant 8-bit scalar quantization, and hardware SIMD acceleration.
An authoritative systems engineering guide to programmatic internal linking: directed graph PageRank modeling, vectorized semantic embeddings via pgvector, crawl depth reduction to under 3 clicks, and Next.js 14 edge caching for 100k+ page catalogs.
How to eliminate enterprise RAG hallucinations: Pairing dense pgvector embeddings with sparse BM25 lexical search, Reciprocal Rank Fusion (RRF), Cross-Encoder reranking, and parent-child document chunking.
The hidden operational tax of running dedicated vector databases alongside your primary database. How PostgreSQL 16, pgvector 0.7+, and HNSW indexing deliver sub-8ms vector search without dual-write synchronization bugs.
Our engineering team designs sub-50ms distributed backends, edge AI pipelines, and high-concurrency systems.