Deep pgvector internals, plus newer ground with Qdrant and Weaviate as the vector landscape expands.
Why pgvector changes backup and restore planning for RAG systems, including vector column size, index rebuilds, embedding reproducibility, source-of-truth design, and DR runbooks.
A DBA watchlist for running pgvector on Aurora PostgreSQL, covering extension support, memory, I/O, WAL, replicas, failover, backups, parameters, and cost.
A staged playbook for moving from Postgres and pgvector prototypes to hybrid search, agents, and GraphRAG only when the workload requires it.
Why marketplace, travel, retail, local commerce, and support search often need a dedicated search platform instead of only pgvector.
A database-first decision guide for using PostgreSQL full-text search and pgvector before adding a search engine or vector database.
A production decision framework for choosing lexical search, vector search, hybrid retrieval, RAG, agents, or GraphRAG by workload shape.
A database engineer's guide to Weaviate hybrid search, including collections, objects, BM25, vectors, filters, tenancy, schema design, and operational tradeoffs.
How Weaviate named vectors let one object carry title, body, image, code, or support-ticket embeddings, and what that means for schema evolution and backfills.
Why dense plus sparse retrieval in Qdrant needs careful fusion, score normalization, candidate sizing, and reranking to work in production RAG.
A DBA and platform-engineering view of Qdrant for production RAG, covering collections, points, payloads, filters, dense and sparse retrieval, snapshots, scaling, and limits.
When OpenSearch is the right vector-search platform because keyword search, hybrid retrieval, relevance debugging, and search operations already matter.
A practical DBA guide to pgvector HNSW and IVFFlat tradeoffs across build time, memory, recall, writes, maintenance, and query tuning.
Why approximate pgvector searches can under-return rows after SQL filters, and how to tune filtered HNSW with ef_search, partial indexes, partitioning, and iterative scans.
When a Postgres-first RAG design with pgvector is simpler, safer, and easier to operate than adding a separate vector database.
A production-oriented decision matrix for choosing pgvector, OpenSearch, Qdrant, or Weaviate by workload shape, filters, hybrid search, operations, cost, tenancy, and recovery.
OpenSearch vector search failure modes for operators, including shard count, hot shards, tenant skew, memory pressure, recall degradation, slow merges, filters, and recovery.
The tradeoffs of Amazon OpenSearch Service for vector search, including managed operations, scaling, instance choice, storage, memory, transfer, snapshots, and index design cost.
An infrastructure view of OpenSearch vector search, covering k-NN fields, HNSW, shards, segments, refresh, merges, memory, node sizing, and operational gotchas.
How to combine PostgreSQL full-text search and pgvector for low-cost hybrid retrieval, including tsvector, ranking, semantic search, fusion, filters, observability, and when to outgrow it.
How DBAs should read PostgreSQL EXPLAIN plans for pgvector queries, including index scans, sequential scans, ORDER BY distance, LIMIT, filters, iterative scans, cost estimates, and plan surprises.
How to design tenant-scoped pgvector search with tenant filters, partial indexes, list or hash partitioning, filtered HNSW behavior, query plans, operational limits, and isolation tradeoffs.
The second wave of March 2026 breakouts: an agent that learns from every conversation, a Rust vector index that outperforms FAISS at a fraction of the memory, and a Kubernetes-native agent control plane.
A production triage guide for multi-stage vector search: isolating query embedding latency, HNSW graph degradation, hybrid BM25 fusion skew, and cross-encoder reranking bottlenecks.
A systems engineering analysis of vector search performance: navigating the fundamental tradeoff between Recall@K, query latency, index memory footprint, quantization, and filtered search.
October's memory and retrieval breakouts: a structured agent memory framework with benchmarks, a self-hosted cognitive memory engine, and sub-10ms semantic search without a vector database cluster.
Three May 2025 open-source projects eliminate the manual scaffolding that blocks every AI agent deployment: orchestration glue, vector database setup, and MCP gateway configuration.
How pgvector adds vector storage and similarity search to PostgreSQL, what the three distance operators do, and the index you must create before you hit 100K rows.
How tree-based retrieval can improve DB runbooks, schema docs, and incident knowledge over chunked vector search.
A DBA-friendly explanation of how vector search works, why GPUs help, and where vector retrieval fits inside modern database and AI systems.
How CPU, GPU, and TPU architectures differ in ways that matter for databases and AI workloads — and which compute class to reach for when adding vector search, embedding generation, or GPU-accelerated analytics.