How large embedding backfills stress PostgreSQL through batch size, WAL growth, checkpoints, autovacuum lag, bloat, index timing, throttling, and rollback planning.
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.
A production decision guide for GraphRAG, entity graphs, community summaries, relationship reasoning, and where graph retrieval is overkill.
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.
Why production search systems should combine BM25, vector retrieval, filters, fusion, ranking, and reranking before relying on LLM answers.
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.
A breakdown of Fareed Khan's open-source RAG pipeline for large document corpora: hybrid retrieval, claim-level faithfulness verification, and calibrated abstention — with the real numbers, and the honest limits of the 10M-vector scale claim.
A production architecture for product catalog hybrid search with OpenSearch, combining BM25, vector retrieval, filters, shard design, reranking, and relevance debugging.
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.
A safe migration path from keyword search to semantic or hybrid OpenSearch retrieval using dual indexing, embeddings, backfill, relevance evaluation, A/B testing, fallback, rollback, and cutover.
OpenSearch vector search failure modes for operators, including shard count, hot shards, tenant skew, memory pressure, recall degradation, slow merges, filters, and recovery.
A production guide to OpenSearch hybrid retrieval with BM25, vector k-NN, metadata filters, score fusion, reranking, relevance debugging, and observability.
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.
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.