Mercurium Analytics
Official@mercurium-analytics · United Kingdom
AI-Complience Company
Agent Skills by Mercurium Analytics
Showing 10 vetted skills indexed across 1 GitHub repositories.
Mercurium-Analyticspg-cron-scheduling
Schedule Postgres maintenance jobs with pg_cron in-database.
Mercurium-Analyticspg-repack-runbook
Rebuild bloated PostgreSQL tables online with pg_repack.
Mercurium-Analyticspg-partman-partitioning
Automate declarative partitioning for large PostgreSQL tables with pg_partman.
Mercurium-Analyticspgvectorscale-diskann
Enables memory-efficient large-scale vector search on PostgreSQL using DiskANN and SBQ.
Mercurium-Analyticspg-search-bm25
Index BM25 text searches in Postgres with the pg_search extension.
Mercurium-Analyticspgbouncer-pool-modes
Configure PgBouncer pool modes for Django, FastAPI, and Celery stacks.
Mercurium-Analyticsbm25-tokenizer-guide
Guide per-field tokenizer selection for BM25 indexes with a decision tree.
Mercurium-Analyticshybrid-lexical-semantic
Fuse BM25 lexical relevance with vector semantic similarity in pgsv.hybrid_search.
Mercurium-Analyticsdjango-pgsearch-patterns
Integrate Django with pg_search and pgvector for hybrid BM25 and vector search.
Mercurium-Analyticsfastapi-pgsearch-patterns
Combine FastAPI with pgvector and pg_search for hybrid lexical and vector retrieval.
Frequently Asked Questions About Mercurium Analytics
FAQPage SchemaWhat specific database tasks does Mercurium Analytics enable?▼
Mercurium Analytics enables in-database job scheduling, online table rebuilding, declarative partitioning, and high-performance search indexing. It provides technical patterns for integrating BM25 lexical search and DiskANN vector retrieval directly within PostgreSQL environments to support complex data retrieval requirements.
Which engineering personas benefit from these database patterns?▼
These patterns are designed for Database Administrators, Backend Engineers, and Data Architects managing high-scale PostgreSQL deployments. They specifically assist teams building search-heavy applications who require optimized connection pooling and efficient indexing strategies for large-scale relational and vector datasets.
What are the primary prerequisites for implementing these search patterns?▼
Implementation requires a PostgreSQL instance with specific extensions installed, including pg_cron, pg_repack, pg_partman, pg_search, and pgvector. Users must also have an existing application stack, such as Django or FastAPI, configured to interface with these database-level search and maintenance capabilities.