Most teams reach for Neo4j or Apache AGE the moment they read the Microsoft GraphRAG paper. The honest answer is most GraphRAG workloads don’t need a graph database — pgvector + recursive CTEs + tsvector handle 1-3 hop traversal in one ACID database.
Seven walkthroughs with opinions — what each chunker is good at, where it falls over, and the corpus shape that flips the leaderboard between them. A field guide, not a recommendation. Bakeoff first.
An illegal chop shop for your data — the YAML-driven RAG ingest tool that ships a bakeoff primitive so you measure chunker × embedder × your corpus instead of vibe-picking from somebody else’s blog post.
Most production RAG pipelines run on one signal: chunks. Add doc-level summaries plus structured metadata in Postgres and you get three signals — with working SQL at the bottom of the post.
Sub-millisecond extractive summarization with byte-identical Python and Rust implementations. The preprocessor that sits in front of the LLM call and cuts tokens 40-94 percent.
Capture your real MySQL slow log, push it through a MySQL→Postgres transform pipeline, and replay against Postgres. Every failure in replay is one you don’t find in production.
A full LLM-driven tuning loop with four real outcomes: a successful apply, an automatic rollback on regression, a safety-layer rejection, and a hint-driven redirect. No recommendations without measurement.
Build a capacity benchmark from real captured traffic, sweep 1x → 50x, and drive PostgreSQL into saturation on purpose so you find the knee before production does.
From git clone to capturing a real PostgreSQL workload and replaying it against a test target — end-to-end on your laptop, with every command actually run.
Part 3 of 3. 391 real SCOTUS cases, four retrieval strategies running side by side, multi-hop Cypher that no hybrid search can match, and the production-ready 3-stage architecture you should actually ship.
