Four Vectors, One Record: How I Split Embeddings Before They Hit Search
The failure that pushed me into this design was not subtle. I had a blended embedding that kept returning candidate matches that looked reasonable at …
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RocheDB: Data Locality as a First-Stage Retrieval Index
One of the ideas behind RocheDB is easy to miss if we describe it only as a NoSQL database, a document store, or a vector-aware storage engine for AI …
Give Your AI Assistant a Private Memory
AI assistants are very good at explaining things, but they usually have a weak memory of your own context. They do not know which documentation page y…
The Hybrid Retrieval Pattern
Pattern Defined Precise Definition: Hybrid Retrieval is an inference pattern that combines semantic vector search with traditional keyword-based BM25 …
Vector Search in Elasticsearch: From Keywords to Meaning - Building Semantic Search and RAG Pipelines
You type "k8s deployment troubleshooting" into your documentation search. The top result is a page about Kubernetes architecture that never mentions t…
Building Hybrid Search with PostgreSQL, pgvector, and Citus
Search looks simple from the outside. A user types something like: short-range copper module and expects the system to return the right product, maybe…
From Elasticsearch Bottlenecks to Weaviate: How We Built Fast Hybrid Search in Production
For years, Elasticsearch was one of those tools I would almost automatically reach for whenever a system needed search. And honestly, for many use cas…
One Question, Five AI Search Engines, Five Different Answers
I asked five AI search engines the same question: "What's the best CI/CD tool for a small team?" Google AI Overviews recommended GitHub Actions. ChatG…