How Roaring Bitmaps Make Filters Fast
Roaring bitmaps store integer sets in three container types so filters stay small and fast. How they work, why OpenSearch leans on them, and the tradeoffs.
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Dedupe a RAG Corpus with MinHash and LSH
Exact md5 hashing misses near-duplicate documents; comparing every pair is O(n²). How MinHash and LSH find and cluster them across a RAG corpus.
Reciprocal Rank Fusion for Hybrid Search
Reciprocal Rank Fusion merges two ranked lists without tuning score weights. The RRF formula, why the k constant matters, and how to run it in OpenSearch.
Contextual Retrieval for RAG, Step by Step
Vector search misses chunks that lost their document context. Contextual Retrieval prepends a short LLM-written summary to each chunk before you embed it.
Zero-Downtime Reindexing in OpenSearch
Change an OpenSearch mapping without dropping writes or serving stale data. A step-by-step reindex with aliases, the Reindex API, and the failure modes.
Contextual Retrieval for RAG Pipelines
Chunking a document for RAG strips each piece of its context. Contextual retrieval adds an LLM-written note to every chunk before you index it.
How to Choose an Embedding Model for RAG
The embedding model sets the ceiling on RAG retrieval quality. How to choose one by task fit, sequence length, dimensions, and domain, plus the silent bugs.