Cutting Vector Index Costs in a Developer Changelog Tracker (and When Web Search Wins)
Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context. Keep the vector index for know…
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Hybrid Search Architecture: Combining BM25 Keyword Search with Dense Vector Embeddings
Introduction & Industry Context In the early wave of generative AI and Retrieval-Augmented Generation (RAG) deployments, engineering teams gravi…
I Tested Amazon S3 Vectors' New Pre-Filtering Against Exact Ground Truth
A filtered vector query that asks for 10 results can come back with 2, and nothing errors. AWS's own launch post shows exactly that on a CLASSIC ind…
Running DynamoDB vector search locally
Dynoxide has had DynamoDB vector search since 1.0.0 landed on the 25th of August: vector indexes, SearchVectors , and the write paths keeping them c…
DynamoDB Now Does Vector Search
Originally published on Build With AWS . Subscribe for weekly AWS builds. On August 5, 2026, AWS made vector search generally available in Amazon Dy…
Vector Search Lands in DynamoDB Natively — Issue #89
This week shipped one of the more consequential infrastructure changes in a while: DynamoDB absorbed vector search, collapsing a common two-database…
Forget Total Recall. Give Your AI Agent Selective Memory with OpenSearch.
Imagine handing an LLM the full text of Anna Karenina and asking what Levin thinks about farming. The model has the information somewhere in its win…
Vector Search Fundamentals for RAG Design: ANN (HNSW), Distance Metrics, Metadata Filtering, and BM25
Introduction When designing a RAG (Retrieval-Augmented Generation) system, understanding what's happening inside vector search lets you tune the tra…
Bringing an External CRM's Chats into Firestore for AI Search: Vector Search, Webhooks, and a Stubborn Bundling Error
A client wanted their management panel's AI assistant to answer questions like "what did this contact write me" or "who replied about a certain topi…
"Most Of Your Vectors Are Steerage. Why Are They In First Class?"
I was on a call last month with a startup CTO who had just gotten their AWS bill. They had built a beautiful RAG application: semantic search, conve…
Silent Drift: Why Re-Embedding Only on Count Changes Rots Your Semantic Index
My index reported 354 points. The collection had 451. Both numbers were "correct," and that gap is the whole problem. Here's the setup. I have a wik…
Vector Split by Chunk: Why My Retrieval Stops at the Boundary I Drew
I watched a draft miss the exact file span I needed, and the failure was embarrassingly clean: the vector was "close," but the chunk I wanted was bu…
How Vector Search Actually Works: IVF and HNSW
Every system that does "semantic" anything — RAG pipelines, recommendation engines, image search, dedup — boils down to one operation: given this ve…
How We Vectorize 33.7M Ukrainian Court Decisions via Voyage AI
EDRSR — the Unified State Register of Court Decisions — is effectively all of Ukraine's judicial practice in open access. Today Qdrant holds **44M+…
A FalkorDB Vector Search Gotcha: Why Won't db.idx.vector.queryNodes Work?
When using FalkorDB (a Redis-protocol-compatible graph database) for GraphRAG or semantic search, we often want to tap into its built-in native vect…
ParadeDB NPM Package Seeks Feedback on Simplifying Postgres Integration with JavaScript via Drizzle ORM
Introduction: Bridging the Gap Between Postgres and JavaScript with ParadeDB The ParadeDB NPM package emerges as a targeted solution to a growing pa…
Vector Search at Scale: Why Your Index Isn't as Healthy as You Think
Vector search has become load-bearing infrastructure in modern AI systems remarkably fast. A year or two ago, it was primarily a research curiosity…