#vector-search articles

Databricks Lakehouse Platform for Real-Time, Agentic Data and AI Workloads

For years, data engineering has often meant connecting systems that were never designed to work together. ERP data lives in one place. Manufacturing data lives somewhere else. Application events arriv

aidatalegends.hashnode.dev

Aritra Chakraborty4d ago · 4 min read

Databricks Runtime Introduces NEAREST BY: Native SQL Vector Search at Scale

Vector search has become a foundational building block for retrieval-augmented generation (RAG), recommendation systems, semantic matching, and AI-powered data applications. But there is an architectu

aidatalegends.hashnode.dev

Muhammad TahirOct 11 · 13 min read

Hybrid Search Architecture: Combining BM25 Keyword Search with Dense Vector Embeddings

This article was originally published on Muhammad Tahir's Portfolio. Introduction & Industry Context In the early wave of generative AI and Retrieval-Augmented Generation (RAG) deployments, engineerin

mtdeveloper.hashnode.dev

marketSep 28 · 1 min read

Correct where= cut 12 cross-product bleeds — meta_purity 0.65→1.0 on our 96-chunk Chroma set

Same author multi-product corpus (96 chunks, products A/B/C + locale/version) on Chroma where=. Compared vector-only vs correct $and vs wrong product. Desk API: $0. pipeline P@5 Recall@5 empty% met

eworldlive.hashnode.dev

Mohammed Yahya AhmedSep 24 · 7 min read

From Embeddings to Search Architecture: Building BrainZen’s Retrieval Foundation

When I first started adding semantic search to BrainZen, I thought the difficult part would be generating embeddings and storing them in MongoDB. It turns out that was only the beginning. Once the emb

learningwithyahya.hashnode.dev

Mohammed Yahya AhmedSep 22 · 5 min read

Choosing the Embedding Architecture for BrainZen

When I first started adding semantic search to BrainZen, I figured the actual vector search algorithms would be the biggest headache. I was wrong. The very first hurdle was a much more foundational qu

1 learningwithyahya.hashnode.dev

Mohammed Yahya AhmedSep 21 · 5 min read

Building Search for BrainZen: From Embeddings to Hybrid Retrieval

Search initially seems simple: type a query, get matching content from the database. But once I started trying to make BrainZen—my second-brain app—actually understand what I'm looking for by meaning

5 learningwithyahya.hashnode.dev

Jaival SutharSep 21 · 26 min read

Knowledge Hub: Building a Multi-Source RAG System for Real-World Knowledge

Most RAG systems are easy to demonstrate. Put a document into a vector database, retrieve a few chunks, pass them to an LLM, and generate an answer. The difficult part starts when the system has to an

jaivalsuthar.hashnode.dev

Niran PravithanaSep 19 · 11 min read

Cutting MCP Round-Trips with Jev (Part 3)

The ping-pong problem MCP pays every day MarketIV lets AI assistants (like Claude) pull market data through MCP — a set of "tools" the LLM can call itself: find stocks, pull financials, run screens, a

2 marketiv.hashnode.dev

vatsala singhSep 4 · 43 min read

Building a Billion-Vector Search System Without Putting Everything in RAM

In the landscape of high-scale AI, many architects fall into the “RAM Trap”: the expensive conviction that a billion-vector search system requires a professional-grade server cluster groaning under te

techwithvatsala.hashnode.dev

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