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MYMohammed Yahya Ahmedlearningwithyahya.hashnode.dev9h ago · 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

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MYMohammed Yahya Ahmedlearningwithyahya.hashnode.dev2d ago · 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

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MYMohammed Yahya Ahmedlearningwithyahya.hashnode.dev2d ago · 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

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SSsatyam sahuthebuildersnotebook.hashnode.devAug 27 · 44 min read

Building Hybrid Search for Financial Intelligence

Originally published on Medium. Reposted here for the Hashnode community. Consider this. You are an equity analyst or an engineer building an AI-powered financial research tool, and you are trying to

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SRSukalyan Roysukalyanroy.hashnode.devJun 20 · 13 min read

Adding Rank Fusion to my RAG retrieval logic

In the previous part, we explored Agentic RAG as an evolution of the traditional Retrieval-Augmented Generation pipeline:https://sukalyanroy.hashnode.dev/discovering-rags-2-what-is-agentic-rag TL;DR

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VKVamsi Krishnablog.vamsiannamreddy.comJun 6 · 3 min read

Day 17: Why hybrid search is often the practical default

Choosing between keyword and semantic retrieval means choosing which failure mode you would rather live with. Hybrid search declines the choice. Run both, merge the results, cover each system's blind

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SSapotaCorpsapotacorpvn.hashnode.devMay 24 · 6 min read

Why hybrid search is the boring default we keep recommending

A founder we work with had been debugging a confusing failure for two weeks. The internal AI assistant could not find content about "GPT-4o pricing" even though the exact phrase appeared in three diff

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JGJames Gainesblog.gainesai.comApr 28 · 10 min read

Why Vector Search Alone Fails on Regulatory Documents

TL;DR Vector search is great at finding semantically similar content, but it struggles with broad keyword-heavy queries common in regulatory and clinical document search. By combining BM25 keyword mat

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CTChandre Tosca Van Der Westhuizenmindsdb.hashnode.devFeb 4 · 10 min read

How Web Apps Can Gain a Competitive Edge with Supabase and MindsDB

SaaS companies live and die by their metrics. Data is collected in operational systems, copied into warehouses, transformed through pipelines, and finally visualized in dashboards. Finance teams learn where to click. Analysts learn how to maintain th...

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AKAnjali Kakdevectorai.hashnode.devFeb 4 · 14 min read

Vector Databases in Practice: Building a Realistic Hybrid Search RAG System with Qdrant

Vector databases are often introduced as tools for semantic similarity search. In practice, that understanding breaks down the moment you try to build a real RAG system. In this article, I explain what vector databases actually do inside modern retri...

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#hybrid-search

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Trending tags this week

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

Choosing the Embedding Architecture for BrainZen

Building Search for BrainZen: From Embeddings to Hybrid Retrieval

Building Hybrid Search for Financial Intelligence

Adding Rank Fusion to my RAG retrieval logic

Day 17: Why hybrid search is often the practical default

Why hybrid search is the boring default we keep recommending

Why Vector Search Alone Fails on Regulatory Documents

How Web Apps Can Gain a Competitive Edge with Supabase and MindsDB

Vector Databases in Practice: Building a Realistic Hybrid Search RAG System with Qdrant