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#semantic-search

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Hassan Aziz21m ago · 14 min read

How semantic search works, with a Python example

The short version Semantic search ranks results by meaning: the query and every document become vectors, and the documents whose vectors point closest to the query's come first. An embedding model tu

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Vikkurthi Manikanta1d ago · 4 min read

Why Enterprise AI Agents Fail Without Structured Knowledge

Your agents aren't hallucinating because the models are bad. They're failing because they have no world model of your enterprise. The inconsistency problem Picture this: your team has just deployed a

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RONI DASOct 15 · 17 min read

I cut EmbeddingGemma 2's vectors to a sixth of their size. It got more answers right, and that was the worrying part.

A computer cannot compare meanings. It can compare numbers. So if you want a search box that understands "why can't you make good tea at the top of Everest", you first need something that turns words

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Asfaq LeeonOct 9 · 13 min read

Embeddings, Vector Search & RAG from Scratch with Node.js and PostgreSQL (pgvector)

Once I thought RAG should start with npm install langchain and that time I don't understand the chaining abstractions. One of my friend told me that if something breaks (and it will), you can't debug

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praveenkulhareeOct 4 · 6 min read

Exploring GenAI Retrieval with a Resume: Keyword, Semantic, and Hybrid Search

I’ve been spending some time learning and exploring Generative AI concepts, especially around embeddings, retrieval, and RAG. Instead of starting with a large production-style application, I wanted to

2 dev-praveen.hashnode.dev

ChidinmaOct 2 · 21 min read

The Index Is a Bet You Made at Write Time

Search feels instantaneous. We type a few words and hit enter. Millions of documents collapse into a neat list of results in less than a second. It gives the illusion of a system that heard your reque

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Pranshu BansalOct 1 · 23 min read

I Tested Qdrant Quantization and mmap on EV Charger Search

When I started this project, I had a question in mind: if a charging network keeps adding stations and search records, how much memory will its vector search need? A small demo can fit on a laptop. A

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Mohammed Yahya AhmedSep 25 · 9 min read

Building Consistent Semantic Search: Updates, Embeddings & Concurrency in BrainZen

When I wrote my previous article, I had reached an important point in BrainZen. The creation pipeline was working: Content ↓ Semantic text ↓ Embedding ↓ MongoDB And I had started designing t

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Thai TranSep 24 · 5 min read

Why semantic search is not the smartest search

Key takeaway Semantic search is not the smartest search. It covers the words that miss. The exact match still has to lead. Keyword search wins on a unit code and on the word bachelor. It misses “tut

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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