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Modern semantic search, retrieval-augmented generation (RAG) pipelines, and large-scale recommendation models heavily rely on embeddings — transformations of natural language text...
Why Searching 1 Million Arrays of 1536 Numbers Does NOT Melt Your CPUContinue reading on Medium »
The idea that makes language models, recommendation systems, and semantic search actually workContinue reading on Medium »
How a language model reads a word — and why context changes everythingContinue reading on Medium »
Недавно мы в Beeline Cloud делали подборку руководств и обучающих материалов по теме эмбеддингов. Сегодня решили поговорить о распространенном подходе к семантическому поиску на ос...
The Setup: The Hype Machine It’s vector database season. Conferences are full of RAG pipeline talks. Pinecone raised over $100 million; Milvus, Weaviate, and Qdrant are all well-fu...
In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for modern AI applications. We start...
Liquid AI's LFM2.5 Retrievers combine a dense bi-encoder and ColBERT late-interaction model for multilingual search on edge devices. The post Liquid AI Introduces LFM2.5-Embedding-...
Vector search is a search technique that finds results based on meaning, not just...
Анизотропия эмбеддингов не всегда зло, но «сырой» косинус часто даёт слишком размытый сигнал. Центрирование убирает общий фон и помогает увидеть различия, не разрушая локальные смы...
You've probably shipped this bug before, where a user types " affordable laptop " into your search bar and gets zero results.
Every RAG tutorial follows the same script: embed your documents, spin up a vector database (Pinecone, Weaviate, pgvector, OpenSearch), manage its infrastructure, and pray the cost...
You've seen the tutorials. Spin up Pinecone, call .upsert(), do a similarity search, ship it. Everyone claps. The demo works. Then you take it to production and it starts lying to...
I spent a weekend building a Q&A bot for my team's internal docs. It sounded easy: dump PDFs into a vector database, query with embeddings, get answers. Three days later, I had...
Отзывы пользователей — один из самых ценных источников информации о продукте, при этом часто клиенты описывают одну и ту же тему или проблему десятками разных слов. Раньше работать...
Stop treating pgvector like a regular index. Here’s the production playbook nobody talks about.Continue reading on Medium »
The rapid growth of generative AI has introduced a concept I hadn’t paid much attention to before: vector databases. Initially, I assumed…Continue reading on Medium »
A hands-on guide to setting up image similarity search in Milvus, and why visual replication isn't always enough. The post The Power and Pitfalls of Vector-Based Image Search appea...
В данной статье я хочу пройтись по двум самым популярным алгоритмам векторного поиска, используемым на практике. Попробуем понять, почему точный поиск не работает в высоких размерн...
In this quiz, you’ll test your understanding of Embeddings and Vector Databases With ChromaDB. By working through this quiz, you’ll revisit key concepts like vectors, cosine si...
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of...
Chroma vs FAISS vs Pinecone is not a simple winner-takes-all comparison. Chroma is best for local RAG development, lightweight embedding storage, and fast Python prototyping. FAISS...
In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG) system. The articl...
Ask your vector RAG pipeline "what are the main themes in this corpus?" and watch it return three random chunks that share a keyword. Flat vector retrieval is built for "find me th...
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