The Vector Database Lie
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...
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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...
A frank comparison from an engineering standpoint — architecture trade-offs, honest benchmarks, real pricing math, and Java client examples for both. The “which vector database sho...
A practitioner’s comparison of pgvector, Pinecone, Weaviate, Qdrant, Milvus, and 10 other vector databases.Continue reading on Medium »
Vector databases are now core retrieval infrastructure for RAG and agentic AI. This guide compares nine production options on architecture, pricing, and scale. The post Best Vector...
Everyone working in AI reaches a moment where they search a document and get back something that looks right but means nothing — or searches for a concept and gets back noise. That...
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...
The Meeting That Triggered This Article A few months ago, I sat in a room as a team pitched a $5,000/month vector database subscription. Their use case: storing roughly 100,000 pro...
To recall, Integrating our private documents with LLM is called RAG. Lets assume that, we have some pdfs containing our data. That data in the pdf will be broken down into chunk...
Choosing the right distance is not a mathematical detail. Sometimes, it is the whole product.Continue reading on Data Science Collective В»
The problem I’ve been creating and serving web-based maps such as this one for some time. That’s based on raster tiles, and an osm2pgsql database is used to store the data that t...
In this post, we share Ring’s billion-scale semantic video search on Amazon RDS for PostgreSQL with pgvector architectural decisions vs alternatives, cost-performance-scale challen...
pgvector is a Postgres extension that adds vector storage and similarity search to an existing database, so you can run semantic queries directly against your application data with...
Stop treating pgvector like a regular index. Here’s the production playbook nobody talks about.Continue reading on Medium »
The era of large language models (LLMs) is here, bringing with it rapidly evolving libraries like ChromaDB that help augment LLM applications. You’ve most likely heard of chatbots...
В программировании частая задача это работа с последовательными элементами. В этой, порой непростой задаче, нам часто помогают вектора. Вектора бывают самыми разными от queue и set...
Если вы делаете RAG (Retrieval-Augmented Generation) на .NET, то рано или поздно упираетесь в вопрос: куда складывать эмбеддинги и как быстро искать по ним.Существующие варианты де...
I see developers trying to build “AI Chatbots” that know about their specific company data. They want…
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...
Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search. The post I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian appeare...
In this post, you’ll learn how to query Amazon S3 Vectors from Amazon Aurora PostgreSQL-Compatible Edition using standard SQL, and how to combine vector similarity results with rel...
Here is the uncomfortable truth: most teams shipping “RAG-powered” features today are over-engineering their stack.Continue reading on Medium »
The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymo...
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...
turbovec brings Google Research's TurboQuant algorithm to vector search, offering 16x compression and zero codebook training for RAG pipelines. The post Meet Turbovec: A Rust Vecto...
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