Social Media Ideas for Vector-Database

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...

S3 Vectors: How to Build a RAG Without a Vector Database

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...

Best Open-Source Vector Databases In 2026: Top Options Compared

A vector database open source stack gives teams more control over retrieval infrastructure for RAG, semantic search, recommendation, and AI agents. The best open-source vector data...

Vector Databases Are Not Magic, Here's What's Actually Happening Under the Hood

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...

Why Data Engineers Need to Learn Vector Databases

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 »

What is Vector Search?

Vector search is a search technique that finds results based on meaning, not just...

How I Optimized PostgreSQL for Vector Embeddings and Cut Query Latency by 94%

Stop treating pgvector like a regular index. Here’s the production playbook nobody talks about.Continue reading on Medium »

Best ChromaDB Alternatives For RAG And Vector Search

ChromaDB alternatives help teams move from a lightweight vector store to a retrieval stack that matches production RAG, semantic search, metadata filtering, hybrid search, multi-te...

Easiest way to understand Vector Embeddings and Vector Search

Why Searching 1 Million Arrays of 1536 Numbers Does NOT Melt Your CPUContinue reading on Medium »

Running pgvector in production on Amazon Aurora PostgreSQL

Running pgvector on Amazon Aurora PostgreSQL gives you a production-grade vector store on a database you already know, backed by the operational tooling, high availability, and sca...

Building High‑Precision Vector Search for Document Retrieval on Databricks

For years, search technology meant one thing: type in a keyword, and the system goes hunting for an exact match. That works fine for product SKUs or error codes, but it falls apart...

Встраиваемая векторная БД для RAG на .NET 8: когда внешние сервисы избыточны

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...

A Coding Guide to Implement a pgvector-Powered Semantic, Hybrid, Sparse, and Quantized Vector Search System

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...

Is ChromaDB Silently Killing Your RAG Accuracy?

You’ve probably seen a dozen “vector database comparison” articles by now. Most of them compare managed cloud services on someone else’s…Continue reading on Medium »

Как я писал in-memory векторный движок на Go — и в каком месте он обогнал hnswilb

Полгода назад я начал писать in-memory базу с векторным поиском на Go: RESP-протокол, HNSW-индекс, WAL, многопоточность. Рассказываю, что из этого вышло: как я мерил производительн...

RAG Was Always a Temporary Workaround. What is Next?

Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases. T...

Алгоритмы векторного поиска: IVF и HNSW

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...

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

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...

VectorX Core EA

Meet Turbovec: A Rust Vector Index with Python Bindings, and Built on Google’s TurboQuant Algorithm

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...

Pocket DB – embedded single-file NoSQL for Node.js, MongoDB-style API, zero native deps

Comments

Benchmarking Vortex File Format ... vs Parquet, CSV ... vs DuckDB, Polars, Datafusion.

just because we want to make em' mad

How to Build a Production-Ready RAG Pipeline With Vector DBs

The adoption of retrieval-augmented generation (RAG) from research papers to production systems has been rapid. Those who tried it in 2023 are now deploying it at scale for enterpr...