DEV Community

# vectordatabase

Vector databases are purpose-built databases that are specialized to tackle the problems that arise when managing vector embeddings in production scenarios.

Posts

👋 Sign in for the ability to sort posts by relevant, latest, or top.
Why “Lost in the Middle” Breaks Most RAG Systems

Why “Lost in the Middle” Breaks Most RAG Systems

Comments
2 min read
Loaders, Splitters & Embeddings — How Bad Chunking Breaks Even Perfect RAG Systems

Loaders, Splitters & Embeddings — How Bad Chunking Breaks Even Perfect RAG Systems

Comments
3 min read
Efficient Vector Storage for AI: Why I Chose Pinecone with AWS

Efficient Vector Storage for AI: Why I Chose Pinecone with AWS

Comments
2 min read
Running AI on premises with Postgres

Running AI on premises with Postgres

Comments
7 min read
Azure AI Search at Scale: Building RAG Applications with Enhanced Vector Capacity

Azure AI Search at Scale: Building RAG Applications with Enhanced Vector Capacity

Comments
6 min read
S3 Vectors: 90% Cheaper Than Pinecone? Our Migration Guide

S3 Vectors: 90% Cheaper Than Pinecone? Our Migration Guide

Comments
7 min read
How to Choose the Right Vector Database for Enterprise AI

How to Choose the Right Vector Database for Enterprise AI

Comments
4 min read
Vector-Database: Qdrant-cluster on ECS-Fargate

Vector-Database: Qdrant-cluster on ECS-Fargate

Comments
4 min read
Choosing Rowstore or Columnstore? How to Pick the Right Engine for Your Workload

Choosing Rowstore or Columnstore? How to Pick the Right Engine for Your Workload

Comments
10 min read
Dense vs Sparse Vector Stores: Which One Should You Use — and When?

Dense vs Sparse Vector Stores: Which One Should You Use — and When?

Comments
2 min read
Introducing Embex: The Universal Vector Database ORM

Introducing Embex: The Universal Vector Database ORM

1
Comments
3 min read
I go by the name of Vector — Using AWS S3 vector storage for cost effective and performant…

I go by the name of Vector — Using AWS S3 vector storage for cost effective and performant…

Comments
7 min read
VectorDatabase Showdown 2025: Pinecone vs Qdrant vs Weaviate con Benchmarks Reales

VectorDatabase Showdown 2025: Pinecone vs Qdrant vs Weaviate con Benchmarks Reales

Comments
3 min read
Questionnaire Made Easy – with IRIS, FHIR SQL Builder, and Vector Search

Questionnaire Made Easy – with IRIS, FHIR SQL Builder, and Vector Search

Comments
4 min read
Optimizing Milvus Standalone for Production: Achieving 72% Memory Reduction While Maintaining Performance

Optimizing Milvus Standalone for Production: Achieving 72% Memory Reduction While Maintaining Performance

Comments
3 min read
The Serverless Semantic Engine: Architecting Mass Indexing Pipelines with Modal and Vector Databases

The Serverless Semantic Engine: Architecting Mass Indexing Pipelines with Modal and Vector Databases

Comments
17 min read
Enterprise RAG Architecture: A Complete Technical Guide by AgenixHub

Enterprise RAG Architecture: A Complete Technical Guide by AgenixHub

Comments
2 min read
Oracle 23ai's Phantom Vector Memory: A Troubleshooting Guide

Oracle 23ai's Phantom Vector Memory: A Troubleshooting Guide

Comments
11 min read
IVFFlat Indexing in pgvector

IVFFlat Indexing in pgvector

Comments
3 min read
Introducing Supabase ETL

Introducing Supabase ETL

6
Comments
4 min read
Dense vs Sparse Retrieval: Mastering FAISS, BM25, and Hybrid Search

Dense vs Sparse Retrieval: Mastering FAISS, BM25, and Hybrid Search

Comments
15 min read
Demystifying Retrieval-Augmented Generation (RAG)

Demystifying Retrieval-Augmented Generation (RAG)

1
Comments
4 min read
Vector Dimensions, Cosine Similarity, Dot Product — and Why Your Distance Metric Silently Ruins Relevance

Vector Dimensions, Cosine Similarity, Dot Product — and Why Your Distance Metric Silently Ruins Relevance

Comments
2 min read
Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Comments
4 min read
Introducing Vector Buckets

Introducing Vector Buckets

16
Comments 1
6 min read
loading...