Forem

# 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.
🚀 Build Your Own AI Assistant with Node.js: My Roadmap and Journey 🌟
Cover image for 🚀 Build Your Own AI Assistant with Node.js: My Roadmap and Journey 🌟

🚀 Build Your Own AI Assistant with Node.js: My Roadmap and Journey 🌟

3
Comments 2
4 min read
VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval
Cover image for VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval

VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval

5
Comments
1 min read
Building a smarter Web scraper: Vector embeddings for intelligent content retrieval
Cover image for Building a smarter Web scraper: Vector embeddings for intelligent content retrieval

Building a smarter Web scraper: Vector embeddings for intelligent content retrieval

1
Comments
3 min read
Recent Trends in Distributed Warehousing and Data Mining

Recent Trends in Distributed Warehousing and Data Mining

1
Comments
2 min read
Meetup: Vector databases, RAG, BM25, sparse searches, AI model training
Cover image for Meetup: Vector databases, RAG, BM25, sparse searches, AI model training

Meetup: Vector databases, RAG, BM25, sparse searches, AI model training

Comments 1
2 min read
Vector Search in Action: Personalization with AI Embeddings
Cover image for Vector Search in Action: Personalization with AI Embeddings

Vector Search in Action: Personalization with AI Embeddings

1
Comments
5 min read
Why PostgreSQL Might Be All the Backend You Need: Forget the Kitchen Sink

Why PostgreSQL Might Be All the Backend You Need: Forget the Kitchen Sink

6
Comments
4 min read
Beyond Basic Practice: Creating the JobSage AI Interview Simulator with Gemini & Embeddings

Beyond Basic Practice: Creating the JobSage AI Interview Simulator with Gemini & Embeddings

Comments
5 min read
Entendiendo los Embeddings en Inteligencia Artificial

Entendiendo los Embeddings en Inteligencia Artificial

Comments
3 min read
Smarter RAG Systems with Graphs
Cover image for Smarter RAG Systems with Graphs

Smarter RAG Systems with Graphs

43
Comments
4 min read
Postgres vs. Qdrant: Why Postgres Wins for AI and Vector Workloads
Cover image for Postgres vs. Qdrant: Why Postgres Wins for AI and Vector Workloads

Postgres vs. Qdrant: Why Postgres Wins for AI and Vector Workloads

1
Comments
4 min read
Vector Databases: their utility and functioning (RAG usage)

Vector Databases: their utility and functioning (RAG usage)

2
Comments
12 min read
Improve Your Python Search Relevancy with Astra DB Hybrid Search
Cover image for Improve Your Python Search Relevancy with Astra DB Hybrid Search

Improve Your Python Search Relevancy with Astra DB Hybrid Search

1
Comments
11 min read
Build Code-RAGent, an agent for your codebase
Cover image for Build Code-RAGent, an agent for your codebase

Build Code-RAGent, an agent for your codebase

7
Comments
5 min read
Semantic Search with Spring Boot & Redis
Cover image for Semantic Search with Spring Boot & Redis

Semantic Search with Spring Boot & Redis

2
Comments
10 min read
👋 Sign in for the ability to sort posts by relevant, latest, or top.