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# vectordatabase

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

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Couchbase Weekly Updates - June 27, 2025

Couchbase Weekly Updates - June 27, 2025

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2 min read
SQL vs NoSQL? Why Not Both?

SQL vs NoSQL? Why Not Both?

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2 min read
Local Elasticsearch Playground: A Practical Introduction and hands-on test (and moving to a RAG solution)

Local Elasticsearch Playground: A Practical Introduction and hands-on test (and moving to a RAG solution)

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12 min read
A Complete Guide to Retrieval-Augmented Generation

A Complete Guide to Retrieval-Augmented Generation

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8 min read
Building AI Agents: From Zero to Hero

Building AI Agents: From Zero to Hero

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11 min read
Cosine Similarity in Vector Databases: Why It Matters for GenAI & RAG Systems

Cosine Similarity in Vector Databases: Why It Matters for GenAI & RAG Systems

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2 min read
Quick Apply: Building A Conversational Job Application Web App

Quick Apply: Building A Conversational Job Application Web App

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4 min read
Growing the Tree: Multi-Agent LLMs Meet RAG, Vector Search, and Goal-Oriented Thinking - Part 2

Growing the Tree: Multi-Agent LLMs Meet RAG, Vector Search, and Goal-Oriented Thinking - Part 2

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

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

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4 min read
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

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

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

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3 min read
Recent Trends in Distributed Warehousing and Data Mining

Recent Trends in Distributed Warehousing and Data Mining

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

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

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2 min read
Vector Search in Action: Personalization with AI Embeddings

Vector Search in Action: Personalization with AI Embeddings

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5 min read
📺 Find the Anime: A Semantic AI Anime Search Tool

📺 Find the Anime: A Semantic AI Anime Search Tool

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4 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

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4 min read
Processing data with “Data Prep Kit” (part 2)

Processing data with “Data Prep Kit” (part 2)

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8 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

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5 min read
Entendiendo los Embeddings en Inteligencia Artificial

Entendiendo los Embeddings en Inteligencia Artificial

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3 min read
Smarter RAG Systems with Graphs

Smarter RAG Systems with Graphs

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4 min read
Vector Database Indexing: A Comprehensive Guide

Vector Database Indexing: A Comprehensive Guide

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

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

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

Vector Databases: their utility and functioning (RAG usage)

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12 min read
Improve Your Python Search Relevancy with Astra DB Hybrid Search

Improve Your Python Search Relevancy with Astra DB Hybrid Search

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11 min read
Build Code-RAGent, an agent for your codebase

Build Code-RAGent, an agent for your codebase

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5 min read
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