Tag feed

#qdrant

107 posts2 followers

Explore Hashnode

Alternatives

Trending tags this week

SSsatyam sahuthebuildersnotebook.hashnode.dev1d ago · 30 min read

Building an Offline “Life Memorizer” with Gemini 2.0 & Qdrant Edge

Originally published on Medium. Reposted here for the Hashnode community. Consider this. You are trying to find where you left an item, like your wallet or keys, inside your own house. You know you s

0

MPMohana Priya Rmona23.hashnode.dev2d ago · 2 min read

Why I chose Qdrant over ChromaDB in my RAG System

When I started building Nexus — my multi-source RAG system — one of the first real decisions I had to make was picking a vector database. It sounds like a small choice. It isn't. First, why do you eve

0

LSLakhan Samaniblog.authorizer.devJun 16 · 11 min read

Your AI assistant can leak files people aren't allowed to see — here's how to fix it

TL;DR Vector search ignores your permission model: an engineer can ask your RAG assistant about board-level financials and get them, because semantically close = retrieved, and a restricted chunk in

6

JZSRI

TSThirumurthi Sthirumurthi.hashnode.devJun 1 · 9 min read

RAG system in Spring AI and Langflow using Qdrant Vector Storage

The motivation of the article is use AI with my financial statements as context and ask questions around those. In order to build everything locally had to use RAG approach. The statements in this cas

0

MSMD SAMIUL ISLAMsamiul-codes.hashnode.devMay 23 · 13 min read

I Tried Building an AI SaaS Strategist in Node.js Using YouTube Transcripts

Recently, I’ve been exploring a lot about RAG systems and local LLMs, not from a research perspective, but more from a curiosity perspective as a developer who genuinely enjoys building things and und

0

TPTushar Patilblog.techtush.inMay 15 · 16 min read

🔍 MCP + RAG: Building a Knowledge-Base Tool with Vector Search in TypeScript

This is Part 10 of the AI Engineering with TypeScript series. Prerequisites: Part 2 — MCP Fundamentals · Part 3 — AI Agent · Part 5 — Production MCP Server Stack: Node.js 20+ · TypeScript 5.x · OpenAI

0

JKJangwook Kimeffloow.hashnode.devApr 17 · 12 min read

Vector Database Comparison 2026: Qdrant vs Pinecone vs Chroma

RAG now drives 51% of enterprise AI implementations — up from 31% just a year ago. Behind every one of those pipelines is a vector database making millisecond-level similarity decisions across millions or billions of embeddings. Choosing the wrong on...

0

APAbhishek Pathakpathak404.hashnode.devApr 14 · 17 min read

From "We Should Fix This" to Actual Code: Building a Meeting-to-Execution Pipeline

📂 Explore the Source If you want to dive straight into the implementation, you can explore the repositories below: Srot Core (github.com/pathak404/srot-core): The central intelligence pipeline, Fast

0

EBElisé Baraka M.ebmurha.hashnode.devApr 7 · 14 min read

Building a RAG Engine — Part 1: Retrieval Foundation

I started building a RAG engine because I was tired of asking large language models questions limited by whatever context I could paste or attach into a chat window. I was looking for information arou

0

JJahanzaibjahanzaibai.hashnode.devApr 7 · 17 min read

Vector Databases for AI Agents: Which One Actually Works in Production?

A client came to me in early 2024 with a fully built AI agent that kept timing out in production. The retrieval step took 800ms on average and occasionally spiked past two seconds. Their users were abandoning queries. The "AI is too slow" complaint w...

0

#qdrant

Search Hashnode

#qdrant

Explore Hashnode

Trending tags this week

Building an Offline “Life Memorizer” with Gemini 2.0 & Qdrant Edge

Why I chose Qdrant over ChromaDB in my RAG System

Your AI assistant can leak files people aren't allowed to see — here's how to fix it

RAG system in Spring AI and Langflow using Qdrant Vector Storage

I Tried Building an AI SaaS Strategist in Node.js Using YouTube Transcripts

🔍 MCP + RAG: Building a Knowledge-Base Tool with Vector Search in TypeScript

Vector Database Comparison 2026: Qdrant vs Pinecone vs Chroma

From "We Should Fix This" to Actual Code: Building a Meeting-to-Execution Pipeline

Building a RAG Engine — Part 1: Retrieval Foundation

Vector Databases for AI Agents: Which One Actually Works in Production?