🚀 Medial Secures Investment on Shark Tank India - Fueling the Future of Professional Social Networking. 🔥

News

Messages

Try our Valuation Calculator →

Back

Rahul Agarwal

Founder | Agentic AI... • 26d

AI apps don’t search text. They search meaning. If you work with LLMs, you’re already relying on vector databases whether you realize it or not. Here’s what’s really happening behind the scenes. First, you organize data into collections. Think of them like smart containers for related content. Each entry holds attributes like titles, tags, dates, or categories. Next comes structure. Every collection has rules. Data types. Index logic. Vector settings. This is where integrations like embeddings and compression get wired in. Now the interesting part starts. When someone sends a query, the text is not matched. It’s transformed. An embedding model converts words into numbers that represent intent and context, not keywords. Those numbers get stored as vectors. Each one has an ID. Each one represents meaning. To scale, the system splits data into shards. Indexes map relationships between vectors. This makes similarity search fast instead of brute force. When a new request arrives, it’s vectorized again. Then compared using math like cosine distance. Closest matches rise to the top. Finally, results are returned. Not because they look similar. Because they think similar. In simple terms: Vector databases don’t store text. They store understanding. That’s why modern AI can retrieve ideas, not just strings.

Recommendations from Medial

Gigaversity

Gigaversity.in • 9m

We built an e-commerce platform that worked well initially. But as the product catalog grew, users started facing issues—search results were slow and often not relevant. This led to frustration and a drop in engagement. To solve this, we upgraded th

Kimiko

Startups | AI | info... • 9m

Vector databases for AI memory just got disrupted… by MP4 files?! Video as Database: Store millions of text chunks in a single MP4 file Store millions of text chunks with blazing-fast semantic search — no database required. 100% open source. Zero

2 Replies

Sanskar

Keen Learner and Exp... • 6m

Day 2 of learning mathematics for AI/ML as a no math person. Topic: vectors and matrices. We use NumPy python library for these. I got introduced to the concept of vectors and matrices. Vectors are like lists and are divided Vectors are divided in

Sanskar

Keen Learner and Exp... • 6m

Rahul Agarwal

Founder | Agentic AI... • 2m

Most people don't even know these basics of RAG. I've explained it in a simple way below. 1. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 Convert documents into a format that AI can quickly search later. Step-by-step: • 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁: You start with files like PDFs, Word

4 Replies

Rahul Agarwal

Founder | Agentic AI... • 3m

Steps to building AI systems with LLM's. I've given a simple detailed explanation below. 𝗦𝘁𝗲𝗽 1 – 𝗟𝗟𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀) • These are the 𝗯𝗿𝗮𝗶𝗻𝘀 of the system. • Examples: GPT (OpenAI), Gemini, Claude etc. • Th

Rahul Agarwal

Founder | Agentic AI... • 1m

Most people ignore how AI actually searches. I've explained in a simple way below. 1: 𝗘𝗻𝗰𝗼𝗱𝗲𝗿 (𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝘁𝗲𝘅𝘁 𝗶𝗻𝘁𝗼 𝗻𝘂𝗺𝗯𝗲𝗿𝘀) Computers don’t understand text like humans do. So the first job is to 𝗰𝗼𝗻𝘃𝗲𝗿𝘁 𝘁𝗲𝘅𝘁 �

Rahul Agarwal

Founder | Agentic AI... • 2m

Most people building AI systems miss these crucial steps. I've explained the architecture in simple way below. 𝗦𝘁𝗲𝗽 1 – 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (𝗜𝗻𝗴𝗲𝘀𝘁 𝗟𝗮𝘆𝗲𝗿) • This step brings data into your AI system. •

1 Reply

Mickey shelby

An entrepreneur • 7m

An index is an organized list or reference system that helps locate information quickly. It can appear in books, databases, or websites, showing topics and page numbers. In finance, an index measures market trends. In programming, an index indicates

Lingeshwaran

Looking at objective... • 1y

IDEA : AI Training Data generator A Developer Platform which is much specify for AI-ML field to achieve various tasks like Analysis, Generation and Segmentation of real time data across data like text, image, audio, video that requires heavy and hig

6 Replies