Tag Qdrant

All the articles tagged Qdrant

7 articles found

Hybrid search in 2026 for AI systems that truly hold up

Use hybrid search if you want to stop getting almost-right results

Signs you need hybrid search: unstable ranking, ignored tokens, imprecise results. Solutions across keyword search, embedding and re-ranking.

Semantic Kernel framework for connecting AI and data

The Semantic Kernel is the director that orchestrates patterns and information

Semantic Kernel is the Microsoft framework that allows you to orchestrate AI models, embeddings and vector databases in a logical and scalable flow.

RAG Semantic Kernel RAG

AI memory: how to give long-lasting memory to models

AI memory is the way to turn a model into a system that actually learns

A model has limited memory, find out how you can build persistent memory with RAG and make the AI more powerful.

How vector indexing works and why it is essential

Vector indexing is the trick that makes research in artificial intelligence immediate

Vector indexing is the mechanism that allows Qdrant and other vector databases to respond quickly even with millions of saved data.

RAG Vector indexing Qdrant

RAG pipeline how to build an effective workflow

RAG pipeline: the path that connects documents to artificial intelligence responses

The RAG pipeline connects business documents to AI responses, ensuring a stable, reliable and truly useful flow.

Qdrant is the vector database that gives your AI memory

Qdrant vector database the heart of semantic search and RAG

Qdrant is a vector database that stores and searches embeddings in a fast and scalable way, giving real memory to artificial intelligence systems

RAG: eliminate hallucinations from AI models

RAG in software development: reliable AI answers without hallucinations

RAG connects real data and AI models to obtain reliable answers in software development, reducing errors and hallucinations