25 июл. 2024 г. · Our new Query API allows you to build a hybrid search system that uses different search methods to improve search quality & experience.

This tutorial shows you how to build and deploy your own hybrid search service to look through descriptions of companies from startups-list.com and pick the ... Prepare sample dataset · Run Qdrant in Docker

Qdrant supports hybrid search by combining search results from sparse and dense vectors. dense vectors are the ones you have probably already ...

Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with ... Hybrid Search · Multi-stage queries · Re-scoring examples

Продолжительность: 1:01:18
Опубликовано: 19 июл. 2024 г.

This repository contains the materials for the hands-on webinar "How to Build the Ultimate Hybrid Search with Qdrant". It is a step-by-step guide on how to ...

9 дек. 2023 г. · Let's see how you can make a hybrid search query in Qdrant. First, you need to create a collection with both dense and sparse vectors ...

1 июл. 2024 г. · The current state-of-the-art approach to retrieval nowadays tries to incorporate BM25 along with embeddings into a hybrid search system.

21 авг. 2024 г. · To achieve similar functionality in Qdrant: Custom Hybrid Search, perform vector and keyword searches separately and then combine results manually.

Qdrant recently released a new lightweight approach to sparse embeddings, BM42. In this notebook, we walk through how to use BM42 with llama-index, for ...