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Dive into next-gen search capabilities with Qdrant, offering a smarter way to deliver precise and tailored content to users, enhancing interaction accuracy and depth.
Search with Qdrant
Qdrant enhances search, offering semantic, similarity, multimodal, and hybrid search capabilities for accurate, user-centric results, serving applications in different industries like e-commerce to healthcare.
Semantic Search
Qdrant optimizes similarity search, identifying the closest database items to any query vector for applications like recommendation systems, RAG and image retrieval, enhancing accuracy and user experience.
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Hybrid Search for Text
By combining dense vector embeddings with sparse vectors e.g. BM25, Qdrant powers semantic search to deliver context-aware results, transcending traditional keyword search by understanding the deeper meaning of data.
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Multimodal Search
Qdrant's capability extends to multi-modal search, indexing and retrieving various data forms (text, images, audio) once vectorized, facilitating a comprehensive search experience.
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Single Stage filtering That Works
Qdrant enhances search speeds and control and context understanding through filtering on any nested entry in our payload. Unique architecture allows Qdrant to avoid expensive pre-filtering and post-filtering stages, making search faster and accurate.
Learn MoreLearn how to get started with Qdrant for your search use case
Startup Semantic Search Demo
The demo showcases semantic search for startup descriptions through SentenceTransformer and Qdrant, comparing neural search's accuracy with traditional searches for better content discovery.
Multimodal Semantic Search with FastEmbed
This tutorial shows you how to run a proper multimodal semantic search system with a few lines of code, without the need to annotate the data or train your networks.
Create a Simple Neural Search Service
This tutorial shows you how to build and deploy your own neural search service.
Image Classification with Qdrant Vector Semantic Search
In this tutorial, you will learn how a semantic search engine for images can help diagnose different types of skin conditions.
Create a Hybrid Search Service with FastEmbed
This tutorial guides you through building and deploying your own hybrid search service using FastEmbed.
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Turn embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and more.
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