Advanced Search

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.

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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.

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Learn 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.

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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.

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