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* Updated Articles Pages * fixes * Links removed * Fixed url * light theme fix * toc fix * new article categories * categorize articles * fix article preview images * rename categories --------- Co-authored-by: trean <trean.mi@gmail.com> Co-authored-by: generall <andrey@vasnetsov.com>
100 lines
5.3 KiB
Markdown
100 lines
5.3 KiB
Markdown
---
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title: Home
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weight: 2
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hideTOC: true
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breadcrumb: false
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content:
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- partial: "documentation/banners/banner-a"
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title: Qdrant Documentation
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description: Qdrant is an AI-native vector database and a semantic search engine. You can use it to extract meaningful information from unstructured data.
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linkDescription: <a href="https://github.com/qdrant/qdrant_demo/" target="_blank">Clone this repo now</a> and build a search engine in five minutes.
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cloudButton:
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text: Cloud Quickstart
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url: /documentation/quickstart-cloud/
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localButton:
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text: Local Quickstart
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url: /documentation/quickstart/
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contained: true
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- partial: documentation/banners/banner-d
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developingTitle: Ready to start developing?
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developingDescription: Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our <a href="https://qdrant.to/cloud" target="_blank">free tier</a> on Qdrant Cloud. It scales easily and provides a UI where you can interact with data.
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developingBlock:
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title: Create your first Qdrant Cloud cluster today
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button:
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text: Get Started
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url: https://qdrant.to/cloud
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image:
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src: /img/rocket.svg
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alt: Rocket
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- partial: documentation/sections/cards-section
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title: Optimize Qdrant's performance
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description: Boost search speed, reduce latency, and improve the accuracy and memory usage of your Qdrant deployment.
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button:
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text: Learn More
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url: /documentation/guides/optimize/
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cardsPartial: documentation/cards/docs-cards
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cards:
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- id: 1
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tag: Documents
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icon:
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src: /icons/outline/documentation-blue.svg
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alt: Documents
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title: Distributed Deployment
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description: Scale Qdrant beyond a single node and optimize for high availability, fault tolerance, and billion-scale performance.
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link:
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url: /documentation/guides/distributed_deployment/
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text: Read More
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- id: 2
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tag: Documents
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icon:
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src: /icons/outline/documentation-blue.svg
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alt: Documents
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title: Multitenancy
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description: Build vector search apps that serve millions of users. Learn about data isolation, security, and performance tuning.
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link:
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url: /documentation/guides/multiple-partitions/
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text: Read More
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- id: 3
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tag: Blog
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tagColor: violet
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icon:
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src: /icons/outline/blog-purple.svg
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alt: Blog
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title: Vector Quantization
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description: Learn about cutting-edge techniques for vector quantization and how they can be used to improve search performance.
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link:
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url: /articles/what-is-vector-quantization/
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text: Read More
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partition: qdrant
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---
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THIS CONTENT IS GOING TO BE IGNORED FOR NOW
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# Documentation
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Qdrant is an AI-native vector database and a semantic search engine. You can use it to extract meaningful information from unstructured data. Want to see how it works? [Clone this repo now](https://github.com/qdrant/qdrant_demo/) and build a search engine in five minutes.
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|-:|:-|
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|[Cloud Quickstart](/documentation/quickstart-cloud/)|[Local Quickstart](/documentation/quick-start/)|
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## Ready to start developing?
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***<p style="text-align: center;">Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our [free tier](https://qdrant.to/cloud) on Qdrant Cloud. It scales easily and provides an UI where you can interact with data.</p>***
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[](https://qdrant.to/cloud)
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## Qdrant's most popular features:
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|:-|:-|:-|
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|[Filtrable HNSW](/documentation/filtering/) </br> Single-stage payload filtering | [Recommendations & Context Search](/documentation/concepts/explore/#explore-the-data) </br> Exploratory advanced search| [Pure-Vector Hybrid Search](/documentation/hybrid-queries/)</br>Full text and semantic search in one|
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|[Multitenancy](/documentation/guides/multiple-partitions/) </br> Payload-based partitioning|[Custom Sharding](/documentation/guides/distributed_deployment/#sharding) </br> For data isolation and distribution|[Role Based Access Control](/documentation/guides/security/?q=jwt#granular-access-control-with-jwt)</br>Secure JWT-based access |
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|[Quantization](/documentation/guides/quantization/) </br> Compress data for drastic speedups|[Multivector Support](/documentation/concepts/vectors/?q=multivect#multivectors) </br> For ColBERT late interaction |[Built-in IDF](/documentation/concepts/indexing/?q=inverse+docu#idf-modifier) </br> Advanced similarity calculation|
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## Developer guidebooks:
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| [A Complete Guide to Filtering in Vector Search](/articles/vector-search-filtering/) </br> Beginner & advanced examples showing how to improve precision in vector search.| [Building Hybrid Search with Query API](/articles/hybrid-search/) </br> Build a pure vector-based hybrid search system with our new fusion feature.|
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|----------------------------------------------|-------------------------------|
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| [Multitenancy and Sharding: Best Practices](/articles/multitenancy/) </br> Combine two powerful features for complete data isolation and scaling.| [Benefits of Binary Quantization in Vector Search](/articles/binary-quantization/) </br> Compress data points while retaining essential meaning for extreme search performance.|
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