lukawskikacper/qdrant-landing-page-2024-06-21 huggingface dataset:
qdrant-landing/archetypes/blog-post.md

---
title: "{{ replace .Name "-" " " | title }}"
draft: false
slug: {{ .Name }} # Change this slug to your page slug if needed
short_description:  This is a blog post # Change this
description: This is a blog post # Change this
preview_image: /blog/Article-Image.png # Change this

# social_preview_image: /blog/Article-Image.png # Optional image used for link previews
# title_preview_image: /blog/Article-Image.png # Optional image used for blog post title
# small_preview_image: /blog/Article-Image.png # Optional image used for small preview in the list of blog posts

date: {{ .Date }}
author: John Doe # Change this
featured: false # if true, this post will be featured on the blog page
tags: # Change this, related by tags posts will be shown on the blog page
  - news
  - blog
weight: 0 # Change this weight to change order of posts
# For more guidance, see https://github.com/qdrant/landing_page?tab=readme-ov-file#blog
---

Here is your blog post content. You can use markdown syntax here.

# Header 1
## Header 2
### Header 3
#### Header 4
##### Header 5
###### Header 6

<aside role="alert">
  You can add a note to your page using this aside block.
</aside>

<aside role="status">
  This is a warning message.
</aside>

> This is a blockquote following a header.

Table:

| Header 1 | Header 2 | Header 3 | Header 4 |
| -------- | -------- | -------- | -------- |
| Cell 1   | Cell 2   | Cell 3   | Cell 4   |
| Cell 3   | Cell 4   | Cell 5   | Cell 6   |

- List item 1
    - Nested list item 1
    - Nested list item 2
- List item 2
- List item 3

1. Numbered list item 1
    1. Nested numbered list item 1
    2. Nested numbered list item 2
2. Numbered list item 2
3. Numbered list item 3
qdrant-landing/content/advanced-search/advanced-search-features.md

---
title: Search with Qdrant
description: 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.
features:
- id: 0
  icon: 
    src: /icons/outline/similarity-blue.svg
    alt: Similarity
  title: Semantic Search
  description: 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.
  link:
    text: Learn More
    url: /documentation/concepts/search/
- id: 1
  icon:
    src: /icons/outline/search-text-blue.svg
    alt: Search text
  title: Hybrid Search for Text
  description: 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.
  link:
    text: Learn More
    url: /documentation/tutorials/hybrid-search-fastembed/
- id: 2
  icon:
    src: /icons/outline/selection-blue.svg
    alt: Selection
  title: Multimodal Search
  description: Qdrant's capability extends to multi-modal search, indexing and retrieving various data forms (text, images, audio) once vectorized, facilitating a comprehensive search experience.
  link:
    text: View Tutorial
    url: /documentation/tutorials/aleph-alpha-search/
- id: 3
  icon:
    src: /icons/outline/filter-blue.svg
    alt: Filter
  title: Single Stage filtering that Works
  description: 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.
  link:
    text: Learn More
    url: /articles/filtrable-hnsw/
sitemapExclude: true
---
qdrant-landing/content/advanced-search/advanced-search-hero.md

---
title: Advanced Search
description: 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.
startFree:
  text: Get Started
  url: https://cloud.qdrant.io/
learnMore:
  text: Contact Us
  url: /contact-us/
image:
  src: /img/vectors/vector-0.svg
  alt: Advanced search
sitemapExclude: true
---
qdrant-landing/content/advanced-search/advanced-search-use-cases.md

---
title: Learn how to get started with Qdrant for your search use case
features:
- id: 0
  image:
    src: /img/advanced-search-use-cases/startup-semantic-search.svg
    alt: Startup Semantic Search
  title: Startup Semantic Search Demo
  description: The demo showcases semantic search for startup descriptions through SentenceTransformer and Qdrant, comparing neural search's accuracy with traditional searches for better content discovery.
  link:
    text: View Demo
    url: https://demo.qdrant.tech/
- id: 1
  image:
    src: /img/advanced-search-use-cases/multimodal-semantic-search.svg
    alt: Multimodal Semantic Search
  title: Multimodal Semantic Search with Aleph Alpha
  description: 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.
  link:
    text: View Tutorial
    url: /documentation/examples/aleph-alpha-search/
- id: 2
  image:
    src: /img/advanced-search-use-cases/simple-neural-search.svg
    alt: Simple Neural Search
  title: Create a Simple Neural Search Service
  description: This tutorial shows you how to build and deploy your own neural search service.
  link:
    text: View Tutorial
    url: /documentation/tutorials/neural-search/
- id: 3
  image:
    src: /img/advanced-search-use-cases/image-classification.svg
    alt: Image Classification
  title: Image Classification with Qdrant Vector Semantic Search
  description: In this tutorial, you will learn how a semantic search engine for images can help diagnose different types of skin conditions.
  link:
    text: View Tutorial
    url: https://www.youtube.com/watch?v=sNFmN16AM1o
- id: 4
  image:
    src: /img/advanced-search-use-cases/semantic-search-101.svg
    alt: Semantic Search 101
  title: Semantic Search 101
  description: Build a semantic search engine for science fiction books in 5 mins.
  link:
    text: View Tutorial
    url: /documentation/tutorials/search-beginners/
- id: 5
  image:
    src: /img/advanced-search-use-cases/hybrid-search-service-fastembed.svg
    alt: Create a Hybrid Search Service with Fastembed
  title: Create a Hybrid Search Service with Fastembed
  description: This tutorial guides you through building and deploying your own hybrid search service using Fastembed.
  link:
    text: View Tutorial
    url: /documentation/tutorials/hybrid-search-fastembed/
sitemapExclude: true
---
qdrant-landing/content/articles/qdrant-0-10-release.md

---
title: Qdrant 0.10 released
short_description: A short review of all the features introduced in Qdrant 0.10
description: Qdrant 0.10 brings a lot of changes. Check out what's new!
preview_dir: /articles_data/qdrant-0-10-release/preview
small_preview_image: /articles_data/qdrant-0-10-release/new-svgrepo-com.svg
social_preview_image: /articles_data/qdrant-0-10-release/preview/social_preview.jpg
weight: 70
author: Kacper Łukawski
author_link: https://medium.com/@lukawskikacper
date: 2022-09-19T13:30:00+02:00
draft: false
---

[Qdrant 0.10 is a new version](https://github.com/qdrant/qdrant/releases/tag/v0.10.0) that brings a lot of performance 
improvements, but also some new features which were heavily requested by our users. Here is an overview of what has changed.

## Storing multiple vectors per object

Previously, if you wanted to use semantic search with multiple vectors per object, you had to create separate collections 
for each vector type. This was even if the vectors shared some other attributes in the payload. With Qdrant 0.10, you can 
now store all of these vectors together in the same collection, which allows you to share a single copy of the payload. 
This makes it easier to use semantic search with multiple vector types, and reduces the amount of work you need to do to 
set up your collections.

## Batch vector search

Previously, you had to send multiple requests to the Qdrant API to perform multiple non-related tasks. However, this 
can cause significant network overhead and slow down the process, especially if you have a poor connection speed. 
Fortunately, the [new batch search feature](/documentation/concepts/search/#batch-search-api) allows 
you to avoid this issue. With just one API call, Qdrant will handle multiple search requests in the most efficient way 
possible. This means that you can perform multiple tasks simultaneously without having to worry about network overhead
or slow performance.

## Built-in ARM support

To make our application accessible to ARM users, we have compiled it specifically for that platform. If it is not 
compiled for ARM, the device will have to emulate it, which can slow down performance. To ensure the best possible 
experience for ARM users, we have created Docker images specifically for that platform. Keep in mind that using 
a limited set of processor instructions may affect the performance of your vector search. Therefore, we have tested 
both ARM and non-ARM architectures using similar setups to understand the potential impact on performance.

## Full-text filtering

Qdrant is a vector database that allows you to quickly search for the nearest neighbors. However, you may need to apply 
additional filters on top of the semantic search. Up until version 0.10, Qdrant only supported keyword filters. With the 
release of Qdrant 0.10, [you can now use full-text filters](/documentation/concepts/filtering/#full-text-match) 
as well. This new filter type can be used on its own or in combination with other filter types to provide even more 
flexibility in your searches.
qdrant-landing/content/articles/qdrant-0-11-release.md

---
title: Introducing Qdrant 0.11
short_description: Check out what's new in Qdrant 0.11
description: Replication support is the most important change introduced by Qdrant 0.11. Check out what else has been added!
preview_dir: /articles_data/qdrant-0-11-release/preview
small_preview_image: /articles_data/qdrant-0-11-release/announcement-svgrepo-com.svg
social_preview_image: /articles_data/qdrant-0-11-release/preview/social_preview.jpg
weight: 65
author: Kacper Łukawski
author_link: https://medium.com/@lukawskikacper
date: 2022-10-26T13:55:00+02:00
draft: false
---

We are excited to [announce the release of Qdrant v0.11](https://github.com/qdrant/qdrant/releases/tag/v0.11.0), 
which introduces a number of new features and improvements.

## Replication

One of the key features in this release is replication support, which allows Qdrant to provide a high availability 
setup with distributed deployment out of the box. This, combined with sharding, enables you to horizontally scale 
both the size of your collections and the throughput of your cluster. This means that you can use Qdrant to handle 
large amounts of data without sacrificing performance or reliability.

## Administration API

Another new feature is the administration API, which allows you to disable write operations to the service. This is 
useful in situations where search availability is more critical than updates, and can help prevent issues like memory 
usage watermarks from affecting your searches.

## Exact search

We have also added the ability to report indexed payload points in the info API, which allows you to verify that 
payload values were properly formatted for indexing. In addition, we have introduced a new `exact` search parameter 
that allows you to force exact searches of vectors, even if an ANN index is built. This can be useful for validating 
the accuracy of your HNSW configuration.

## Backward compatibility

This release is backward compatible with v0.10.5 storage in single node deployment, but unfortunately, distributed 
deployment is not compatible with previous versions due to the large number of changes required for the replica set 
implementation. However, clients are tested for backward compatibility with the v0.10.x service.
qdrant-landing/content/articles/templates/release-post-template.md

---
title: "Qdrant x.y.0 - <include headline> #required; update version and headline"
draft: true # Change to false to publish the article at /articles/
slug: qdrant-x.y.z # required; subtitute version number
short_description: "Headline-like description."
description: "Headline with more detail. Suggested limit: 140 characters. " 
# Follow instructions in https://github.com/qdrant/landing_page?tab=readme-ov-file#articles to create preview images
# social_preview_image: /articles_data/<slug>/social_preview.jpg # This image will be used in social media previews, should be 1200x600px. Required.
# small_preview_image: /articles_data/<slug>/icon.svg # This image will be used in the list of articles at the footer, should be 40x40px
# preview_dir: /articles_data/<slug>/preview # This directory contains images that will be used in the article preview. They can be generated from one image. Read more below. Required.
weight: 10 # This is the order of the article in the list of articles at the footer. The lower the number, the higher the article will be in the list. Negative numbers OK.
author: <name> # Author of the article. Required.
author_link: https://medium.com/@yusufsarigoz # Link to the author's page. Not required.
date: 2022-06-28T13:00:00+03:00 # Date of the article. Required. If the date is in the future it does not appear in the build
tags: # Keywords for SEO
  - vector databases comparative benchmark
  - benchmark
  - performance
  - latency
---

[Qdrant x.y.0 is out!]((https://github.com/qdrant/qdrant/releases/tag/vx.y.0).

Include headlines:

- **Headline 1:** Description
- **Headline 2:** Description
- **Headline 3:** Description

## Related to headline 1

Description

Highlights:

- **Detail 1:** Description
- **Detail 2:** Description
- **Detail 3:** Description

Include before / after information, ideally with graphs and/or numbers
Include links to documentation
Note limits, such as availability on Qdrant Cloud

## Minor improvements and new features

Beyond these enhancements, [Qdrant vx.y.0](https://github.com/qdrant/qdrant/releases/tag/vx.y.0) adds and improves on several smaller features:

1. 
1. 

## Release notes

For more information, see [our release notes](https://github.com/qdrant/qdrant/releases/tag/vx.y.0). 
Qdrant is an open source project. We welcome your contributions; raise [issues](https://github.com/qdrant/qdrant/issues), or contribute via [pull requests](https://github.com/qdrant/qdrant/pulls)!
qdrant-landing/content/benchmarks/_index.md

---
title: Vector Database Benchmarks
description: The first comparative benchmark and benchmarking framework for vector search engines and vector databases.
keywords:
  - vector databases comparative benchmark
  - ANN Benchmark
  - Qdrant vs Milvus
  - Qdrant vs Weaviate
  - Qdrant vs Redis
  - Qdrant vs ElasticSearch
  - benchmark
  - performance
  - latency
  - RPS
  - comparison
  - vector search
  - embedding
preview_image: /benchmarks/benchmark-1.png
seo_schema: { "@context": "https://schema.org", "@type": "Article", "headline": "Vector Search Comparative Benchmarks", "image": [ "https://qdrant.tech/benchmarks/benchmark-1.png" ], "abstract": "The first comparative benchmark and benchmarking framework for vector search engines", "datePublished": "2022-08-23", "dateModified": "2022-08-23", "author": [{ "@type": "Organization", "name": "Qdrant", "url": "https://qdrant.tech" }] }
 
---
qdrant-landing/content/benchmarks/benchmarks-intro.md

---
draft: false
id: 2
title: How vector search should be benchmarked?
weight: 1
---

# Benchmarking Vector Databases

At Qdrant, performance is the top-most priority. We always make sure that we use system resources efficiently so you get the **fastest and most accurate results at the cheapest cloud costs**. So all of our decisions from [choosing Rust](/articles/why-rust/), [io optimisations](/articles/io_uring/), [serverless support](/articles/serverless/), [binary quantization](/articles/binary-quantization/), to our [fastembed library](/articles/fastembed/) are all based on our principle. In this article, we will compare how Qdrant performs against the other vector search engines.

Here are the principles we followed while designing these benchmarks:

- We do comparative benchmarks, which means we focus on **relative numbers** rather than absolute numbers.
- We use affordable hardware, so that you can reproduce the results easily.
- We run benchmarks on the same exact machines to avoid any possible hardware bias.
- All the benchmarks are [open-sourced](https://github.com/qdrant/vector-db-benchmark), so you can contribute and improve them.

<details>
<summary> Scenarios we tested </summary>

1. Upload & Search benchmark on single node [Benchmark](/benchmarks/single-node-speed-benchmark/)
2. Filtered search benchmark - [Benchmark](/benchmarks/#filtered-search-benchmark)
3. Memory consumption benchmark - Coming soon
4. Cluster mode benchmark - Coming soon

</details>

</br>

Some of our experiment design decisions are described in the [F.A.Q Section](/benchmarks/#benchmarks-faq).
Reach out to us on our [Discord channel](https://qdrant.to/discord) if you want to discuss anything related Qdrant or these benchmarks.
qdrant-landing/content/benchmarks/filtered-search-benchmark.md

---
draft: false
id: 5
title:
description: '<b> Updated: Feb 2023 </b>'

filter_data: /benchmarks/filter-result-2023-02-03.json
date: 2023-02-13
weight: 4
---


## Filtered Results

As you can see from the charts, there are three main patterns:

- **Speed boost** - for some engines/queries, the filtered search is faster than the unfiltered one. It might happen if the filter is restrictive enough, to completely avoid the usage of the vector index.

- **Speed downturn** - some engines struggle to keep high RPS, it might be related to the requirement of building a filtering mask for the dataset, as described above.

- **Accuracy collapse** - some engines are loosing accuracy dramatically under some filters. It is related to the fact that the HNSW graph becomes disconnected, and the search becomes unreliable.

Qdrant avoids all these problems and also benefits from the speed boost, as it implements an advanced [query planning strategy](/documentation/search/#query-planning).

<aside role="status">The Filtering Benchmark is all about changes in performance between filter and un-filtered queries. Please refer to the search benchmark for absolute speed comparison.</aside>
qdrant-landing/content/benchmarks/filtered-search-intro.md

---
draft: false
id: 4
title: Filtered search benchmark
description:

date: 2023-02-13
weight: 3
---

# Filtered search benchmark

Applying filters to search results brings a whole new level of complexity.
It is no longer enough to apply one algorithm to plain data. With filtering, it becomes a matter of the _cross-integration_ of the different indices.

To measure how well different search engines perform in this scenario, we have prepared a set of **Filtered ANN Benchmark Datasets** -
 https://github.com/qdrant/ann-filtering-benchmark-datasets


It is similar to the ones used in the [ann-benchmarks project](https://github.com/erikbern/ann-benchmarks/) but enriched with payload metadata and pre-generated filtering requests. It includes synthetic and real-world datasets with various filters, from keywords to geo-spatial queries.

### Why filtering is not trivial?

Not many ANN algorithms are compatible with filtering.
HNSW is one of the few of them, but search engines approach its integration in different ways:

- Some use **post-filtering**, which applies filters after ANN search. It doesn't scale well as it either loses results or requires many candidates on the first stage.
- Others use **pre-filtering**, which requires a binary mask of the whole dataset to be passed into the ANN algorithm. It is also not scalable, as the mask size grows linearly with the dataset size.

On top of it, there is also a problem with search accuracy.
It appears if too many vectors are filtered out, so the HNSW graph becomes disconnected.

Qdrant uses a different approach, not requiring pre- or post-filtering while addressing the accuracy problem.
Read more about the Qdrant approach in our [Filtrable HNSW](/articles/filtrable-hnsw/) article.
qdrant-landing/content/benchmarks/single-node-speed-benchmark-2022.md

---
draft: false
id: 1
title: Single node benchmarks (2022)
single_node_title: Single node benchmarks
single_node_data: /benchmarks/result-2022-08-10.json
preview_image: /benchmarks/benchmark-1.png
date: 2022-08-23
weight: 2
Unlisted: true
---

This is an archived version of Single node benchmarks. Please refer to the new version [here](/benchmarks/single-node-speed-benchmark/).
qdrant-landing/content/blog/_index.md

---
title: Qdrant Blog
subtitle: Check out our latest posts
description: A place to learn how to become an expert traveler through vector space. Subscribe and we will update you on features and news.
email_placeholder: Enter your email
subscribe_button: Subscribe
features_title: Features and News
search_placeholder: What are you Looking for?
aliases: # There is no need to add aliases for future new tags and categories!
  - /tags
  - /tags/case-study
  - /tags/dailymotion
  - /tags/recommender-system
  - /tags/binary-quantization
  - /tags/embeddings
  - /tags/openai
  - /tags/gsoc24
  - /tags/open-source
  - /tags/summer-of-code
  - /tags/vector-database
  - /tags/artificial-intelligence
  - /tags/machine-learning
  - /tags/vector-search
  - /tags/case_study
  - /tags/dust
  - /tags/announcement
  - /tags/funding
  - /tags/series-a
  - /tags/azure
  - /tags/cloud
  - /tags/data-science
  - /tags/information-retrieval
  - /tags/benchmarks
  - /tags/performance
  - /tags/qdrant
  - /tags/blog
  - /tags/large-language-models
  - /tags/podcast
  - /tags/retrieval-augmented-generation
  - /tags/search
  - /tags/vector-search-engine
  - /tags/vector-image-search
  - /tags/vector-space-talks
  - /tags/retriever-ranker-architecture
  - /tags/semantic-search
  - /tags/llm
  - /tags/entity-matching-solution
  - /tags/real-time-processing
  - /tags/vector-space-talk
  - /tags/fastembed
  - /tags/quantized-emdedding-models
  - /tags/llm-recommendation-system
  - /tags/integrations
  - /tags/unstructured
  - /tags/integration
  - /tags/n8n
  - /tags/news
  - /tags/webinar
  - /tags/cohere
  - /tags/embedding-model
  - /tags/database
  - /tags/vector-search-database
  - /tags/neural-networks
  - /tags/similarity-search
  - /tags/embedding
  - /tags/corporate-news
  - /tags/nvidia
  - /tags/docarray
  - /tags/jina-integration
  - /categories
  - /categories/news
  - /categories/vector-search
  - /categories/webinar
  - /categories/vector-space-talk
---