lukawskikacper/qdrant-landing-page-2024-06-21 huggingface dataset:
qdrant-landing/content/recommendations/recommendations-features.md

---
title: Recommendations with Qdrant
description: Recommendation systems, powered by Qdrant's efficient data retrieval, boost the ability to deliver highly personalized content recommendations across various media, enhancing user engagement and accuracy on a scalable platform. Explore why Qdrant is the optimal solution for your recommendation system projects.
features:
- id: 0
  icon:
    src: /icons/outline/chart-bar-blue.svg
    alt: Chart bar
  title: Efficient Data Handling
  description: Qdrant excels in managing high-dimensional vectors, enabling streamlined storage and retrieval for complex recommendation systems.
- id: 1
  icon:
    src: /icons/outline/search-text-blue.svg
    alt: Search text
  title: Advanced Indexing Method
  description: Leveraging HNSW indexing, Qdrant ensures rapid, accurate searches crucial for effective recommendation engines.
- id: 2
  icon:
    src: /icons/outline/headphones-blue.svg
    alt: Headphones
  title: Flexible Query Options
  description: With support for payloads and filters, Qdrant offers personalized recommendation capabilities through detailed metadata handling.
sitemapExclude: true
---
qdrant-landing/content/recommendations/recommendations-hero.md

---
title: Recommendation Systems
description: Step into the next generation of recommendation engines powered by Qdrant. Experience a new level of intelligence in application interactions, offering unprecedented accuracy and depth in user personalization.
startFree:
  text: Get Started
  url: https://cloud.qdrant.io/
learnMore:
  text: Contact Us
  url: /contact-us/
image:
  src: /img/vectors/vector-1.svg
  alt: Recommendation systems
sitemapExclude: true
---
qdrant-landing/content/recommendations/recommendations-use-cases.md

---
title: Learn how to get started with Qdrant for your recommendation system use case
features:
- id: 0
  image:
    src: /img/recommendations-use-cases/music-recommendation.svg
    srcMobile: /img/recommendations-use-cases/music-recommendation-mobile.svg
    alt: Music recommendation
  title: Music Recommendation with Qdrant
  description: Build a song recommendation engine based on music genres and other metadata.
  link:
    text: View Tutorial
    url: /blog/human-language-ai-models/
- id: 1
  image:
    src: /img/recommendations-use-cases/food-discovery.svg
    srcMobile: /img/recommendations-use-cases/food-discovery-mobile.svg
    alt: Food discovery
  title: Food Discovery with Qdrant
  description: Interactive demo recommends meals based on likes/dislikes and local restaurant options.
  link:
    text: View Demo
    url: https://food-discovery.qdrant.tech/
caseStudy:
  logo:
    src: /img/recommendations-use-cases/customer-logo.svg
    alt: Logo
  title: Recommendation Engine with Qdrant Vector Database
  description: Dailymotion's Journey to Crafting the Ultimate Content-Driven Video Recommendation Engine with Qdrant Vector Database.
  link:
    text: Read Case Study
    url: /blog/case-study-dailymotion/
  image:
    src: /img/recommendations-use-cases/case-study.png
    alt: Preview
sitemapExclude: true
---
qdrant-landing/content/retrieval-augmented-generation/retrieval-augmented-generation-evaluation.md

---
title: RAG Evaluation
descriptionFirstPart: Retrieval Augmented Generation (RAG) harnesses large language models to enhance content generation by effectively leveraging existing information. By amalgamating specific details from various sources, RAG facilitates accurate and relevant query results, making it invaluable across domains such as medical, finance, and academia for content creation, Q&A applications, and information synthesis.
descriptionSecondPart: However, evaluating RAG systems is essential to refine and optimize their performance, ensuring alignment with user expectations and validating their functionality.
image:
  src: /img/retrieval-augmented-generation-evaluation/become-a-partner-graphic.svg
  alt: Graphic
partnersTitle: "We work with the best in the industry on RAG evaluation:"
logos:
- id: 0
  icon:
    src: /img/retrieval-augmented-generation-evaluation/arize-logo.svg
    alt: Arize logo
- id: 1
  icon:
    src: /img/retrieval-augmented-generation-evaluation/ragas-logo.svg
    alt: Ragas logo
- id: 2
  icon:
    src: /img/retrieval-augmented-generation-evaluation/quotient-logo.svg
    alt: Quotient logo
sitemapExclude: true
---
qdrant-landing/content/retrieval-augmented-generation/retrieval-augmented-generation-features.md

---
title: RAG with Qdrant
description: RAG, powered by Qdrant's efficient data retrieval, elevates AI's capacity to generate rich, context-aware content across text, code, and multimedia, enhancing relevance and precision on a scalable platform. Discover why Qdrant is the perfect choice for your RAG project.
features:
- id: 0
  icon:
    src: /icons/outline/speedometer-blue.svg
    alt: Speedometer
  title: Highest RPS
  description: Qdrant leads with top requests-per-second, outperforming alternative vector databases in various datasets by up to 4x.
- id: 1
  icon:
    src: /icons/outline/time-blue.svg
    alt: Time
  title: Fast Retrieval
  description: "Qdrant achieves the lowest latency, ensuring quicker response times in data retrieval: 3ms response for 1M Open AI embeddings."
- id: 2
  icon:
    src: /icons/outline/vectors-blue.svg
    alt: Vectors
  title: Multi-Vector Support
  description: Integrate the strengths of multiple vectors per document, such as title and body, to create search experiences your customers admire.
- id: 3
  icon:
    src: /icons/outline/compression-blue.svg
    alt: Compression
  title: Built-in Compression
  description: Significantly reduce memory usage, improve search performance and save up to 30x cost for high-dimensional vectors with Quantization.
sitemapExclude: true
---
qdrant-landing/content/retrieval-augmented-generation/retrieval-augmented-generation-hero.md

---
title: Retrieval Augmented Generation (RAG)
description: Unlock the full potential of your AI with RAG powered by Qdrant. Dive into a new era of intelligent applications that understand and interact with unprecedented accuracy and depth.
startFree:
  text: Get Started
  url: https://cloud.qdrant.io/
learnMore:
  text: Contact Us
  url: /contact-us/
image:
  src: /img/vectors/vector-2.svg
  alt: Retrieval Augmented Generation
sitemapExclude: true
---
qdrant-landing/content/retrieval-augmented-generation/retrieval-augmented-generation-integrations.md

---
title: Qdrant integrates with all leading LLM providers and frameworks
integrations:
- id: 0
  icon:
    src: /img/integrations/integration-cohere.svg
    alt: Cohere logo
  title: Cohere
  description: Integrate Qdrant with Cohere's co.embed API and Python SDK.
- id: 1
  icon:
    src: /img/integrations/integration-gemini.svg
    alt: Gemini logo
  title: Gemini
  description: Connect Qdrant with Google's Gemini Embedding Model API seamlessly.
- id: 2
  icon:
    src: /img/integrations/integration-open-ai.svg
    alt: OpenAI logo
  title: OpenAI
  description: Easily integrate OpenAI embeddings with Qdrant using the official Python SDK.
- id: 3
  icon:
    src: /img/integrations/integration-aleph-alpha.svg
    alt: Aleph Alpha logo
  title: Aleph Alpha
  description: Integrate Qdrant with Aleph Alpha's multimodal, multilingual embeddings.
- id: 4
  icon:
    src: /img/integrations/integration-jina.svg
    alt: Jina logo
  title: Jina
  description: Easily integrate Qdrant with Jina's embeddings API.
- id: 5
  icon:
    src: /img/integrations/integration-aws.svg
    alt: AWS logo
  title: AWS Bedrock
  description: Utilize AWS Bedrock's embedding models with Qdrant seamlessly.
- id: 6
  icon:
    src: /img/integrations/integration-lang-chain.svg
    alt: LangChain logo
  title: LangChain
  description: Qdrant seamlessly integrates with LangChain for LLM development.
- id: 7
  icon:
    src: /img/integrations/integration-llama-index.svg
    alt: LlamaIndex logo
  title: LlamaIndex
  description: Qdrant integrates with LlamaIndex for efficient data indexing in LLMs.
sitemapExclude: true
---
qdrant-landing/content/retrieval-augmented-generation/retrieval-augmented-generation-use-cases.md

---
title: Learn how to get started with Qdrant for your RAG use case
features:
- id: 0
  image:
    src: /img/retrieval-augmented-generation-use-cases/case1.svg
    srcMobile: /img/retrieval-augmented-generation-use-cases/case1-mobile.svg
    alt: Music recommendation
  title: Question and Answer System with LlamaIndex
  description: Combine Qdrant and LlamaIndex to create a self-updating Q&A system.
  link:
    text: Video Tutorial
    url: https://www.youtube.com/watch?v=id5ql-Abq4Y&t=56s
- id: 1
  image:
    src: /img/retrieval-augmented-generation-use-cases/case2.svg
    srcMobile: /img/retrieval-augmented-generation-use-cases/case2-mobile.svg
    alt: Food discovery
  title: Retrieval Augmented Generation with OpenAI and Qdrant
  description: Basic RAG pipeline with Qdrant and OpenAI SDKs.
  link:
    text: Learn More
    url: /articles/food-discovery-demo/
caseStudy:
  logo:
    src: /img/retrieval-augmented-generation-use-cases/customer-logo.svg
    alt: Logo
  title: See how Dust is using Qdrant for RAG
  description: Dust provides companies with the core platform to execute on their GenAI bet for their teams by deploying LLMs across the organization and providing context aware AI assistants through RAG.
  link:
    text: Read Case Study
    url: /blog/dust-and-qdrant/
  image:
    src: /img/retrieval-augmented-generation-use-cases/case-study.png
    alt: Preview
sitemapExclude: true
---
qdrant-landing/content/stars/stars-about.md

---
title: About Qdrant Stars
descriptionFirstPart: Qdrant Stars is an exclusive program to the top contributors and evangelists inside the Qdrant community.
descriptionSecondPart: These are the experts responsible for leading community discussions, creating high-quality content, and participating in Qdrant’s events and meetups.
image:
  src: /img/stars-about.png
  alt: Stars program
sitemapExclude: true
---
qdrant-landing/content/stars/stars-benefits.md

---
title: Everything you need to extend your current reach to be the voice of the developer community and represent Qdrant
benefits:
- id: 0
  icon:
    src: /icons/outline/training-blue.svg
    alt: Training
  title: Training
  description: You will be equipped with the assets and knowledge to organize and execute successful talks and events. Get access to our content library with slide decks, templates, and more.
- id: 1
  icon:
    src: /icons/outline/award-blue.svg
    alt: Award
  title: Recognition
  description: Win a certificate and be featured on our website page. Plus, enjoy the distinction of receiving exclusive Qdrant swag.
- id: 2
  icon:
    src: /icons/outline/travel-blue.svg
    alt: Travel
  title: Travel
  description: Benefit from a dedicated travel fund for speaking engagements at developer conferences.
- id: 3
  icon:
    src: /icons/outline/star-ticket-blue.svg
    alt: Star ticket
  title: Beta-tests
  description: Get a front-row seat to the future of Qdrant with opportunities to beta-test new releases and access our detailed product roadmap.
sitemapExclude: true
---
qdrant-landing/content/stars/stars-hero.md

---
title: You are already a star in our community!
description: The Qdrant Stars program is here to take that one step further.
button:
  text: Become a Star
  url: https://forms.gle/q4fkwudDsy16xAZk8
image:
  src: /img/stars-hero.svg
  alt: Stars
sitemapExclude: true
---
qdrant-landing/content/stars/stars-marketplaces.md

---
title: Join our growing community
cards:
- id: 0
  icon:
    src: /img/stars-marketplaces/github.svg
    alt: Github icon
  title: Stars
  statsToUse: githubStars
  description: Join our GitHub community and contribute to the future of vector databases.
  link:
    text: Start Contributing
    url: https://github.com/qdrant/qdrant
- id: 1
  icon:
    src: /img/stars-marketplaces/discord.svg
    alt: Discord icon
  title: Members
  statsToUse: discordMembers
  description: Discover and chat on a vibrant community of developers working on the future of AI.
  link:
    text: Join our Conversations
    url: https://qdrant.to/discord
- id: 2
  icon:
    src: /img/stars-marketplaces/twitter.svg
    alt: Twitter icon
  title: Followers
  statsToUse: twitterFollowers
  description: Join us on X, participate and find out about our updates and releases before anyone else.
  link:
    text: Spread the Word
    url: https://qdrant.to/twitter
sitemapExclude: true
---
qdrant-landing/content/subscribe/_index.md

---
title: Subscribe
section_title: Subscribe
subtitle: Subscribe 
description: Subscribe 
image: 
  src: /img/subscribe.svg
  srcMobile: /img/mobile/subscribe.svg
  alt: Astronaut
form:
  title: Sign up for our newsletter
  description: Stay up to date on product news, technical articles, and upcoming educational webinars.
  label: Email
  placeholder: info@qdrant.com
  button: Subscribe
footer:
  rights: "&copy; 2024 Qdrant. All Rights Reserved"
  termsLink:
    url: /legal/terms_and_conditions/
    text: Terms
  policyLink:
    url: /legal/privacy-policy/
    text: Privacy Policy
  impressumLink:
    url: /legal/impressum/
    text: Impressum
sitemapExclude: true
---
qdrant-landing/content/use-cases/_index.md

---
title: Vector Database Use Cases
section_title: Apps and Ideas Qdrant made possible
type: page
description: Applications, business cases and startup ideas you can build with Qdrant vector search engine.
build:
  render: always
cascade:
- build:
    list: local
    publishResources: false
    render: never
aliases:
  - /solutions/
---
qdrant-landing/content/use-cases/advertising.md

---
title: Advertising
icon: ad-campaign
sitemapExclude: True
---

User interests cannot be described with rules, and that's where neural networks come in. 
Qdrant vector database will allow sufficient flexibility in neural network recommendations so that each user sees only the relevant ad.
Advanced filtering mechanisms, such as geo-location, do not compromise on speed and accuracy, which is especially important for online advertising.
qdrant-landing/content/use-cases/customer-support-optimization.md

---
title: Customer Support and Sales Optimization
icon: customer-service
sitemapExclude: True
---

Current advances in NLP can reduce the retinue work of customer service by up to 80 percent.
No more answering the same questions over and over again. A chatbot will do that, and people can focus on complex problems.
But not only automated answering, it is also possible to control the quality of the department and automatically identify flaws in conversations.
qdrant-landing/content/use-cases/e-commerce-search.md

---
title: E-Commerce Search
icon: dairy-products
weight: 30
sitemapExclude: True
---

Increase your online basket size and revenue with the AI-powered search.
No need in manually assembled synonym lists, neural networks get the context better.
With neural approach the search results could be not only precise, but also **personalized**.
And Qdrant will be the backbone of this search. 

Read more about [Deep Learning-based Product Recommendations](https://arxiv.org/abs/2104.07572) in the paper by The Home Depot.
qdrant-landing/content/use-cases/face-recognition.md

---
title: Biometric identification
icon: face-scan
sitemapExclude: True
---

Not only totalitarian states use facial recognition. 
With this technology, you can also improve the user experience and simplify authentication.
Make it possible to pay without a credit card and buy in the store without cashiers.
And the scalable face recognition technology is based on vector search, which is what Qdrant provides.
Some of the many articles on the topic of [Face Recognition](https://arxiv.org/abs/1810.06951v1) and [Speaker Recognition](https://arxiv.org/abs/2003.11982).
qdrant-landing/content/use-cases/fashion-search.md

---
title: Fashion Search
icon: clothing
custom_link_name: Article by Zalando
custom_link: https://engineering.zalando.com/posts/2018/02/search-deep-neural-network.html
custom_link_name2: Our Demo
custom_link2: https://qdrant.to/fashion-search-demo
sitemapExclude: True
---


Empower shoppers to find the items they want by uploading any image or browsing through a gallery instead of searching with keywords.
A visual similarity search helps solve this problem. And with the advanced filters that Qdrant provides, you can be sure to have the right size in stock for the jacket the user finds.

Large companies like [Zalando](https://engineering.zalando.com/posts/2018/02/search-deep-neural-network.html) are investing in it, but we also made our [demo](https://qdrant.to/fashion-search-demo) using public dataset.
qdrant-landing/content/use-cases/fintech.md

---
title: Fintech
icon: bank
sitemapExclude: True
---

Fraud detection is like recommendations in reverse. 
One way to solve the problem is to look for similar cheating behaviors. 
But often this is not enough and manual rules come into play.
Qdrant vector database allows you to combine both approaches because it provides a way to filter the result using arbitrary conditions.
And all this can happen in the time till the client takes his hand off the terminal.
Here is some related [research paper](https://arxiv.org/abs/1808.05492).
qdrant-landing/content/use-cases/food-search.md

---
title: Food Discovery
weight: 20
icon: search
sitemapExclude: True
---

There are multiple ways to discover things, text search is not the only one. 
In the case of food, people rely more on appearance than description and ingredients.
So why not let people choose their next lunch by its appearance, even if they don't know the name of the dish? We made a [demo](https://food-discovery.qdrant.tech/) to showcase this approach.
qdrant-landing/content/use-cases/job-matching.md

---
title: HR & Job Search
icon: job-search
weight: 10
sitemapExclude: True
---

Vector search engine can be used to match candidates and jobs even if there are no matching keywords or explicit skill descriptions. 
For example, it can automatically map **'frontend engineer'** to **'web developer'**, no need for any predefined categorization.
Neural job matching is used at [MoBerries](https://www.moberries.com/) for automatic job recommendations.
qdrant-landing/content/use-cases/law-search.md

---
title: Law Case Search
icon: hammer
sitemapExclude: True
---

The wording of court decisions can be difficult not only for ordinary people, but sometimes for the lawyers themselves.
It is rare to find words that exactly match a similar precedent. 
That's where AI, which has seen hundreds of thousands of court decisions and can compare them using vector similarity search engine, can help. Here is some related [research](https://arxiv.org/abs/2004.12307).
qdrant-landing/content/use-cases/media-and-games.md

---
title: Media and Games
icon: game-controller
sitemapExclude: True
---

Personalized recommendations for music, movies, games, and other entertainment content are also some sort of search.
Except the query in it is not a text string, but user preferences and past experience.
And with Qdrant, user preference vectors can be updated in real-time, no need to deploy a MapReduce cluster. Read more about "[Metric Learning Recommendation System](https://arxiv.org/abs/1803.00202)"
qdrant-landing/content/use-cases/medical-diagnostics.md

---
title: Medical Diagnostics
icon: x-rays
sitemapExclude: True
---

The growing volume of data and the increasing interest in the topic of health care is creating products to help doctors with diagnostics.
One such product might be a search for similar cases in an ever-expanding database of patient histories.
Search not only by symptom description, but also by data from, for example, MRI machines.
Vector Search [is applied](https://www.sciencedirect.com/science/article/abs/pii/S0925231217308445) even here.
qdrant-landing/content/use-cases/vectors-use-case.md

---
title: Qdrant Vector Database Use Cases
subtitle: Explore the vast applications of the Qdrant vector database. From retrieval augmented generation to anomaly detection, advanced search, and recommendation systems, our solutions unlock new dimensions of data and performance.
featureCards:
  - id: 0
    title: Advanced Search
    content: Elevate your apps with advanced search capabilities. Qdrant excels in processing high-dimensional data, enabling nuanced similarity searches, and understanding semantics in depth. Qdrant also handles multimodal data with fast and accurate search algorithms.
    link:
      text: Learn More
      url: /advanced-search/
  - id: 1
    title: Recommendation Systems
    content: Create highly responsive and personalized recommendation systems with tailored suggestions. Qdrant’s Recommendation API offers great flexibility, featuring options such as best score recommendation strategy. This enables new scenarios of using multiple vectors in a single query to impact result relevancy.
    link:
      text: Learn More
      url: /recommendations/
  - id: 2
    title: Retrieval Augmented Generation (RAG)
    content: Enhance the quality of AI-generated content. Leverage Qdrant's efficient nearest neighbor search and payload filtering features for retrieval-augmented generation. You can then quickly access relevant vectors and integrate a vast array of data points.
    link:
      text: Learn More
      url: /rag/
  - id: 3
    title: Data Analysis and Anomaly Detection
    content: Transform your approach to Data Analysis and Anomaly Detection. Leverage vectors to quickly identify patterns and outliers in complex datasets. This ensures robust and real-time anomaly detection for critical applications.
    link:
      text: Learn More
      url: /data-analysis-anomaly-detection/
---
qdrant-landing/layouts/partials/README.md

# What to put in the partials directory on the top level (aka here)

Partials not depending on the theme which can be reused even if the theme changes, for example:
- a partial returning only the <head> of a page
- a partial returning only one html element like a link or a image without any class or style
- a partial returning only some logic like a loop or a condition without any html

# What not to put in the partials directory on the top level (aka here)

Partials depending on the theme, for example:
- a partial returning elements with classes or styles
- a partial returning a whole section of a page
qdrant-landing/styles/Google/README.md

Based on https://github.com/errata-ai/Google, last updated October 16, 2023.

Exceptions, based on "standard" language used by Qdrant:

- Deleted ["We.yml"](https://github.com/errata-ai/Google/blob/master/Google/We.yml)
- Set ["Exclamation.yml"](https://github.com/errata-ai/Google/blob/master/Google/Exclamation.yml) to suggestion level, revised message.
- Removed reference to GCP as "preferred" cloud from WordList.yml
- Removed preference for "Command-line tool" over "CLI" from WordList.yml
- Updated Acronyms.yml
qdrant-landing/styles/Qdrant/LICENSE.md

The files in this directory were created by [GitLab](https://about.gitlab.com/), licensed under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). We modified the contents of the files for Cobalt styles.

For the current versions of these files, see https://gitlab.com/gitlab-org/gitlab/-/tree/master/doc/.vale/gitlab.