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qdrant-landing/content/recommendations/recommendations-api.md | ---
title: Qdrant Recommendation API
description: The Qdrant Recommendation API enhances recommendation systems with advanced flexibility, supporting both ID and vector-based queries, and search strategies for precise, personalized content suggestions.
learnMore:
text: Learn More
url: /documentation/concepts/explore/
image:
src: /img/recommendation-api.svg
alt: Recommendation api
sitemapExclude: true
---
|
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/_index.md | ---
title: retrieval-augmented-generation
description: retrieval-augmented-generation
url: rag
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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:"
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alt: Ragas logo
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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
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alt: Cohere logo
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description: Easily integrate OpenAI embeddings with Qdrant using the official Python SDK.
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alt: Aleph Alpha logo
title: Aleph Alpha
description: Integrate Qdrant with Aleph Alpha's multimodal, multilingual embeddings.
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description: Easily integrate Qdrant with Jina's embeddings API.
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alt: AWS logo
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description: Utilize AWS Bedrock's embedding models with Qdrant seamlessly.
- id: 6
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alt: LangChain logo
title: LangChain
description: Qdrant seamlessly integrates with LangChain for LLM development.
- id: 7
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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/stack/_index.md | ---
title: Trusted by developers worldwide
subtitle: Qdrant is powering thousands of innovative AI solutions at leading companies. Engineers are choosing Qdrant for its top performance, high scalability, ease of use, and flexible cost and resource-saving options
sitemapExclude: True
--- |
qdrant-landing/content/stack/alphasense.md | ---
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qdrant-landing/content/stack/bayer.md | ---
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image: "content/images/logos/bayer-logo-mono"
name: "Bayer"
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--- |
qdrant-landing/content/stack/dailymotion.md | ---
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image: "content/images/logos/dailymotion-logo-mono"
name: "Dailymotion"
sitemapExclude: True
--- |
qdrant-landing/content/stack/deloitte.md | ---
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image: "content/images/logos/deloitte-logo-mono"
name: "Deloitte"
sitemapExclude: True
--- |
qdrant-landing/content/stack/disney-streaming.md | ---
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image: "content/images/logos/disney-streaming-logo-mono"
name: "Disney Streaming"
sitemapExclude: True
--- |
qdrant-landing/content/stack/flipkart.md | ---
draft: false
image: "content/images/logos/flipkart-logo-mono"
name: "Flipkart"
sitemapExclude: True
--- |
qdrant-landing/content/stack/hp-enterprise.md | ---
draft: false
image: "content/images/logos/hp-enterprise-logo-mono"
name: "Hewlett Packard Enterprise"
sitemapExclude: True
--- |
qdrant-landing/content/stack/hrs.md | ---
draft: false
image: "content/images/logos/hrs-logo-mono"
name: "HRS"
sitemapExclude: True
--- |
qdrant-landing/content/stack/johnoson-and-johnson.md | ---
draft: false
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name: "Johnson & Johnson"
sitemapExclude: True
--- |
qdrant-landing/content/stack/kaufland.md | ---
draft: false
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name: "Kaufland"
sitemapExclude: True
--- |
qdrant-landing/content/stack/microsoft.md | ---
draft: false
image: "content/images/logos/microsoft-logo-mono"
name: "Bayer"
sitemapExclude: True
--- |
qdrant-landing/content/stack/mozilla.md | ---
draft: false
image: "content/images/logos/mozilla-logo-mono"
name: "Mozilla"
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--- |
qdrant-landing/content/stars/_index.md | ---
title: Qdrant Stars
description: Qdrant Stars - Our Ambassador Program
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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:
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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-get-started.md | ---
title: Are you contributing to our code, content, or community?
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text: Become a Star
image:
src: /img/stars.svg
alt: Stars
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-list.md | ---
title: Meet our Stars
cards:
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alt: Robert Caulk Photo
name: Robert Caulk
position: Founder of Emergent Methods
description: Robert is working with a team on AskNews.app to adaptively enrich, index, and report on over 1 million news articles per day
- id: 1
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src: /img/stars/joshua-mo.jpg
alt: Joshua Mo Photo
name: Joshua Mo
position: DevRel at Shuttle.rs
description: Hey there! I primarily use Rust and am looking forward to contributing to the Qdrant community!
- id: 2
image:
src: /img/stars/nick-khami.jpg
alt: Nick Khami Photo
name: Nick Khami
position: Founder & Product Engineer
description: Founder and product engineer at Trieve and has been using Qdrant since late 2022
- id: 3
image:
src: /img/stars/owen-colegrove.jpg
alt: Owen Colegrove Photo
name: Owen Colegrove
position: Founder of SciPhi
description: Physics PhD, Quant @ Citadel and Founder at SciPhi
- id: 4
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alt: M K Pavan Kumar Photo
name: M K Pavan Kumar
position: Data Scientist and Lead GenAI
description: A seasoned technology expert with 14 years of experience in full stack development, cloud solutions, & artificial intelligence
- id: 5
image:
src: /img/stars/niranjan-akella.jpg
alt: Niranjan Akella Photo
name: Niranjan Akella
position: Scientist by Heart & AI Engineer
description: I build & deploy AI models like LLMs, Diffusion Models & Vision Models at scale
- id: 6
image:
src: /img/stars/bojan-jakimovski.jpg
alt: Bojan Jakimovski Photo
name: Bojan Jakimovski
position: Machine Learning Engineer
description: I'm really excited to show the power of the Qdrant as vector database
- id: 7
image:
src: /img/stars/haydar-kulekci.jpg
alt: Haydar KULEKCI Photo
name: Haydar KULEKCI
position: Senior Software Engineer
description: I am a senior software engineer and consultant with over 10 years of experience in data management, processing, and software development.
- id: 8
image:
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alt: Nicola Procopio Photo
name: Nicola Procopio
position: Senior Data Scientist @ Fincons Group
description: Nicola, a data scientist and open-source enthusiast since 2009, has used Qdrant since 2023. He developed fastembed for Haystack, vector search for Cheshire Cat A.I., and shares his expertise through articles, tutorials, and talks.
- id: 9
image:
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alt: Eduardo Vasquez Photo
name: Eduardo Vasquez
position: Data Scientist and MLOps Engineer
description: I am a Data Scientist and MLOps Engineer exploring generative AI and LLMs, creating YouTube content on RAG workflows and fine-tuning LLMs. I hold an MSc in Statistics and Data Science.
- id: 10
image:
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alt: Benito Martin Photo
name: Benito Martin
position: Independent Consultant | Data Science, ML and AI Project Implementation | Teacher and Course Content Developer
description: Over the past year, Benito developed MLOps and LLM projects. Based in Switzerland, Benito continues to advance his skills.
- id: 11
image:
src: /img/stars/nirant-kasliwal.jpg
alt: Nirant Kasliwal Photo
name: Nirant Kasliwal
position: FastEmbed Creator
description: I'm a Machine Learning consultant specializing in NLP and Vision systems for early-stage products. I've authored an NLP book recommended by Dr. Andrew Ng to Stanford's CS230 students and maintain FastEmbed at Qdrant for speed.
sitemapExclude: true
---
|
qdrant-landing/content/stars/stars-marketplaces.md | ---
title: Join our growing community
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description: Join our GitHub community and contribute to the future of vector databases.
link:
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url: https://github.com/qdrant/qdrant
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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
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alt: Twitter icon
title: Followers
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link:
text: Spread the Word
url: https://qdrant.to/twitter
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---
|
qdrant-landing/content/subscribe-confirmation/_index.md | ---
title: Subscribe
section_title: Subscribe
subtitle: Subscribe
description: Subscribe
--- |
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.
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url: /legal/terms_and_conditions/
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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.
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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. |
qdrant-landing/styles/write-good/README.md | Based on [write-good](https://github.com/btford/write-good).
> Naive linter for English prose for developers who can't write good and wanna learn to do other stuff good too.
```
The MIT License (MIT)
Copyright (c) 2014 Brian Ford
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
|
qdrant-landing/themes/qdrant-2024/README.md | ## Building, transpiling and running the project
Project uses Hugo build-in pipes to transpile and minify assets.
Pre-requisites:
- Dart Sass
### Install Dart Sass
You can [download](https://github.com/sass/dart-sass/releases/) a package for your OS from [Sass github](https://github.com/sass/dart-sass/releases/) and [add it to your PATH](https://www.google.com/search?q=add+path+variable).
Don't forget to restart your terminal after adding Dart Sass to your PATH.
#### Troubleshooting
Run the following commands:
```bash
hugo env | grep "dart-sass"
sass --embedded --version
```
And check that both commands output the same version of Dart Sass. If not, you may have several versions of Dart Sass installed on your system.
Don't forget to restart your terminal after adding Dart Sass to your PATH.
Don't use node-sass, it's incompatible.
|
qdrant-landing/themes/qdrant-2024/assets/css/components/readme.md | ## What to put in components?
Components are the building blocks which don't have relations to partials or layouts.
They are reusable classes that can be used across different pages. |
qdrant-landing/themes/qdrant-2024/assets/css/partials/readme.md | ## What to put in partials?
This directory contains styles related to specific partial in `../../layouts/partial` directory. |
qdrant-landing/themes/qdrant-2024/layouts/debug.skip/README.md | This section dedicated to debugging and troubleshooting, and to provide a list of components. This section **won't be rendered in production**. |
qdrant-landing/themes/qdrant/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/themes/qdrant/archetypes/customer-logo.md | ---
draft: false
image: "content/images/logos/{{ replace .Name "-" " " }}-logo" #logo image should be in pdf format, do not include extension here
name: "{{ replace .Name "-" " " | title }}"
sitemapExclude: True
---
|
qdrant-landing/themes/qdrant/archetypes/default.md | +++
+++
|
qdrant-landing/themes/qdrant/archetypes/delimiter.md | ---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "{{ replace .Name "-" " " | title }}"
type: delimiter
weight: 0 # Change this weight to change order of sections
sitemapExclude: True
--- |
qdrant-landing/themes/qdrant/archetypes/external-link.md | ---
# External link template
title: "{{ replace .Name "-" " " | title }}"
type: external-link
external_url: https://github.com/qdrant/qdrant # Change this link to your external link
sitemapExclude: True
---
|