lukawskikacper/qdrant-landing-page-2024-06-21 huggingface dataset:
qdrant-landing/content/documentation/tutorials/_index.md

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# Tutorials 

These tutorials demonstrate different ways you can build vector search into your applications. 

| Essential How-Tos                                                                      | Description                                                       | Stack                                       |   
|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
| [Semantic Search for Beginners](../tutorials/search-beginners/)                 | Create a simple search engine locally in minutes.                 | Qdrant                                      | 
| [Simple Neural Search](../tutorials/neural-search/)                             | Build and deploy a neural search that browses startup data.       | Qdrant, BERT, FastAPI                       | 
| [Neural Search with FastEmbed](../tutorials/neural-search-fastembed/)                                 | Build and deploy a neural search with our FastEmbed library.               | Qdrant                                      |  
| [Bulk Upload Vectors](../tutorials/bulk-upload/)                                | Upload a large scale dataset.                                     | Qdrant                                      | 
| [Asynchronous API](../tutorials/async-api/)                                     | Communicate with Qdrant server asynchronously with Python SDK.    | Qdrant, Python                              |
| [Create Dataset Snapshots](../tutorials/create-snapshot/)                       | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant                                      | 
| [Load HuggingFace Dataset](../tutorials/huggingface-datasets/)                      | Load a Hugging Face dataset to Qdrant                             | Qdrant, Python, datasets                    |
| [Measure retrieval quality](../tutorials/retrieval-quality/)                    | Measure and fine-tune the retrieval quality                       | Qdrant, Python, datasets                    |
| [Use semantic search to navigate your codebase](../tutorials/code-search/)      | Implement semantic search application for code search task        | Qdrant, Python, sentence-transformers, Jina |
qdrant-landing/content/enterprise-solutions/enterprise-benefits.md

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qdrant-landing/content/enterprise-solutions/enterprise-solutions-hero.md

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title: Enterprise-Grade Vector Search 
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qdrant-landing/content/enterprise-solutions/testimonial.md

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review: Enterprises like Bosch use Qdrant for unparalleled performance and massive-scale vector search. “With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform at enterprise scale.”
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qdrant-landing/content/features/_index.md

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Qdrant is a vector database & vector similarity search engine.
It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
qdrant-landing/content/features/easy-to-use.md

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Provides the [OpenAPI v3 specification](https://api.qdrant.tech/api-reference) to generate a client library in almost any programming language.
Alternatively utilise [ready-made client for Python](https://github.com/qdrant/qdrant-client) or other programming languages with additional functionality.
qdrant-landing/content/features/fast-and-accurate.md

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Implement a unique custom modification of the [HNSW algorithm](https://arxiv.org/abs/1603.09320) for Approximate Nearest Neighbor Search.
Search with a [State-of-the-Art speed](https://github.com/qdrant/benchmark/tree/master/search_benchmark) and apply search filters without [compromising on results](https://blog.vasnetsov.com/posts/categorical-hnsw/).
qdrant-landing/content/features/rich-data-types.md

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Vector payload supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more.
Payload filtering conditions allow you to build almost any custom business logic that should work on top of similarity matching.
qdrant-landing/content/headless/cookies.md

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qdrant-landing/content/headless/newsletter.md

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qdrant-landing/content/headless/our-story.md

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extraContent: We started Qdrant with the mission to build the most efficient, scalable, high-performance vector database on the market. Since then we have seen incredible user growth and support from our open-source community with thousands of users and millions of downloads.
year: In 2021
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qdrant-landing/content/headless/tutorials.md

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qdrant-landing/content/headless/main/docker-deploy.md

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qdrant-landing/content/headless/main/vectors.md

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title: Vectors in Action
subtitle: Turn embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and more.
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    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.
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    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.
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    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.
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    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.
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qdrant-landing/content/hybrid-cloud/_index.md

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title: 'Qdrant Hybrid Cloud: Flexible Deployment, Data Privacy, and Cost Efficiency'
description: Qdrant's new Hybrid Cloud was created for seamless deployment and management of vector databases. Ensure privacy, data sovereignty, and cost efficiency for AI-driven applications. Learn more and get started today.
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qdrant-landing/content/hybrid-cloud/hybrid-cloud-cases.md

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qdrant-landing/content/hybrid-cloud/hybrid-cloud-features.md

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description: Seamlessly deploy and manage the vector database across diverse environments, ensuring performance, security, and cost efficiency for AI-driven applications.
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