Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Day 0 Video 02: Set Up Your Environment| Qdrant - Vector Database
Day 0 Video 03: Create a Collection| Qdrant - Vector Database
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
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.| qdrant.tech
A place to learn how to become an expert traveler through vector space. Subscribe and we will update you on features and news.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Discover Qdrant Cloud, the cutting-edge managed cloud for scalable, high-performance AI applications. Manage and deploy your vector data with ease today.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Bulk Upload Vectors to a Qdrant Collection Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that. The first important detail about data uploading is that the bottleneck is usually located on the client side, not on the server side. This means that if you are uploading a large dataset, you should prefer a high-performance client library. We recommend using our Rust client library for this purpose, as it is the fastest client library availa...| Qdrant - Vector Database
Frequently Asked Questions: General Topics VectorsSearchCollectionsCompatibilityCloud Vectors What is the maximum vector dimension supported by Qdrant? Qdrant supports up to 65,535 dimensions by default, but this can be configured to support higher dimensions. What is the maximum size of vector metadata that can be stored? There is no inherent limitation on metadata size, but it should be optimized for performance and resource usage. Users can set upper limits in the configuration.| Qdrant - Vector Database
Reranking in RAG with Qdrant Vector Database In Retrieval-Augmented Generation (RAG) systems, irrelevant or missing information can throw off your model’s ability to produce accurate, meaningful outputs. One of the best ways to ensure you’re feeding your language model the most relevant, context-rich documents is through reranking. It’s a game-changer. In this guide, we’ll dive into using reranking to boost the relevance of search results in Qdrant. We’ll start with an easy use case...| Qdrant - Vector Database
Role Management 💡 You can access this in Access Management > User & Role Managementif available see this page for details. A Role contains a set of permissions that define the ability to perform or control specific actions in Qdrant Cloud. Permissions are accessible through the Permissions tab in the Role Details page and offer fine-grained access control, logically grouped for easy identification. Built-In Roles Qdrant Cloud includes some built-in roles for common use-cases. The permissio...| Qdrant - Vector Database
Build Your First Semantic Search Engine in 5 Minutes Time: 5 - 15 minLevel: Beginner Overview If you are new to vector databases, this tutorial is for you. In 5 minutes you will build a semantic search engine for science fiction books. After you set it up, you will ask the engine about an impending alien threat. Your creation will recommend books as preparation for a potential space attack.| Qdrant - Vector Database
Creating a Hybrid Cloud Environment The following instruction set will show you how to properly set up a Qdrant cluster in your Hybrid Cloud Environment. You can also watch a video demo on how to set up a Hybrid Cloud Environment: To learn how Hybrid Cloud works, read the overview document. Prerequisites Kubernetes cluster: To create a Hybrid Cloud Environment, you need a standard compliant Kubernetes cluster. You can run this cluster in any cloud, on-premise or edge environment, with distrib...| Qdrant - Vector Database
Qdrant Private Cloud Setup Requirements Kubernetes cluster: To install Qdrant Private Cloud, you need a standard compliant Kubernetes cluster. You can run this cluster in any cloud, on-premise or edge environment, with distributions that range from AWS EKS to VMWare vSphere. See Deployment Platforms for more information. Storage: For storage, you need to set up the Kubernetes cluster with a Container Storage Interface (CSI) driver that provides block storage. For vertical scaling, the CSI dri...| Qdrant - Vector Database
How Does Vector Search Work in Qdrant? If you are still trying to figure out how vector search works, please read ahead. This document describes how vector search is used, covers Qdrant’s place in the larger ecosystem, and outlines how you can use Qdrant to augment your existing projects. For those who want to start writing code right away, visit our Complete Beginners tutorial to build a search engine in 5-15 minutes.| Qdrant - Vector Database
Automate filtering with LLMs Our complete guide to filtering in vector search describes why filtering is important, and how to implement it with Qdrant. However, applying filters is easier when you build an application with a traditional interface. Your UI may contain a form with checkboxes, sliders, and other elements that users can use to set their criteria. But what if you want to build a RAG-powered application with just the conversational interface, or even voice commands? In this case, ...| Qdrant - Vector Database
Build a Neural Search Service with Sentence Transformers and Qdrant Time: 30 minLevel: BeginnerOutput: GitHub This tutorial shows you how to build and deploy your own neural search service to look through descriptions of companies from startups-list.com and pick the most similar ones to your query. The website contains the company names, descriptions, locations, and a picture for each entry. A neural search service uses artificial neural networks to improve the accuracy and relevance of searc...| Qdrant - Vector Database
Private Cloud Configuration The Qdrant Private Cloud helm chart has several configuration options. The following YAML shows all configuration options with their default values: operator:# Amount of replicas for the Qdrant operator (v2)replicaCount:1image:# Image repository for the qdrant operatorrepository:registry.cloud.qdrant.io/qdrant/operator# Image pullPolicypullPolicy:IfNotPresent# Overrides the image tag whose default is the chart appVersion.tag:""# Optional image pull secretsimagePull...| Qdrant - Vector Database
Backup and Restore Qdrant Collections Using Snapshots Time: 20 minLevel: Beginner A collection is a basic unit of data storage in Qdrant. It contains vectors, their IDs, and payloads. However, keeping the search efficient requires additional data structures to be built on top of the data. Building these data structures may take a while, especially for large collections. That’s why using snapshots is the best way to export and import Qdrant collections, as they contain all the bits and piece...| Qdrant - Vector Database
Creating a Qdrant Cluster in Hybrid Cloud Once you have created a Hybrid Cloud Environment, you can create a Qdrant cluster in that enviroment. Use the same process to Create a cluster. Make sure to select your Hybrid Cloud Environment as the target. Note that in the “Kubernetes Configuration” section you can additionally configure: Node selectors for the Qdrant database pods Toleration for the Qdrant database pods Additional labels for the Qdrant database pods A service type and annotati...| Qdrant - Vector Database
Send S3 Data to Qdrant Vector Store with LangChain Time: 30 minLevel: Beginner Data ingestion into a vector store is essential for building effective search and retrieval algorithms, especially since nearly 80% of data is unstructured, lacking any predefined format. In this tutorial, we’ll create a streamlined data ingestion pipeline, pulling data directly from AWS S3 and feeding it into Qdrant. We’ll dive into vector embeddings, transforming unstructured data into a format that allows yo...| Qdrant - Vector Database
Frequently Asked Questions: Database Optimization How do I reduce memory usage? The primary source of memory usage is vector data. There are several ways to address that: Configure Quantization to reduce the memory usage of vectors. Configure on-disk vector storage The choice of the approach depends on your requirements. Read more about configuring the optimal use of Qdrant. How do you choose the machine configuration? There are two main scenarios of Qdrant usage in terms of resource consumpt...| Qdrant - Vector Database
Upload and Search Large collections cost-efficiently Time: 2 daysLevel: Advanced In this tutorial, we will describe an approach to upload, index, and search a large volume of data cost-efficiently, on an example of the real-world dataset LAION-400M. The goal of this tutorial is to demonstrate what minimal amount of resources is required to index and search a large dataset, while still maintaining a reasonable search latency and accuracy.| Qdrant - Vector Database
How to Generate Text Embedings with FastEmbed Install FastEmbed pipinstallfastembed Just for demo purposes, you will use Lists and NumPy to work with sample data. fromtypingimportListimportnumpyasnp Load default model In this example, you will use the default text embedding model, BAAI/bge-small-en-v1.5. fromfastembedimportTextEmbedding Add sample data Now, add two sample documents. Your documents must be in a list, and each document must be a string documents:List[str]=["FastEmbed is lighter...| Qdrant - Vector Database
Reranking Hybrid Search Results with Qdrant Vector Database Hybrid search combines dense and sparse retrieval to deliver precise and comprehensive results. By adding reranking with ColBERT, you can further refine search outputs for maximum relevance. In this guide, we’ll show you how to implement hybrid search with reranking in Qdrant, leveraging dense, sparse, and late interaction embeddings to create an efficient, high-accuracy search system. Let’s get started! Overview Let’s start by...| Qdrant - Vector Database
Navigate Your Codebase with Semantic Search and Qdrant Time: 45 minLevel: Intermediate You too can enrich your applications with Qdrant semantic search. In this tutorial, we describe how you can use Qdrant to navigate a codebase, to help you find relevant code snippets. As an example, we will use the Qdrant source code itself, which is mostly written in Rust. The approach We want to search codebases using natural semantic queries, and searching for code based on similar logic. You can set up ...| Qdrant - Vector Database
Agentic RAG With CrewAI & Qdrant Vector Database Time: 45 minLevel: BeginnerOutput: GitHub By combining the power of Qdrant for vector search and CrewAI for orchestrating modular agents, you can build systems that don’t just answer questions but analyze, interpret, and act. Traditional RAG systems focus on fetching data and generating responses, but they lack the ability to reason deeply or handle multi-step processes. In this tutorial, we’ll walk you through building an Agentic RAG syste...| Qdrant - Vector Database
User Management 💡 You can access this in Access Management > User & Role Managementif available see this page for details. Inviting Users to an Account Users can be invited via the User Management section, where they are assigned the Base role by default. Additionally, users have the option to select a specific role when inviting another user. The Base role is a predefined role with minimal permissions, granting users access to the platform while restricting them to viewing only their own ...| Qdrant - Vector Database
Agentic RAG With LangGraph and Qdrant Traditional Retrieval-Augmented Generation (RAG) systems follow a straightforward path: query → retrieve → generate. Sure, this works well for many scenarios. But let’s face it—this linear approach often struggles when you’re dealing with complex queries that demand multiple steps or pulling together diverse types of information. Agentic RAG takes things up a notch by introducing AI agents that can orchestrate multiple retrieval steps and smartl...| Qdrant - Vector Database
Use Collaborative Filtering to Build a Movie Recommendation System with Qdrant Time: 45 minLevel: Intermediate Every time Spotify recommends the next song from a band you’ve never heard of, it uses a recommendation algorithm based on other users’ interactions with that song. This type of algorithm is known as collaborative filtering. Unlike content-based recommendations, collaborative filtering excels when the objects’ semantics are loosely or unrelated to users’ preferences. This ada...| Qdrant - Vector Database
Configuring Qdrant Operator: Advanced Options The Qdrant Operator has several configuration options, which can be configured in the advanced section of your Hybrid Cloud Environment. The following YAML shows all configuration options with their default values: # Additional pod annotationspodAnnotations:{}# Configuration for the Qdrant operator service monitor to scrape metricsserviceMonitor:enabled:false# Resource requests and limits for the Qdrant operatorresources:{}# Node selector for the ...| Qdrant - Vector Database
Using FastEmbed with Qdrant for Vector Search Install Qdrant Client and FastEmbed pipinstall"qdrant-client[fastembed]>=1.14.2" Initialize the client Qdrant Client has a simple in-memory mode that lets you try semantic search locally. fromqdrant_clientimportQdrantClient,modelsclient=QdrantClient(":memory:")# Qdrant is running from RAM. Add data Now you can add two sample documents, their associated metadata, and a point id for each. docs=["Qdrant has a LangChain integration for chatbots.","Qdr...| Qdrant - Vector Database
Load and Search Hugging Face Datasets with Qdrant Hugging Face provides a platform for sharing and using ML models and datasets. Qdrant also publishes datasets along with the embeddings that you can use to practice with Qdrant and build your applications based on semantic search. Please let us know if you’d like to see a specific dataset! arxiv-titles-instructorxl-embeddings This dataset contains embeddings generated from the paper titles only. Each vector has a payload with the title used ...| Qdrant - Vector Database
Managing a Qdrant Cluster The most minimal QdrantCluster configuration is: apiVersion:qdrant.io/v1kind:QdrantClustermetadata:name:qdrant-a7d8d973-0cc5-42de-8d7b-c29d14d24840labels:cluster-id:"a7d8d973-0cc5-42de-8d7b-c29d14d24840"customer-id:"acme-industries"spec:id:"a7d8d973-0cc5-42de-8d7b-c29d14d24840"version:"v1.11.3"size:1resources:cpu:100mmemory:"1Gi"storage:"2Gi" The id should be unique across all Qdrant clusters in the same namespace, the name must follow the above pattern and the clust...| Qdrant - Vector Database
Permission Reference This document outlines the permissions available in Qdrant Cloud. --- 💡 When enabling write:* permissions in the UI, the corresponding read:* permission will also be enabled and non-actionable. This guarantees access to resources after creating and/or updating them. Identity and Access Management Permissions for users, user roles, management keys, and invitations. PermissionDescription read:rolesView roles in the Access Management page. write:rolesCreate and modify rol...| Qdrant - Vector Database
Build a Hybrid Search Service with FastEmbed and Qdrant Time: 20 minLevel: BeginnerOutput: GitHub This tutorial shows you how to build and deploy your own hybrid search service to look through descriptions of companies from startups-list.com and pick the most similar ones to your query. The website contains the company names, descriptions, locations, and a picture for each entry. As we have already written on our blog, there is no single definition of hybrid search. In this tutorial we are co...| Qdrant - Vector Database
Agentic RAG Discord ChatBot with Qdrant, CAMEL-AI, & OpenAI Time: 45 minLevel: Intermediate Unlike traditional RAG techniques, which passively retrieve context and generate responses, agentic RAG involves active decision-making and multi-step reasoning by the chatbot. Instead of just fetching data, the chatbot makes decisions, dynamically interacts with various data sources, and adapts based on context, giving it a much more dynamic and intelligent approach.| Qdrant - Vector Database
Backups To create a one-time backup, create a QdrantClusterSnapshot resource: apiVersion:qdrant.io/v1kind:QdrantClusterSnapshotmetadata:name:"qdrant-a7d8d973-0cc5-42de-8d7b-c29d14d24840-snapshot-timestamp"labels:cluster-id:"a7d8d973-0cc5-42de-8d7b-c29d14d24840"customer-id:"acme-industries"spec:cluster-id:"a7d8d973-0cc5-42de-8d7b-c29d14d24840"retention:1h You can also create a recurring backup with the QdrantClusterScheduledSnapshot resource: apiVersion:qdrant.io/v1kind:QdrantClusterScheduledS...| Qdrant - Vector Database
Using Qdrant’s Async API for Efficient Python Applications Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI have embraced this new paradigm, but it is also becoming a standard for ML models served as SaaS. For example, the Cohere SDK provides an async client next to its synchronous counterpart. Databases are often launched as separate services and are accessed via a network. All the interactions with them are IO-bound and can be performed asyn...| Qdrant - Vector Database
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How to Get Started with Qdrant Locally In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query. Download and run First, download the latest Qdrant image from Dockerhub: docker pull qdrant/qdrant Then, run the service: docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z"\ qdrant/qdrant Under the default configuration all data will be stored in the ./qdrant_storage directory. This will also be th...| Qdrant - Vector Database
Configuring Logging & Monitoring in Qdrant Private Cloud Logging You can access the logs with kubectl or the Kubernetes log management tool of your choice. For example: kubectl -n qdrant-private-cloud logs -l app=qdrant,cluster-id=a7d8d973-0cc5-42de-8d7b-c29d14d24840 Configuring log levels: You can configure log levels for the databases individually through the QdrantCluster spec. Example: apiVersion:qdrant.io/v1kind:QdrantClustermetadata:name:qdrant-a7d8d973-0cc5-42de-8d7b-c29d14d24840labels...| Qdrant - Vector Database
Measure and Improve Retrieval Quality in Semantic Search Time: 30 minLevel: Intermediate Semantic search pipelines are as good as the embeddings they use. If your model cannot properly represent input data, similar objects might be far away from each other in the vector space. No surprise, that the search results will be poor in this case. There is, however, another component of the process which can also degrade the quality of the search results. It is the ANN algorithm itself.| Qdrant - Vector Database
Configuring Networking, Logging & Monitoring in Qdrant Hybrid Cloud Configure network policies For security reasons, each database cluster is secured with network policies. By default, database pods only allow egress traffic between each and allow ingress traffic to ports 6333 (rest) and 6334 (grpc) from within the Kubernetes cluster. You can modify the default network policies in the Hybrid Cloud environment configuration: qdrant:networkPolicies:ingress:- from:- ipBlock:cidr:192.168.0.0/22- ...| Qdrant - Vector Database
Scaling PDF Retrieval with Qdrant Time: 30 minLevel: IntermediateOutput: GitHub Efficient PDF documents retrieval is a common requirement in tasks like (agentic) retrieval-augmented generation (RAG) and many other search-based applications. At the same time, setting up PDF documents retrieval is rarely possible without additional challenges. Many traditional PDF retrieval solutions rely on optical character recognition (OCR) together with use case-specific heuristics to handle visually comple...| Qdrant - Vector Database
Interfaces Qdrant supports these “official” clients. Note: If you are using a language that is not listed here, you can use the REST API directly or generate a client for your language using OpenAPI or protobuf definitions. Client Libraries Client RepositoryInstallationVersion Python + (Client Docs)pip install qdrant-client[fastembed]Latest Release JavaScript / Typescriptnpm install @qdrant/js-client-restLatest Release Rustcargo add qdrant-clientLatest Release Gogo get github.com/qdrant/g...| Qdrant - Vector Database
API Reference Packages qdrant.io/v1 qdrant.io/v1 Package v1 contains API Schema definitions for the qdrant.io v1 API group Resource Types QdrantCloudRegion QdrantCloudRegionList QdrantCluster QdrantClusterList QdrantClusterRestore QdrantClusterRestoreList QdrantClusterScheduledSnapshot QdrantClusterScheduledSnapshotList QdrantClusterSnapshot QdrantClusterSnapshotList QdrantEntity QdrantEntityList QdrantRelease QdrantReleaseList ClusterPhase Underlying type:string Appears in: QdrantClusterStat...| Qdrant - Vector Database
Changelog 1.7.0 (2025-05-14) qdrant-kubernetes-api versionv1.16.3 operator version2.4.2 qdrant-cluster-manager versionv0.3.5 Add optional automatic shard balancing Set strict mode by default for new clusters to only allow queries with payload filters on fields that are indexed 1.6.4 (2025-04-17) qdrant-kubernetes-api versionv1.15.5 operator version2.3.4 qdrant-cluster-manager versionv0.3.4 Fix bug in operator Helm chart that caused role binding generation to fail when using watch.namespaces 1...| Qdrant - Vector Database
Qdrant Hybrid Cloud: Hosting Platforms & Deployment Options This page provides an overview of how to deploy Qdrant Hybrid Cloud on various managed Kubernetes platforms. For a general list of prerequisites and installation steps, see our Hybrid Cloud setup guide. This platform specific documentation also applies to Qdrant Private Cloud. Akamai (Linode) The Linode Kubernetes Engine (LKE) is a managed container orchestration engine built on top of Kubernetes. LKE enables you to quickly deploy an...| Qdrant - Vector Database
Build a GraphRAG Agent with Neo4j and Qdrant Time: 30 minLevel: IntermediateOutput: GitHub To make Artificial Intelligence (AI) systems more intelligent and reliable, we face a paradox: Large Language Models (LLMs) possess remarkable reasoning capabilities, yet they struggle to connect information in ways humans find intuitive. While groundbreaking, Retrieval-Augmented Generation (RAG) approaches often fall short when tasked with complex information synthesis. When asked to connect disparate ...| Qdrant - Vector Database
Installation requirements The following sections describe the requirements for deploying Qdrant. CPU and memory The preferred size of your CPU and RAM depends on: Number of vectors Vector dimensions Payloads and their indexes Storage Replication How you configure quantization Our Cloud Pricing Calculator can help you estimate required resources without payload or index data. Supported CPU architectures: 64-bit system: x86_64/amd64 AArch64/arm64 32-bit system: Not supported Storage For persist...| Qdrant - Vector Database
Multilingual & Multimodal Search with LlamaIndex Time: 15 minLevel: BeginnerOutput: GitHub Overview We often understand and share information more effectively when combining different types of data. For example, the taste of comfort food can trigger childhood memories. We might describe a song with just “pam pam clap” sounds. Instead of writing paragraphs. Sometimes, we may use emojis and stickers to express how we feel or to share complex ideas.| Qdrant - Vector Database
How to Generate Sparse Vectors with SPLADE SPLADE is a novel method for learning sparse text representation vectors, outperforming BM25 in tasks like information retrieval and document classification. Its main advantage is generating efficient and interpretable sparse vectors, making it effective for large-scale text data. Setup First, install FastEmbed. pipinstall-qfastembed Next, import the required modules for sparse embeddings and Python’s typing module. fromfastembedimportSparseTextEmb...| Qdrant - Vector Database
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
The first comparative benchmark and benchmarking framework for vector search engines and vector databases.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech
We benchmarked several vector databases using various configurations of them on different datasets to check how the results may vary. Those datasets may have different vector dimensionality but also vary in terms of the distance function being used. We also tried to capture the difference we can expect while using some different configuration parameters, for both the engine itself and the search operation separately. Updated: January 2024| qdrant.tech
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.| qdrant.tech