Qdrant Vector DB

What is Qdrant DB?

Qdrant version 1.3 serves as an AI Vector Database and a search engine for vector similarity. Functioning as an API service, it facilitates the search for the closest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be transformed into comprehensive applications for tasks such as matching, searching, recommending, and beyond.

Qdrant Embeddings

Qdrant provides effortless integration with an embedding vector APIs, such as Cohere, Gemini, Jina Embeddings, OpenAI, Aleph Alpha, Fastembed, and AWS Bedrock.

Supported Vector Search Operations

Managed Cloud Deployment

Qdrant Managed Cloud is a SaaS (software-as-a-service) solution, that provides managed Qdrant database clusters on the cloud. Provides the same fast and reliable similarity search engine, but without the need to maintain your infrastructure.

Transitioning to the Managed Cloud version of Qdrant does not change how you interact with the service. All you need is a Qdrant Cloud account and an API key for each request. You can also attach your infrastructure as a Hybrid Cloud Environment. Here you can find a Cloud Deployment pricing calculator.

Hybrid Deployment

Qdrant Hybrid Cloud Seamlessly deploy and manage your vector database across diverse environments, ensuring performance, security, and cost efficiency for AI-driven applications. Qdrant Hybrid Cloud integrates Kubernetes clusters from any setting – cloud, on-premises, or edge – into a unified, enterprise-grade managed service. You can use Qdrant Cloud’s UI to create and manage your database clusters, while they still remain within your infrastructure. All Qdrant databases will operate solely within your network, using your storage and compute resources.

Development deployment using docker image

Deployment of self-hosted Docker Qdrant DB QuickStart, a dashboard is made available. Within this dashboard, users can monitor their collections, access related information, and perform various operations, including configuring their API key.

Within the dashboard, examples of collections include ‘books_collection’ and ‘startups_collection_example’:

When we use ‘startups_collection_example’ collection, now we can access additional details regarding the stored data items. This includes information such as the size of the embedding vectors.

Execute semantic search and query the self-hosted Qdrant DB instance by searching for startups that correlate with the query ‘bitcoin’ while incorporating a city filter for ‘New York’. Anticipate retrieving the most analogous data items to ‘bitcoin’, accompanied by a score assigned to each returned data item.

Qdrant Use Cases

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