Qdrant

Qdrant is a vector database that stores and searches high-dimensional data using advanced technology. Unlike traditional databases that store exact text or numbers, Qdrant works with vectors—mathematical representations of complex information. This makes it perfect for finding similar items based on meaning rather than exact matches.

The database uses a special algorithm called HNSW that makes searches incredibly fast, even with billions of data points. It supports different ways to measure similarity between items, including options for text, images, and other data types.

Qdrant includes features like data compression to save memory, filtering to narrow down results, and the ability to scale up as your needs grow. You can use it through a simple web interface or integrate it directly into your applications using various programming languages.

Getting started with Qdrant is straightforward. Here are the steps to begin:

• Sign up for free: Visit the Qdrant website and create a free account. You get a 1GB cluster without needing a credit card. This is enough for testing and small projects.

• Install locally: For development work, you can run Qdrant on your computer using Docker. Just one command downloads and starts the database on your machine.

• Create a collection: Collections hold your vectors. Choose the size of your vectors and which distance formula to use. Common choices include cosine similarity for text and euclidean distance for images.

• Add your data: Upload vectors along with any extra information you want to store. You can include details like text, categories, or timestamps.

• Search and filter: Run searches to find similar items. You can combine similarity search with filters to get precise results based on your criteria.

• Fast vector similarity search with HNSW indexing

• Written in Rust for speed and reliability

• Multiple distance metrics (cosine, dot product, euclidean)

• Advanced filtering and full-text search

• Quantization for memory efficiency

• Horizontal and vertical scaling

• Cloud-managed and self-hosted options

• Integration with popular AI frameworks

• Real-time data updates

• High availability and disaster recovery

• API support for Python, JavaScript, Rust, Go

• Open-source with active community