Qdrant uses a specialized method called Hierarchical Navigable Small World (HNSW) graphs to organize and index vectors. This approach allows for quick and efficient searches to find items that are most similar in complex, multi-dimensional data.
In addition to storing the vectors themselves, Quadrant keeps related information, known as metadata, alongside each vector. This metadata can be used to narrow down search results based on specific criteria, such as tags, categories, or dates.
By combining similarity searches with these filters, Quadrant enables powerful and flexible queries that go beyond simple matching.
Qdrant is designed to facilitate large-scale, reliable machine learning and data science operations. Some of the most common cases to use it are:
- Indexing embeddings of documents or user queries to perform semantic search and retrieve the most relevant results.
- Powering recommendation engines, which suggest products or content by analyzing user preferences or behavioral embeddings. Usually, embeddings can represent different user preferences or features for certain items. So, Quadrant is able to compare the vectors in order to find items that are similar to the user’s preferences.
- Supporting AI chatbots using Retrieval-Augmented Generation (RAG) to deliver context-aware responses by embedding past dialogue or external documents. It’s important to know that Qdrant allows users to store embedded pieces of documents, and can recover only the details that are most relevant.
- Detecting anomalies by applying vector clustering to identify outliers in time-series or event-driven data. So, when certain time series are directly converted using the autoencoders, users can group all of them in order to allow Qdrant to detect irregularities.
- Enabling multimodal search by combining text, images, and metadata for reach, hybrid search experiences. This feature represents the perfect choice for users who conduct visual product searches, or in the medical field when it is used for medical image retrieval.
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