Every developer is familiar with SQL databases as they are used in every single application. While SQL databases store data in rows, column-oriented databases store data in columns, resulting in faster query performance for analytical workloads. They are optimized for data aggregation, compression, and efficient column-level operations, making them well-suited for analytics and reporting tasks. However, traditional databases, such as relational SQL databases, often struggle to scale effectively when dealing with massive data or high concurrent access environments. In fact, performing vector searches with SQL databases can be challenging due to their focus on structured data and traditional indexing techniques. They can face limitations regarding storage capacity, query performance, and distributed handling.
In this blog, we will explore the growth of vector databases and how they have revolutionized the analysis of unstructured data.
Understanding vector search
Vector search is a technique to find similar vectors or points in a high-dimensional space. Simply put, a natural Vector search is a mechanism for finding the most similar examples based on some neural embeddings of neural Vector representations. It involves measuring the similarity or distance between vectors based on their geometric properties. By comparing the characteristics of vectors, such as embeddings or feature vectors, to a query vector, vector search enables tasks like nearest neighbor search, similarity matching, and clustering. It finds applications in recommendation systems, image and video retrieval, natural language processing, and anomaly detection. With Vector search, users can actually solve the problems of synonyms and problems related to multilingual search, unlike a simple keyword-based search.
Introducing vector databases
One of the best applications of vector databases is geo coordinates. Vector databases are well-suited for storing and querying geo coordinates. These databases enable efficient similarity search and spatial queries by representing latitude and longitude as vectors. They support tasks like finding nearest neighbors, location-based recommendations, and geospatial analysis, making them valuable tools for applications dealing with geospatial data.
Difference between keyword-based search and vector databases search
Keyword-based search relies on textual matching, where queries are matched against keywords or phrases within a document or dataset. It is effective for exact matches or text-based filtering but struggles with semantic understanding, context, and similarity matching.
In contrast, the vector database search is based on mathematical representations of data points in high-dimensional spaces. It measures similarity or distance between vectors, enabling tasks like nearest neighbor search, similarity matching, and clustering. Vector databases excel at handling high-dimensional data, such as embeddings. They are particularly suited for recommendation systems, image and video retrieval, and natural language processing tasks requiring semantic understanding and similarity matching.
Benefits of vector database search
Vector database search offers numerous benefits in various domains. Firstly, it enables efficient similarity search, retrieving similar vectors based on distance or similarity metrics. This is particularly valuable in recommendation systems, content matching, and image retrieval applications. Secondly, vector database search enables high-dimensional data analysis, providing dimensionality reduction, clustering, and anomaly detection capabilities. Thirdly, it supports advanced search functionalities like semantic search and natural language understanding, enhancing the accuracy and relevance of search results. Lastly, vector database search enables real-time query processing, making it suitable for applications requiring fast response times, such as real-time analytics and personalized user experiences.
Teams that should switch to vector database
Teams that deal with high-dimensional data should consider switching to a vector database. It offers efficient similarity search, advanced search functionalities, and real-time query processing, enhancing performance and enabling better data analysis and decision-making. Here are a few examples of users who can benefit from using vector-based database search.
- Search team – Any organization that needs to have a search engine over their internal data must perform complex queries such as reverse image lookup, wherein the user uploads an image and perform the search, will benefit from using a vector database. Similarly, teams that must perform textual searches but struggle with different terminologies or languages can use vector databases.
- Data scientists – Teams can use vector databases to solve problems such as classification or regression. Unlike traditional systems with predefined classes, vector databases can address this concern through similarity search.
- Product team – These teams can use an existing model to build a working product within a few hours. They can also use vector search for anomaly detection using neural embeddings.
Qdrant – A vector database built for scale
Qdrant is a vector database that provides powerful indexing and querying capabilities for high-dimensional data. With Qdrant, you can efficiently store and search vectors, enabling tasks like similarity search, recommendation systems, and image retrieval. Its advanced indexing structures, like HNSW and IVFADC, ensure fast and accurate search operations. Qdrant is suitable for large-scale deployments and offers scalability, fault tolerance, and real-time querying capabilities. It supports various data types and provides a user-friendly API for seamless application integration. Qdrant is valuable for managing and extracting insights from high-dimensional vector data.
ChatGPT and Qdrant
Qdrant offers a ChatGPT plugin that enhances the functionality of OpenAI’s ChatGPT with vector search capabilities. This plugin allows ChatGPT to integrate with Qdrant’s high-performance vector database seamlessly. By leveraging the plugin, ChatGPT gains the ability to perform efficient similarity searches on large-scale vector data, such as recommendations, image retrieval, or content matching. The plugin provides an intuitive interface for querying vectors within the ChatGPT environment, enabling developers to incorporate vector search functionality into their conversational AI applications easily.
This blog is based on an interview with Kacper Łukawski, Developer Advocate at Qdrant. You can watch the full interview here.
