Vector databases have seen a surge in popularity, with numerous startups entering the space and investors showing interest. The rise of large language models and generative AI has created a favorable environment for vector database technologies to thrive.
Traditional relational databases like Postgres or MySQL are well-suited for structured data, but they struggle with unstructured data such as images, videos, and social media posts. Vector databases, on the other hand, store data in the form of vector embeddings, converting various data types into numerical representations to capture meaning and relationships between data points.
Vector databases are ideal for machine learning applications, as they store data spatially based on relevance, making it easier to retrieve semantically similar data. This spatial organization is particularly beneficial for large language models like OpenAI’s GPT-4, enabling better context understanding and facilitating real-time applications such as content recommendations.
Vector search can help reduce “hallucinations” in AI applications by providing additional information not present in the training dataset. According to Andre Zayarni, CEO of Qdrant, vector databases are essential for efficiently working with vector embeddings in large datasets.
