16th October, 2023 | Meghana Denduluri
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In the vast realm of databases, a new player has emerged that promises to revolutionize the way we think about data retrieval: vector databases. As data continues to grow exponentially, traditional databases sometimes struggle to keep up, especially when it comes to complex search queries. Enter vector databases, which offer a unique approach to data storage and retrieval. In this blog post, we'll dive deep into what vector databases are and how they work.
At its core, a vector database is a storage system that uses vectors (mathematical representations of data) to store and retrieve information. Instead of relying on traditional relational models or key-value pairs, vector databases use high-dimensional vectors to represent data points. This allows for more efficient and accurate similarity searches.
Vectors are mathematical constructs that can represent data in multi-dimensional space. By converting data into vectors, we can leverage the power of linear algebra and distance metrics to determine the similarity between different data points. This is particularly useful for tasks like image or text retrieval, where traditional methods might fall short.
The first step in using a vector database is to convert your data into vectors. This is typically done using machine learning models or algorithms that can transform raw data (like text or images) into a numerical format.
Once data is vectorized, it's indexed in the database. The indexing process organizes vectors in a way that makes it efficient to perform similarity searches. Various algorithms, such as HNSW (Hierarchical Navigable Small World) or Annoy, can be used for this purpose.
When you want to retrieve data, you first convert your query into a vector (just like with the data). The database then searches for vectors that are close (or similar) to the query vector. The result is a list of data points ranked by their similarity to the query.