How does a vector database work?

[sumaiyakhatun26]

We all know how relational databases : store strings, numbers, and other types of scalar data in rows and columns. On the other hand, a vector database operates on vectors, so the way it is optimized and queried is quite different.

In traditional databases , we typically query for rows in the database where the value typically matches our query exactly. In vector databases , we apply a similarity metric to find the vector that is most similar to our query.

A vector database uses a combination of several algorithms that all participate in the austria rcs data nearest neighbor search (ANN). These algorithms optimize the search through hashing, quantization, or graph-based search.

These algorithms are assembled into a pipeline that provides fast and accurate retrieval of neighbors of the queried vector. Since the database vector provides approximate results, the main trade-off we consider is between accuracy and speed. The more precise the result, the slower the query. However, a good system can provide ultra-fast search with near-perfect accuracy.

Indexing : It is an algorithm for indexing the database vectors such as PQ, LSH or HNSW. This step maps the vectors into a data structure that will allow for fast searching.
Query : It compares the indexed query vector with the indexed query vector in the dataset to find the nearest neighbors of the database vector (by applying the similarity metric used by that index).
Post-processing : In some cases, the database retrieves the final nearest neighbors from the vector dataset and post-processes them to return the final results. This step may include reclassifying the nearest neighbors using a different similarity measure.