Forespørselvektorer

Forespørselvektorer, sometimes translated as query vectors or request vectors, are a fundamental concept in the field of information retrieval and natural language processing, particularly in the context of vector databases and semantic search. They represent a user's query or a piece of text in a numerical format, allowing for mathematical comparison with other vector representations. This vector representation captures the semantic meaning of the query rather than just its literal keywords.

To generate a forespørselvektor, an embedding model, often a deep learning model like a transformer, is used. This model takes the input text (the query) and transforms it into a high-dimensional vector where dimensions correspond to learned semantic features. The closer two vectors are in this high-dimensional space, the more semantically similar their corresponding texts are considered to be.

When a forespørselvektor is created, it can be used to query a collection of document vectors stored in a vector database. The system then performs a similarity search, typically using metrics like cosine similarity or Euclidean distance, to find the document vectors that are closest to the forespørselvektor. The documents corresponding to these closest vectors are then returned as search results. This approach enables more nuanced and contextually relevant search results compared to traditional keyword-based methods.