Vektorközvetítés

Vektorközvetítés, often translated as vector mediation or vector bridging, is a concept primarily found in the realm of vector databases and their use in artificial intelligence, particularly in retrieval-augmented generation (RAG) systems. It describes the process by which a vector database acts as an intermediary, facilitating the retrieval of relevant information based on semantic similarity between a query and stored data.

In essence, when a user poses a query, this query is transformed into a vector representation. This query vector is then used to search the vector database, which contains numerous other data points also represented as vectors. The vector database's core function is to efficiently find vectors that are "close" to the query vector in a high-dimensional space, indicating semantic relatedness.

The "mediation" aspect comes into play as the vector database doesn't directly answer the question. Instead, it retrieves a set of relevant data chunks or documents whose vector representations are most similar to the query vector. These retrieved chunks are then passed on to a generative AI model, such as a large language model (LLM). The LLM then uses this contextual information, provided through vector mediation, to formulate a more accurate, informed, and relevant response to the original query. This process significantly enhances the capabilities of LLMs by grounding their responses in specific, retrieved data, mitigating issues like hallucinations and improving factual accuracy.