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Semantic gap. Vector search relies on vector representations of items to calculate similarity. There can, however, be a gap between the vector representation and the actual attributes of an item. For example, two bikes might be semantically similar but have different vector representations due to ...
Demo: Oracle Database 23ai: Vector Search - Bring AI to your Data (2:43) Additional resources Tutorial: Fast and Precise Business and Semantic Data Search with AI Vector Search (56:48) Blog: Oracle Announces General Availability of AI Vector Search in Oracle Database 23ai Oracle Connect:...
Embeddings are a specific type of vector representation of content or a query, created by machine learning models that capture the semantic meaning of text or representations of other content such as images. Natural language machine learning models are trained on large amounts of data to identify ...
In the current AI landscape, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI.
Vector image search refers to a unique combination of vector-powered search services and OpenAI solutions that transform and enhance image search functionalities. Vectors and AI empower businesses to build personalizedimage retrieval systemswith enhanced semantic search capabilities. ...
With the recent success of LLMs, semantic search is a perfect way to showcase vector similarity search in action using cuVS. In the following example, aDistilBERTtransformer model combined with each of the three ANN indexes is used to solve a simple question retrieval problem. The Simple Engl...
Vector search calculates and uses nearest neighbor algorithms by transforming all data into vector embeddings. In its most basic form, avector embeddingis a mathematical representation of an object as a list of numbers. Once in this numerical representation, the semantic similarity of objects now bec...
This blog offers an introduction to vector search and some of the technology behind it such as vector embeddings and neural networks.