This article is the first in a series of five that will dive into the intricacies of vector search, also known as semantic search, and how it is implemented in OpenSearch and Elasticsearch. This first part focuses on providing a general introduction to the basics of embedding vectors and how...
Vector search is now part of the Hazelcast unified platform, enabling new use cases like semantic search, fraud detection and RAG. In addition, support for ingesting unstructured data has been added to the Pipeline API, making it simple to construct scalable, robust embedding pipelines and run th...
"key": SearchKey, "indexName": SearchIndex, "topNDocuments": 1, "queryType": "semantic", "semanticConfiguration": "", "fieldsMapping": None, "inScope": True, "roleInformation": "", "vectorQueries": [ { "vector": [], "k": 3, "fields": "", "kind": "" } ] } }...
This support comes in the form of a new capability in Oracle Database 23ai called “AI Vector Search.” It includes vectors as a native data type as well as vector indexes and vector search SQL operators, which together make it possible to store the semantic content of unstructured data as...
Are you using large language models, but they only know information up until 2021? Do you want it to get with the times?! Well then, vector search may be just what you’re looking for. What is vector search? Vector search is a capability that allows you to do semantic search where yo...
Semantic search and content discovery.Powering semantic search is another common use case because keyword-based searches struggle with synonyms, but vector similarity addresses this by capturing meaning. Natural language processing (NLP).Vector search is central to the use ofNLP, which is used in mul...
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...
Image and video retrieval operations employ vector databases for similarity and semantic searches that quickly pinpoint images or videos amid deep catalogs of options. Advantages of Vector Databases Vector databases offer many advantages, including fast similarity search. They are optimized for efficient ...
For instance, you can combine filters, text search with semantic ranking, and vectors into a single query operation. Vector fields must be type: Collection(Edm.Single) with dimensions and vectorSearchProfile properties. The vectorSearch section is an array of approximate nearest neighbor algorithm ...
Semantic searchallows for systems to be able to capture the deeper semantic meaning of words and text. In modern search engines, if someone searches for "tips for planting in spring," it tries to understand the intent and contextual meaning behind the query. It doesn’t try just matching the...