Search

Vector Search is a new set of features that power your Generative AI applications.

search of already indexed vectors.

It works by converting the documents and the query into vectors, and then computing the cosine similarity between the query vector and the document vectors.

Enter vector search: a game-changing approach that captures semantic meaning, not just literal text.

vector search.

Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.

Vector Search: when you upload a rejection letter to the app, BG-AppealAI converts it to the vector, this is called the vectorization process.

First, it embeds the query vector using the same model used when populating the database. Then, we use an SQL query to perform the vector search.

Created an embedding vector database with hundreds of sustainability reports to be open-sourced to the broader community after the event.

There will also be a demo of InterSystems IRIS Vector Search that you can use in your projects.

Hi Community, Let's meet at the online meetup with the winners of the Vector Search, GenAI and ML Contest.

Project Vector Search IntegratedML Embedded Python LLM AI or LangChain Suggest a new idea Find a bug in Vector Search, IntegratedML or Embedded Python

Community, Enjoy the new video in a series of videos created by our Project Managers to highlight some of the interesting features of the new 2025.1 release: ⏯ RAG with built-in Vector

Learn how IRIS Vector Search works in practice. Explore how RAG can improve the reliability and relevance of answers provided by Vector Search.

Well, in our example the context is given by the set of ICD-10 diagnoses and to use them we must first vectorize them. How to vectorize our list of diagnoses?