Use embeddings and vectors in ORDA to interpret intent, enable semantic search, and automate business logic directly inside your 4D applications without external glue code.
Vector data is at the heart of modern AI-powered features: semantic search, recommendation engines, RAG (Retrieval-Augmented Generation) pipelines, an...
If you missed our recent webinars, now is a good time to catch up. These sessions go beyond product overviews. They show practical ways to bring AI, ...
AI is increasingly present in our applications. It can generate complex JSON, extract data from documents, or provide structured responses. But there ...
As AI becomes a core part of your applications, you increasingly rely on embeddings, vector similarity, and AI-driven features. One recurring challeng...
Large reports, contracts, or documentation files often contain more information than can be quickly processed. Wouldn’t it be nice if you could simply...
In the Semantic search: querying by vector similarity post, we introduced how to query entities using vector similarity, a powerful way to find the mo...
In just a couple of years, Artificial Intelligence has gone from an emerging trend to an essential component of modern software. ChatGPT, Grok, Gemini...
When using AI in your application, you often need outputs that your code can parse, not just free-form text. Whether you’re generating data for a user...
With the growing importance of vector-based search in AI applications such as semantic search, recommendation engines, and natural language processing...
With 4D 21, AI takes a giant leap forward. At the heart of this evolution is AI Kit’s tool calling, a massive addition that transforms the way you int...
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