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.
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...
In 4D applications, large documents are commonplace: financial reports, internal guidelines, technical manuals… Searching for an exact keyword often i...
Your users don’t think in filenames or folder hierarchies. They think in ideas. “A robot painted in watercolor.” “A sunny beach filled with colo...
In many business applications, users enter or receive unstructured text: customer feedback, internal notes, support tickets, reports, and more. This c...
In many 4D business applications, documents are everything — technical notes, reports, manuals, internal guides. But when users can’t remember the exa...
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