4D NetKit: Simplifying OAuth 2.0 Redirects after authentication
4D NetKit just made redirecting users after OAuth 2.0 authentication easier with 4D 21. The OAuth2Provider class in 4D NetKit now allows real URLs for authenticationPage and authenticationErrorPage. This means you now have more redirection options after authentication, like a Qodly pages or HTTP Handlers. Whether the authentication succeeds or fails, you stay in control of the user experience with smooth, flexible redirection options.
Track, Audit, Optimize: Take Control of Your 4D Web Sessions
Since the introduction of scalable sessions, server-side session management has become an important component of modern 4D architectures. These sessions enable fine-tuned scalability for web applications, but also require stricter supervision to guarantee performance, stability, and license control. With 4D 21, you now have a comprehensive means of inspecting all open web sessions, whether they originate from REST connections, SOAP calls, or 4DACTION requests.
HTTPS Requests Now Support Windows Certificate Store
Starting with 4D 21, your HTTPS requests can now use a certificate stored in the Windows Certificate Store instead of one saved on disk. This is particularly useful when the client-side of HTTPS requests needs to use local certificates.
Semantic search: querying by vector similarity
With the growing importance of vector-based search in AI applications such as semantic search, recommendation engines, and natural language processing, 4D introduces native support for vector queries in the query() function. This enhancement brings vector similarity comparisons directly into the language of DataClass.query() and EntitySelection.query().
Take Control of Standard 4D Components
As a 4D developer, you often want full control over what gets included in your projects. Starting with 4D 21, you can now decide which components provided by 4D are part of your application.
4D 21 and AI Kit: Redefining how applications Think and Act
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 integrate AI into your applications.
Tool calling allows you to extend the model’s capabilities by registering your own methods or functions, which the AI can call automatically when relevant. This means that instead of manually handling every interaction, the chat helper automatically invokes your handlers, giving you both flexibility and control.
4D 21 Beta Starts Today
Beta testing for 4D 21 starts today—enabling you to run semantic vector queries directly in ORDA, use AI tool calling to expose 4D methods inside AI conversations with structured responses, and maintain 4D Remote sessions uninterrupted across network changes. If you’ve ever needed smarter queries, tighter AI integration, or more resilient client connections—this release delivers.
4D 20 R10 is Here
Introducing 4D 20 R10 – Available Now!
You don’t need more features. You need deeper ones. The kind that add meaning to your data. That catch mistakes before they happen. That react in real time, adapt to structure, and make intelligence feel native—not layered on.
4D 20 R10 does exactly that—richer context, tighter control, smarter defaults. It’s not just powerful. It’s purposeful.
Find the right spot in your 4D Write Pro document with AI
In 4D applications, large documents are commonplace: financial reports, internal guidelines, technical manuals… Searching for an exact keyword often isn’t enough. Scrolling through 30-page reports to find one paragraph is not only time-consuming but also error-prone. This is where AI can help.
The semantic approach based on vectors, introduced in 4D 20 R10, already makes it possible to find a relevant 4D Write Pro document even when different wordings are used (for example, “insert image” vs. “add picture”).
But what happens when a document spans multiple pages and covers various subtopics? Even if the entire text can be converted into a single vector, results are often better when we work at a finer scale. This is the idea behind chunking: splitting a document into coherent segments, each represented by its own vector.
This is precisely what allows us to go further: retrieving not only the right document, but also the exact passage that matches the search.
Search by Meaning, Not Metadata: Semantic Image Filtering with 4D.Vector
Your users don’t think in filenames or folder hierarchies. They think in ideas.
- “A robot painted in watercolor.”
- “A sunny beach filled with color.”
- “Something that feels like Mona Lisa… but from the future.”
It doesn’t matter if that idea comes from an image, a customer order, an email, or a 4D Write Pro document — the challenge is the same: how do you deliver results that match intent, not just keywords?
With 4D.Vector and 4D AI Kit, your application can finally make sense of meaning. In this post, we’ll illustrate it with semantic image similarity search. And here’s the key: we’re not really working with raw images at all — we’re working with their descriptions. The very same approach works for any kind of text data in your application.
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