4D AI: Sorting Query Results by Vector Similarity
In the Semantic search: querying by vector similarity post, we introduced how to query entities using vector similarity, a powerful way to find the most relevant records based on meaning rather than exact values.
4D 21 R2 comes with a new way to sort query results using 4D.Vector fields. You can not only filter entities by similarity but also sort your query results using the .query() function so that the most relevant and semantically closest entities appear first. Whether you’re building an intelligent search experience, a recommendation engine, or an AI-driven assistant, this new feature ensures that your results are ordered by relevance, providing more accurate and user-friendly outcomes.
Give AI to a 30 years old 4D application
In just a couple of years, Artificial Intelligence has gone from an emerging trend to an essential component of modern software. ChatGPT, Grok, Gemini, and other AI assistants now play a major role in everyone’s daily life, both professionally and personally.
That’s why 4D 21 introduces 4D.Vectors and 4D AI Kit: to give 4D developers simple, effective tools to add AI-powered features to their applications.
We’ve already shared many examples, tutorials, and webinars about AI, but I recently wondered: what would it take to bring AI into a 30-year-old 4D application?
Could you simply ask such an app for your top 10 customers and instantly get a nice chart back?
Well, guess what? It turned out to be so straightforward that it deserves its own blog post.
4D AIKit: Structured Outputs
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 interface, automating business logic, or orchestrating multi-step reasoning, predictable, machine-readable responses are essential.
That’s why 4D 21’s 4D AIKit introduces the new response_format attribute, letting you define the exact structure of the model’s output to ensure consistency, validation, and smooth integration into your app logic.
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().
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.
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.
Intelligent 4D Write Pro document analysis with AI
In many business applications, users enter or receive unstructured text: customer feedback, internal notes, support tickets, reports, and more. This content represents valuable information, but it’s difficult to leverage without specific processing.
This is where artificial intelligence becomes a powerful tool: by automatically analyzing the written content of a 4D Write Pro document, it can extract useful metadata for understanding, sorting, or prioritizing.
In this demonstration, we’ve implemented a complete scenario of automatic analysis of 4D Write Pro documents using AI. From a simple text, the AI is capable of:
- Generating a concise title reflecting the content
- Identifying the tone (positive, negative, informative, urgent…)
- Suggesting classification tags
- Evaluating the document’s writing quality
The goal is clear: automatically enrich documents with usable metadata, without changing the user experience.
AI Brings Magical Search to 4D Write Pro Documents
In many 4D business applications, documents are everything — technical notes, reports, manuals, internal guides. But when users can’t remember the exact wording, finding the right one becomes slow, frustrating, or worse — impossible.
With 4D 20 R10, semantic search powered by AI vectors changes that. Instead of matching keywords, you match meaning. Users get the right document, even if they search in different words or a different language. It’s a smarter way to surface the knowledge hidden in your documents — fast, accurate, and built for how people actually search.
Let’s consider a concrete example: a user wants to locate a technical note that explains how to insert an image into a 4D Write Pro document. However, they may not recall the precise phrase used in the document.
SHOWCASE: SMART EXPENSE REPORTING WITH AI
You know the pain of expense reporting — not for you, for your users. The receipts piling up, the manual entry, the typos that slip through. With 4D AIKit, that grind is gone. A simple upload turns a paper receipt or invoice into clean, structured JSON, ready for your database.
No more wasted time on totals, dates, or vendor names. Vision AI reads it, language models structure it, and 4D ties it straight into your app. From paper to database in seconds — and your users never feel the friction.
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