FileMaker 2025 continues to expand its AI capabilities with support for semantic search across not just text—but also images and files. With new vector and embedding functions, developers can now create smarter, context-aware searches that surface the most relevant content, whether it’s a paragraph in a PDF or a specific image stored in a container field.
This evolution transforms FileMaker from a traditional database into a true semantic data platform, where meaning—not just matching words—guides search results.
What Are Embeddings and Vectors?
At the core of semantic search are embeddings—mathematical representations of meaning. Text, images, and even file content can be converted into vector form, allowing FileMaker to compare their similarity based on context instead of keywords.
For example, a search for “eco-friendly packaging” could return:
Product descriptions mentioning “sustainable materials”
A PDF datasheet for recyclable containers
Images tagged with “biodegradable”
Even though those results don’t use the exact search term, FileMaker understands they’re conceptually related.
Searching Across Images, PDFs, and More
With vector and embedding functions, FileMaker 2025 can now perform semantic search on multiple content types, including:
Images: Find visually similar photos or product shots.
Documents: Locate PDFs or text files with related topics.
Notes and Descriptions: Match records based on concept, not wording.
This makes it possible to unify data that previously lived in silos—connecting written content, visuals, and supporting documents in one intelligent search interface.
Real-World Use Cases
Manufacturing: Locate quality control photos related to specific defect reports.
Healthcare: Retrieve reference materials or patient documents that align with a particular case.
Legal: Find similar contracts or clauses based on meaning rather than keywords.
Creative Industries: Search image libraries by theme, emotion, or visual style.
By understanding relationships across different data types, FileMaker enables more intuitive and efficient information retrieval.
Why It Matters
Semantic search with embeddings helps organizations:
Discover related insights faster, even in large or unstructured datasets
Reduce time spent hunting through files, folders, or records
Provide a unified, intelligent search experience for all content
Keep all search processing within the secure FileMaker environment
This means more time spent acting on insights—and less time searching for them.
FileMaker 2025 pushes search beyond keywords with AI-powered semantic capabilities that span text, images, and files. With vector and embedding functions, developers can build truly intelligent apps that surface the right content, every time—no matter the format.
Interested in exploring how semantic search can enhance your FileMaker solutions? Reach out to Kyo Logic here.