Rule-Based Extraction and Entity Recognition
Using rule-based extraction and enrichment, it is possible to easily and with a large amount of flexibility, extract different entities based on your own defined rules.
The rule-based extraction is available at the very heart of the Mindbreeze semantic pipeline – meaning that extracted entities can either be used to enrich the search experience or be used in combination with our other services to perform more complex tasks such as lookups or synthesize metadata.
Adding rule-based extraction is very simple within the configuration settings of your Mindbreeze InSpire management center. Pattern extraction rules in the form of regular expressions can be defined, and entities can be extracted from anywhere within the indexed document or from a specific location. The exact format of the extracted entity can also be defined.
Here is a quick video tutorial for a clearer and visual representation of what this looks like:
Curious to learn more about rule-based extraction and entity recognition? We have two previous blog posts that may help!
Latest Blogs
New features of Mindbreeze InSpire 26.4 Release
Want to check out the highlights of the Mindbreeze InSpire 26.4 Release? Learn more in the following blog post.
Memory in Agentic AI: Why Context Matters
When we talk about agentic AI, it’s easy to focus on reasoning and tool‑calling. But there’s another ingredient that’s just as important: memory. Without it, even the smartest model can only handle one interaction at a time.