Apache NLPCraft is a Java-based open source library for adding a natural language interaction interface to any applications. It can connect with any private or public data source, and has no hardware or software lock-in. It is based on advanced intent-based matching of the input utterances. You can build intents for NLPCraft using any JVM-based languages like Java, Scala, Kotlin, Groovy, etc. NLPCraft exposes REST APIs for integration with end-user applications that can be written in any language or system.
One of the key features of NLPCraft is its use of deterministic intent matching that is tailor made for domain-specific natural language interface. It doesn't force developers to use direct deep learning approach that involves time consuming model training or corpora development - resulting in much simpler & faster implementation.
Another key aspect of NLPCraft is its initial focus on processing English language. Although it may sound counterintuitive, this narrower initial focus enables NLPCraft to deliver unprecedented ease of use combined with unparalleled comprehension capabilities for English input out-of-the-box. It avoids watered down functionality and overly complicated configuration, training and usage. English language is spoken by more than a billion people on this planet and is de facto standard global language of the business and commerce.
So, how does it work in a nutshell?
When using NLPCraft you will be dealing with three main components:
NLPCraft employs model-as-a-code approach where entire data model is an implementation of NCModel Java interface that can be developed using any JVM programming language like Java, Scala, Kotlin or Groovy. Data model defines named entities, various configuration properties as well as intents that use defined named entities. Model-as-a-code natively supports any software lifecycle tools and frameworks in Java ecosystem.
Typically, declarative portion of the model will be stored in a separate JSON or YAML file for simpler maintenance. There are no practical limitation on how complex or simple a model can be, or what other tools it can use. Data models use comprehensive intent-based matching.
To use data model it has to be deployed into a data probe.
Data probe is a light-weight container designed to securely deploy and manage data models. Each probe can deploy and manage multiple models and many probes can be connected to the REST server (or a cluster of REST servers). The main purpose of the data probe is to separate data model hosting from managing REST calls from the clients. While you would typically have just one REST server, you may have multiple data probes deployed in different geo-locations and configured differently.
Data probes can be deployed and run anywhere as long as there is an ingress connectivity from the REST server, and are typically deployed in DMZ or close to your target data sources: on-premise, in the cloud, etc. Data probe uses strong 256-bit encryption and ingress only connectivity for communicating with the REST server.
REST server (or a cluster of REST servers behind a load balancer) provides URL endpoint for end-user applications to securely query data sources using natural language via data models deployed in data probes. Its main purpose is to accept REST-over-HTTP calls from end-user applications and route these requests to and from requested data probes.
Unlike data probe that gets restarted every time the model is changed, i.e. during development, the REST server is a "fire-and-forget" component that can be launched once while various data probes can continuously reconnect to it. It can typically run as a Docker image locally on premise or in the cloud.