Apache NLPCraft is a JVM-based open source library for adding a natural language interface to modern applications. It enables people to interact with your products using voice or text. NLPCraft can connect with any private or public data source, and has no hardware or software lock-ins. Its design is based on advanced Intent Definition Language (IDL) for defining non-trivial intents and a fully deterministic intent matching algorithm for 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.
One of the key features of NLPCraft is its use of IDL coupled with deterministic intent matching that are tailor made for domain-specific natural language interface. This design doesn't force developers to use direct deep learning approach with time consuming corpora development and model training - resulting in much a 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 academic, watered down functionality or overly complicated configuration and usage - following on project's "built for engineers by engineers" ethos. 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 a model-as-a-code approach where everything you do in NLPCraft is part of your source code. Data model is simply 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 to interpret user input. Model-as-a-code natively supports any software lifecycle tools and frameworks in Java ecosystem.
Declarative portion of the model can 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 intents to match the user input.
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 user 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.
Watch this full video (34:42) of the presentation from ApacheCon Asia 2021 conference to get in-depth understanding of the reasons why NLPCraft project was developed and what are the key principles that underlying it: