Add it to your Cargo.toml:

[dependencies]
snips-nlu-lib = { git = "https://github.com/snipsco/snips-nlu-rs", branch = "master" }

Add extern crate snips_nlu_lib to your crate root and you are good to go!

The purpose of the main crate of this repository, snips-nlu-lib, is to perform an information extraction task called intent parsing.

Let’s take an example to illustrate the main purpose of this lib, and consider the following sentence:

"What will be the weather in paris at 9pm?"

Properly trained, the Snips NLU engine will be able to extract structured data such as:

{
   "intent": {
      "intentName": "searchWeatherForecast",
      "confidenceScore": 0.95
   },
   "slots": [
      {
         "value": "paris",
         "entity": "locality",
         "slotName": "forecast_locality"
      },
      {
         "value": {
            "kind": "InstantTime",
            "value": "2018-02-08 20:00:00 +00:00"
         },
         "entity": "snips/datetime",
         "slotName": "forecast_start_datetime"
      }
   ]
}

In order to achieve such a result, the NLU engine needs to be fed with a trained model (json file). This repository only contains the inference part, in order to produce trained models please check the Snips NLU python library.

The interactive parsing CLI is a good example of to how to use snips-nlu-rs.

Here is how you can run the CLI example:

$ git clone https://github.com/snipsco/snips-nlu-rs
$ cd snips-nlu-rs
$ cargo run --example interactive_parsing_cli data/tests/models/nlu_engine

Here we used a sample trained engine, which consists in two intents: MakeCoffee and MakeTea. Thus, it will be able to parse queries like "Make me two cups of coffee please" or "I'd like a hot tea".

As mentioned in the previous section, you can train your own nlu engine with the Snips NLU python library.

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT) or http://opensource.org/licenses/MIT)

at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.