A Simple RasaNLU Http Server

Installation:

Follow the docs to install RasaNLU available here

To run:

git clone git@github.com:dtfiedler/simple-rasa-nlu.git
cd simple-rasa-nlu
python -m rasa_nlu.server --path projects/default

Example Request

Once you have the server running, you can do an example request to the following URL:

http://localhost:5000/parse?q=hello%20there&project=rasaBot

You should see the following response:

{
  "entities": [], 
  "intent": {
    "confidence": 1.0, 
    "name": "greet"
  }, 
  "text": "hello there"
}

Adding your own project

Steps:

1. Create your own training data using the site available here

2. Download the file using the "Download" button in top right as <PROJECT_NAME>-trainging-data.json and save it in the data directory

3. Update the config_spacy.json file to:

    {
        "fixed_model_name": "<PROJECT_NAME>",
        "pipeline": "spacy_sklearn",
        "path" : "./projects",
        "data" : "./data/PROJECT_NAME-training-data.json"
    }
   

4. Run the command (this trains Rasa based on your input)

python -m rasa_nlu.train -c config_spacy.json

5. This should create a new folder in the projects/default directory called <PROJECT_NAME>
6. Rerun your server by running the command:

python -m rasa_nlu.server --path projects/default

7. Test one of your example queries by going to

 http://localhost:5000/parse?q=<YOUR_SAMPLE_TEXT>&project=<YOUR_PROJECT_NAME>