HTTP API ¶

Contents

HTTP API
- Endpoints

Endpoints ¶

`POST /parse`¶

You must POST data in this format '{"q":"<your text to parse>"}', you can do this with

$ curl -XPOST localhost:5000/parse -d '{"q":"hello there"}'

By default, when the project is not specified in the query, the "default" one will be used. You can (should) specify the project you want to use in your query :

$ curl -XPOST localhost:5000/parse -d '{"q":"hello there", "project": "my_restaurant_search_bot"}'

By default the latest trained model for the project will be loaded. You can also query against a specific model for a project :

$ curl -XPOST localhost:5000/parse -d '{"q":"hello there", "project": "my_restaurant_search_bot", "model": "<model_XXXXXX>"}'

You can post your training data to this endpoint to train a new model for a project. This request will wait for the server answer: either the model was trained successfully or the training exited with an error. Using the HTTP server, you must specify the project you want to train a new model for to be able to use it during parse requests later on : /train?project=my_project. The configuration of the model should be posted as the content of the request:

Using training data in json format:

language: "en"

pipeline: "spacy_sklearn"

# data contains the same json, as described in the training data section
data: {
  "rasa_nlu_data": {
    "common_examples": [
      {
        "text": "hey",
        "intent": "greet",
        "entities": []
      }
    ]
  }
}

Using training data in md format:

language: "en"

pipeline: "spacy_sklearn"

# data contains the same md, as described in the training data section
data: |
  ## intent:affirm
  - yes
  - yep

  ## intent:goodbye
  - bye
  - goodbye

Here is an example request showcasing how to send the config to the server to start the training:

$ curl -XPOST -H "Content-Type: application/x-yml" localhost:5000/train?project=my_project \
    -d @sample_configs/config_train_server_md.yml

Note

The request should always be sent as application/x-yml regardless of wether you use json or md for the data format. Do not send json as application/json for example.

Note

You cannot send a training request for a project already training a new model (see below).

Note

The server will automatically generate a name for the trained model. If you want to set the name yourself, call the endpoint using localhost:5000/train?project=my_project&model=my_model_name

`POST /evaluate`¶

You can use this endpoint to evaluate data on a model. The query string takes the project (required) and a model (optional). You must specify the project in which the model is located. N.b. if you don’t specify a model, the latest one will be selected. This endpoint returns some common sklearn evaluation metrics (accuracy, f1 score, precision, as well as a summary report).

$ curl -XPOST localhost:5000/evaluate?project=my_project&model=model_XXXXXX -d @data/examples/rasa/demo-rasa.json | python -mjson.tool

{
    "accuracy": 0.19047619047619047,
    "f1_score": 0.06095238095238095,
    "precision": 0.036281179138321996,
    "predictions": [
        {
            "intent": "greet",
            "predicted": "greet",
            "text": "hey",
            "confidence": 1.0
        },
        ...,
    ]
    "report": ...
}

`GET /status`¶

This returns all the currently available projects, their status (training or ready) and their models loaded in memory. also returns a list of available projects the server can use to fulfill /parse requests.

$ curl localhost:5000/status | python -mjson.tool

{
  "available_projects": {
    "my_restaurant_search_bot" : {
      "status" : "ready",
      "available_models" : [
        <model_XXXXXX>,
        <model_XXXXXX>
      ]
    }
  }
}

`GET /version`¶

This will return the current version of the Rasa NLU instance, as well as the minimum model version required for loading models.

$ curl localhost:5000/version | python -mjson.tool
{
  "version" : "0.13.0",
  "minimum_compatible_version": "0.13.0"
}

`GET /config`¶

This will return the default model configuration of the Rasa NLU instance.

$ curl localhost:5000/config | python -mjson.tool
{
    "config": "/app/rasa_shared/config_mitie.json",
    "data": "/app/rasa_nlu/data/examples/rasa/demo-rasa.json",
    "duckling_dimensions": null,
    "emulate": null,
    ...
  }

`DELETE /models`¶

This will unload a model from the server memory

$ curl -X DELETE localhost:5000/models?project=my_restaurant_search_bot&model=model_XXXXXX