Configuring the HTTP API

Using Rasa’s HTTP API

Note

The instructions below are relevant for configuring how a model is run within a Docker container or for testing the HTTP API locally. If you want to deploy your assistant to users, see Deploying your Rasa Assistant.

You can run a simple HTTP server that handles requests using your trained Rasa model with:

rasa run -m models --enable-api --log-file out.log

All the endpoints this API exposes are documented in HTTP API.

The different parameters are:

-m: the path to the folder containing your Rasa model,
--enable-api: enable this additional API, and
--log-file: the path to the log file.

Rasa can load your model in three different ways:

Fetch the model from a server (see Fetching Models from a Server), or
Fetch the model from a remote storage (see Cloud Storage).
Load the model specified via -m from your local storage system,

Rasa tries to load a model in the above mentioned order, i.e. it only tries to load your model from your local storage system if no model server and no remote storage were configured.

Fetching Models from a Server

You can configure the HTTP server to fetch models from another URL:

rasa run --enable-api --log-file out.log --endpoints my_endpoints.yml

The model server is specified in the endpoint configuration (my_endpoints.yml), where you specify the server URL Rasa regularly queries for zipped Rasa models:

models:
  url: http://my-server.com/models/default@latest
  wait_time_between_pulls: 10   # [optional](default: 100)

Note

If you want to pull the model just once from the server, set wait_time_between_pulls to None.

Note

Your model server must provide zipped Rasa models, and have {\"ETag\": <model_hash_string>} as one of its headers. Rasa will only download a new model if this model hash has changed.

Rasa sends requests to your model server with an If-None-Match header that contains the current model hash. If your model server can provide a model with a different hash from the one you sent, it should send it in as a zip file with an ETag header containing the new hash. If not, Rasa expects an empty response with a 204 or 304 status code.

Configuring SSL / HTTPS

By default the Rasa server is using HTTP for its communication. To secure the communication with SSL, you need to provide a valid certificate and the corresponding private key file.

You can specify these files as part of the rasa run command:

rasa run --ssl-certificate myssl.crt --ssl-keyfile myssl.key

If you encrypted your keyfile with a password during creation, you need to add this password to the command:

rasa run --ssl-certificate myssl.crt --ssl-keyfile myssl.key --ssl-password mypassword

Security Considerations

We recommend not to expose the Rasa Server to the outside world, but rather connect to it from your backend over a private connection (e.g. between docker containers).

Nevertheless, there are two authentication methods built in:

Token Based Auth: Pass in the token using --auth-token thisismysecret when starting the server:

rasa run \
    -m models \
    --enable-api \
    --log-file out.log \
    --auth-token thisismysecret

Your requests should pass the token, in our case thisismysecret, as a parameter:

$ curl -XGET localhost:5005/conversations/default/tracker?token=thisismysecret

JWT Based Auth: Enable JWT based authentication using --jwt-secret thisismysecret. Requests to the server need to contain a valid JWT token in the Authorization header that is signed using this secret and the HS256 algorithm.

Endpoint Configuration

To connect Rasa to other endpoints, you can specify an endpoint configuration within a YAML file. Then run Rasa with the flag --endpoints <path to endpoint configuration.yml>.

For example:

rasa run \
    --m <Rasa model> \
    --endpoints <path to endpoint configuration>.yml