# Configuring the HTTP API

## [Using Rasa’s HTTP API](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/configuring-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](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/how-to-deploy/#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](https://legacy-docs-v1.rasa.com/1.10.6/api/http-api/#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:

1. Fetch the model from a server (see [Fetching Models from a Server](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/configuring-http-api/#fetching-models-from-a-server)), or
2. Fetch the model from a remote storage (see [Cloud Storage](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/cloud-storage/#cloud-storage)).
3. 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](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/configuring-http-api/#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.

### [Configuring SSL / HTTPS](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/configuring-http-api/#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 key file 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](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/configuring-http-api/#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
```

**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](https://legacy-docs-v1.rasa.com/1.10.6/user-guide/configuring-http-api/#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>`.

**Note**: You can use environment variables within configuration files by specifying them with `${name of environment variable}`.