# Order Rasa to train a new model through HTTP API

**URL:** <https://forum.rasa.com/t/order-rasa-to-train-a-new-model-through-http-api/19536>\
**Category:** Rasa Open Source\
**Created:** [October 10, 2019, 2:10am UTC](https://forum.rasa.com/t/order-rasa-to-train-a-new-model-through-http-api/19536 "2019-10-10T02:10:39Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![fuih](https://avatars.discourse-cdn.com/v4/letter/f/d78d45/32.png) [@fuih](https://forum.rasa.com/u/fuih)\
**Post date:** [October 10, 2019, 2:10am UTC](https://forum.rasa.com/t/order-rasa-to-train-a-new-model-through-http-api/19536/1 "2019-10-10T02:10:39Z")

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Hello,

I have some questions about training a new model through HTTP API. In the document, it shows the request samples: application/json

```
{
    "domain": "intents:\n - greet\n - goodbye\n - affirm\n - deny\n - mood_great\n - mood_unhappy\n\nactions:\n - utter_greet\n - utter_cheer_up\n - utter_did_that_help\n - utter_happy\n - utter_goodbye\n\ntemplates:\n utter_greet:\n - text: \"Hey! How are you?\"\n\n utter_cheer_up:\n - text: \"Here is something to cheer you up:\"\n image: \"https://i.imgur.com/nGF1K8f.jpg\"\n\n utter_did_that_help:\n - text: \"Did that help you?\"\n\n utter_happy:\n - text: \"Great carry on!\"\n\n utter_goodbye:\n - text: \"Bye\"",
    "config": "language: en\npipeline: supervised_embeddings\npolicies:\n - name: MemoizationPolicy\n - name: KerasPolicy",
    "nlu": "## intent:greet\n- hey\n- hello\n- hi\n## intent:goodbye\n- bye\n- goodbye\n- have a nice day\n- see you\n## intent:affirm\n- yes\n- indeed\n## intent:deny\n- no\n- never\n## intent:mood_great\n- perfect\n- very good\n- great\n## intent:mood_unhappy\n- sad\n- not good\n- unhappy",
    "stories": "## happy path\n* greet\n\n - utter_greet\n\n* mood_great\n\n - utter_happy\n\n## sad path 1\n* greet\n\n - utter_greet\n\n* mood_unhappy\n\n - utter_cheer_up\n\n - utter_did_that_help\n\n* affirm\n\n - utter_happy\n\n## sad path 2\n* greet\n\n - utter_greet\n\n* mood_unhappy\n\n - utter_cheer_up\n\n - utter_did_that_help\n\n* deny\n\n - utter_goodbye\n\n## say goodbye\n* goodbye\n\n - utter_goodbye",
    "force": false,
    "save_to_default_model_directory": true
}

```

So if i understand correctly, the source has to store the domain, config, nlu,… files and read their content, pass it to the request body and send it to Rasa ? What if i want to keep those files at Rasa side, can i send the request to tell Rasa to train a new model with default paths to those files like it normally does ? I intent to update the nlu files at Rasa side. Which approach is better ?:

- Store training files and update them at the source, read their content and pass it to the request like above.
- Store training files and update them at Rasa side, send the request to tell Rasa to train a new model with default file paths (if possible).

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<div class="post-metadata">

**Author:** ![Tobias\_Wochinger](https://dub1.discourse-cdn.com/flex013/user_avatar/forum.rasa.com/tobias_wochinger/32/4580_2.png) [@Tobias\_Wochinger](https://forum.rasa.com/u/Tobias_Wochinger)\
**Post date:** [October 10, 2019, 7:42am UTC](https://forum.rasa.com/t/order-rasa-to-train-a-new-model-through-http-api/19536/2 "2019-10-10T07:42:58Z")

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> What if i want to keep those files at Rasa side, can i send the request to tell Rasa to train a new model with default paths to those files like it normally does ?

You can do that with Rasa X. The normal Rasa server does not manage training data - you have to supply it.

> Store training files and update them at Rasa side, send the request to tell Rasa to train a new model with default file paths (if possible).

Security wise I think that’s not a good approach. You could tell the server to process any file at the OS and potentially embed that in the model.

So if you don’t use Rasa X to manage the training data, I’d suggest the first approach.