The chatbot replies data from all stores it's installed into. I think you need to apply a filter for the store the app is installed into instead of just 'active' filter. 15:22
Ah, we should've mentioned that at the beginning of the video - this build is for custom (single store) apps. You are absolutely correct, some changes would need to be made for a public/multi-store app. It would also be helpful to add a relationship between the `chatLog` data model and `shopifyShop`, and then add a `.gelly` filter in the access control page to help with multi-tenancy
Thank you for the tutorial. But when I add the gadget to my test shop, I can only prompt 1 sentence and receive a response, then it tells me that network error. What should I do?
Sorry to hear that there are some issues with the responses! You can always check the built-in Gadget Logs page or your browser web console to start debugging If you want some additional help, you can join our developer Discord (ggt.link/discord) we'd be happy to help debug the app - it's a bit easier to share code snippets and error messages
Is the direct script tag for the correct (the --development) environment? If you want help debugging, our Discord is the best place to ask and share code! ggt.link/discord
This video was shot on an older version of Gadget. We released Gadget v1.0 earlier this year, which updated the structure of the files. Backend files, including actions and models, are now in the API folder, and frontend code is in WEB. The most up-to-date version of this tutorial can be found in our docs here: docs.gadget.dev/guides/tutorials/ai-product-recommender
Great little tutorial, and jumping off point for creating a custom GPT for a clients store and using that instead of a default model. Thank you!
The chatbot replies data from all stores it's installed into. I think you need to apply a filter for the store the app is installed into instead of just 'active' filter. 15:22
Ah, we should've mentioned that at the beginning of the video - this build is for custom (single store) apps.
You are absolutely correct, some changes would need to be made for a public/multi-store app. It would also be helpful to add a relationship between the `chatLog` data model and `shopifyShop`, and then add a `.gelly` filter in the access control page to help with multi-tenancy
Please make a video on checkout page... show all discounts coupon code and apply button
Hi, have completed all the steps, my chatbot is visible on the page, but its not opening. please suggest what is to be done?
Hmmm. Hop into our Discord and ask in #code-help someone will be able to take a closer look:
discord.com/invite/fpCMTtyW2Z
Thank you for the tutorial. But when I add the gadget to my test shop, I can only prompt 1 sentence and receive a response, then it tells me that network error. What should I do?
Sorry to hear that there are some issues with the responses! You can always check the built-in Gadget Logs page or your browser web console to start debugging
If you want some additional help, you can join our developer Discord (ggt.link/discord) we'd be happy to help debug the app - it's a bit easier to share code snippets and error messages
Please, make a video setting up a local developemnt enviorment for shopify theme development.
This was a great suggestion, thanks!
We made a video for setting up a local theme dev env: th-cam.com/video/MY-KzAebqqc/w-d-xo.html
Great tutorial, but when I make the call to my chat endpoint, it is throwing 404. Is there a way to debug it?
Is the direct script tag for the correct (the --development) environment?
If you want help debugging, our Discord is the best place to ask and share code! ggt.link/discord
my gadget project has different files (no files folder)
This video was shot on an older version of Gadget. We released Gadget v1.0 earlier this year, which updated the structure of the files. Backend files, including actions and models, are now in the API folder, and frontend code is in WEB.
The most up-to-date version of this tutorial can be found in our docs here: docs.gadget.dev/guides/tutorials/ai-product-recommender