Hi all,
we are working on a search feature that works well for users to swiftly filter our help directory.
I.e. enter the search word and display help cards >
select card and view detail pop-up.
The next two things we want to add:
- Highlight words from the search box input:
- Use voice plugin to store the result in database > then display results in RG (repeating group).
- highlight voice captured words
NOW THE CHALLENGE
We manage to get the result into the search input when users voice the search query. But instead of displaying the result in the RG. Nada.
There is a suggestion from the Zeroqode team to store the result in the database, use the result to display in the RG.
I wonder how to plan the database so that multiple users can use voice at the same time, or will we have issues with the number of queries we store?
Who can help us?
Just to gain clarity: what is the voice search searching?
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Thank you, Rico.
The data source is help + help details. That’s the help card and the help pop up with details.
Also the search is set to .7 i.e. returning the exact words in no particular order in both “help” (name, summary) and “help details” (description, steps, examples, and related help).
OK.
About challenge 2. What if you could do the voice search and NOT store the results in a database, would that work for you?
If I assume that it is OK not to save to the database, what I would do is to change the search in the data source of the repeating group.
Bubble’s built-in text search has improved a lot. But if you want more control, you can use the fuzzy search plug-in. @gregjohnkeegan has just released a tutorial on that: https://www.youtube.com/watch?v=yiMqvbeYvT0
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Oh wow — that sounds intriguing for now. I will try it out immediately.
For context — the reason why I believe I want to data base capabilities, so that we can analyse the most common search queries with Amplitude analytics. But there might be a way around that! For now, without data base capture 
Thank you so much and I will post the updates shortly.
Hi @rico.trevisan and @gregjohnkeegan — thanks to the quality of your help/content see here:
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