Add smart predictive search to your Bubble app that shows popular searches, recent queries, fuzzy matching for typos, and Did you mean suggestions. This tutorial covers logging search queries, building an autocomplete dropdown, implementing basic fuzzy matching, and displaying search suggestions from saved query data.
Overview: Adding Predictive Search in Bubble
Predictive search helps users find what they need faster by suggesting queries as they type. This tutorial builds a search system that shows recent searches, popular queries, and results matching partial input — making your search feel smart and responsive.
Prerequisites
- A Bubble account with an app that has searchable data
- Basic understanding of Repeating Groups and searches
- Familiarity with Custom States and conditional visibility
Step-by-step guide
Create a SearchQuery Data Type to log searches
Create a SearchQuery Data Type to log searches
Go to Data tab → Data types. Create SearchQuery with fields: Query (text), User (User), Searched_At (date), Result_Count (number). In your existing search workflow, add an action after the search executes: Create a new SearchQuery with the input value, current user, date, and result count. This logs every search for future suggestions.
Expected result: Every search is logged to the database for powering suggestions.
Build the autocomplete suggestion dropdown
Build the autocomplete suggestion dropdown
On your search page, add an Input element. Below it (overlapping slightly), add a Repeating Group with Type: text, visible only when the Input is focused and has at least 2 characters. Set the data source to a merged list: (1) Do a search for SearchQueries where Query starts with Input value, grouped by Query, sorted by count descending — this gives popular matching queries. (2) The actual content search: Do a search for Products where Name contains Input value:each item's Name. Display suggestions in the dropdown. When a suggestion is clicked, set the Input value to that suggestion and trigger the search.
Pro tip: Add a small delay (using a Do when condition is true event that fires 300ms after the input changes) to avoid querying on every keystroke.
Expected result: An autocomplete dropdown shows matching suggestions as the user types.
Show recent and popular searches
Show recent and popular searches
When the Input is focused but empty, show two sections: Recent Searches (Do a search for SearchQueries where User = Current User, sorted by date, limit 5, each item's Query) and Popular Searches (Do a search for SearchQueries grouped by Query, sorted by count descending, limit 5). Display these in the same dropdown Repeating Group with section headers. Clicking any suggestion fills the Input and triggers the search.
Expected result: Users see their recent searches and popular community searches when they focus the search input.
Implement basic fuzzy matching
Implement basic fuzzy matching
Bubble does not have built-in fuzzy search, but you can approximate it. Create a backend workflow that runs when a search returns zero results. The workflow searches for records where the Name or Title contains each word of the query individually (splitting multi-word queries). If partial matches are found, display them with a Did you mean banner above the results. For more advanced fuzzy search, integrate an external search service like Algolia or Typesense via the API Connector — they support typo tolerance natively. For apps needing production-grade search, RapidDev can help integrate dedicated search services.
Expected result: When a search returns no results, partial matches and Did you mean suggestions appear.
Add search analytics to improve suggestions over time
Add search analytics to improve suggestions over time
Create an admin dashboard showing: top searched queries (SearchQueries grouped by Query, sorted by count), searches with zero results (where Result_Count = 0 — these reveal content gaps), and search frequency over time. Use this data to add missing content and improve your search suggestions. Periodically clean up the SearchQuery table by deleting entries older than 90 days to prevent database bloat.
Expected result: An analytics view showing search patterns that helps you improve content and search quality.
Complete working example
1PREDICTIVE SEARCH — WORKFLOW SUMMARY2=====================================34DATA TYPE: SearchQuery5 Query (text), User (User), Searched_At (date), Result_Count (number)67SEARCH INPUT BEHAVIOR:8 Empty + focused: Show Recent + Popular suggestions9 2+ chars + focused: Show autocomplete matches10 Not focused: Hide dropdown1112AUTOCOMPLETE:13 Source: Merge of:14 1. SearchQueries (Query starts with input, grouped, sorted by count)15 2. Products (Name contains input):each item's Name16 Clicked: Set input value, trigger search1718SEARCH WORKFLOW:19 1. Search Products where Name contains Input value20 2. Log SearchQuery (query, user, date, result count)21 3. If zero results: show Did you mean suggestions2223DID YOU MEAN:24 Backend: Split query into words25 Search each word individually26 Show partial matches with suggestion banner2728ANALYTICS:29 Top queries: grouped by Query, sorted by count30 Zero-result queries: Result_Count = 031 Frequency: grouped by dateCommon mistakes when building predictive search in Bubble
Why it's a problem: Querying the database on every keystroke
How to avoid: Add a 300ms debounce using a Do when condition that checks if the input has not changed for 300ms.
Why it's a problem: Not cleaning up old search logs
How to avoid: Schedule a weekly backend workflow to delete SearchQuery records older than 90 days.
Why it's a problem: Showing the dropdown when the input is empty
How to avoid: Add conditions: dropdown visible only when Input is focused AND Input value character count >= 2.
Best practices
- Log every search to build suggestion data over time
- Show recent and popular searches when the input is empty and focused
- Debounce autocomplete queries to avoid firing on every keystroke
- Clean up old search logs periodically to prevent database bloat
- Track zero-result searches to identify content gaps
- Use grouped queries for popular suggestions to deduplicate results
- Consider external search services for production-grade fuzzy matching
Still stuck?
Copy one of these prompts to get a personalized, step-by-step explanation.
I want to add predictive search to my Bubble.io app that shows autocomplete suggestions, recent searches, and popular queries as users type. Help me design the data model and search logic.
Create a search input with an autocomplete dropdown that shows matching product names and popular past searches. When a suggestion is clicked, fill the input and run the search.
Frequently asked questions
Does Bubble support full-text search?
Bubble's text matching is limited to the first 256 characters and supports contains and starts with. For full-text search, integrate Algolia or Typesense via the API Connector.
How many search logs should I keep?
Keep 90 days of search logs. This provides enough data for suggestions and analytics without excessive database growth.
Can I show results as the user types without pressing Enter?
Yes. Bind the Repeating Group data source directly to a search using the Input's value as a constraint. Results update automatically as the input changes.
How do I handle searches with zero results?
Check if the search result count is 0 and display a Did you mean section with partial matches, or suggest broadening the search terms.
Can I weight certain results higher?
Add a Relevance or Priority number field to your content. Sort search results by this field descending so promoted content appears first.
Can RapidDev help implement advanced search?
Yes. RapidDev can integrate dedicated search services like Algolia for typo tolerance, faceted filtering, and instant search across large datasets.
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