feat(platform): Builder search history

[kcze]

Preserve user searches in the new builder and cache search results for more efficiency.
Search is saved, so the user can see their previous searches.

Changes 🏗️

• Add BuilderSearch column&migration to save user search (with all filters)
• Builder db.py now caches all search results using @cached and returns paginated results, so following pages are returned much quicker
• Score and sort results
• Update models&routes
• Update frontend, so it works properly with modified endpoints
• Frontend: store serachId and use it for subsequent searches, so we don't save partial searches (e.g. "b", "bl", ..., "block"). Search id is reset when user clears the search field.
• Add clickable chips to the Suggestions builder tab
• Add HorizontalScroll component (chips use it)

Checklist 📋

For code changes:

• I have clearly listed my changes in the PR description
• I have made a test plan
• I have tested my changes according to the test plan:
  • Search works and is cached
  • Search sorts results
  • Searches are preserved properly

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✨ Finishing touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch kpczerwinski/secrt-1731-builder-search-history

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[deepsource-io]

Here's the code health analysis summary for commits 979d7c3..dc5d4ce. View details on DeepSource ↗.

Analysis Summary

Analyzer	Status	Summary	Link
JavaScript	✅ Success	❗ 5 occurences introduced	View Check ↗
Python	✅ Success	🎯 3 occurences resolved	View Check ↗

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[AutoGPT-Agent]

Thank you for your PR! Before we can proceed with merging, there are a few issues that need to be addressed:

1. Your PR description is missing information about the changes you've made. Please fill in the 'Changes' section to describe what your builder search history implementation does.

2. None of the items in the checklist have been checked off. Please complete the checklist by checking the boxes for items you've completed. At minimum:

   • Confirm you've clearly listed your changes
   • Indicate you've made a test plan
   • Note that you've tested your changes according to that plan (and include the test plan)

3. The diff appears to be empty in this review - if you've made code changes, please ensure they're properly committed and showing in the PR.

Once you've updated these items, we can proceed with a more thorough review of your implementation. The PR title looks good with the proper conventional commit format.

[AutoGPT-Agent]

Thank you for your contribution! There are a few items that need to be addressed before this PR can be merged:

1. The PR title mentions "feat(backend): Builder search history" but the changes are to a frontend wallet component. Please update your PR title to accurately reflect your changes (perhaps something like "fix(frontend): Wallet component condition check").

2. The PR description is empty. Please explain:

   • What issue this PR is solving
   • What specific changes you've made

3. The checklist needs to be completed - either check off the items or remove the checklist if the changes are not material (this appears to be a small bugfix).

Once these issues are addressed, we can proceed with the review. The code change itself looks straightforward, but we need proper documentation to understand its purpose.

[AutoGPT-Agent]

Thank you for your PR contribution. Before this can be merged, please update the following:

1. Ensure your PR title and labels are consistent. Your title mentions backend changes, but the PR is labeled as frontend.

2. Complete the PR description to explain the purpose of these changes and what they accomplish.

3. Fill out the checklist completely.

4. Ensure your PR includes the actual code changes (the diff is currently empty).

Once you've addressed these points, please request another review.

[AutoGPT-Agent]

Thank you for implementing the builder search history functionality! The code implementation looks good, but there are a few things that need to be addressed before this can be merged:

1. PR Description: Please complete the PR description by:

   • Explaining why these changes are needed (the purpose of the builder search history feature)
   • Listing the specific changes you've made (e.g., adding BuilderSearch model, implementing search history tracking and retrieval)

2. Checklist: The checklist in your PR description needs to be completed:

   • Check the box confirming you've listed your changes
   • Check the box confirming you've made a test plan
   • Fill out and check off a test plan with specific steps you've taken to test the functionality

The implementation itself looks solid - you've properly:

• Created a new Prisma model for BuilderSearch
• Added the migration script
• Implemented functions to update and retrieve search history
• Updated the suggestions endpoint to use real search history
• Properly used user_id in security-sensitive functions

Please update the PR description and checklist, and this should be ready for review again.

[AutoGPT-Agent]

Thank you for your PR implementing builder search history. The code looks well-structured and focused on the stated purpose, but there are a few issues to address before this can be approved:

Missing Items

1. Your PR description is missing an explanation of the changes and purpose of this PR. Please add a clear description of what this feature does and why it's needed.

2. The checklist in the PR description is incomplete. Please fill out the checklist including your test plan. Since this adds new functionality, a thorough test plan is essential.

Code Review

The code itself looks good overall:

• You've added a new BuilderSearch model to Prisma with appropriate migration
• The search history implementation with caching strategy seems well thought out
• Pagination handling is improved in both backend and frontend
• You've added user_id checks appropriately to secure routes

Please update your PR description with:

1. A clear explanation of the feature and its benefits
2. A complete checklist including your test plan

Once these are addressed, the PR should be ready for approval.

[AutoGPT-Agent]

Thank you for your PR implementing the builder search history feature. Before this can be merged, please update the PR description with the following:

1. Add a clear explanation of why these changes are needed
2. List the specific changes you've made in the PR
3. Complete the checklist by checking off the relevant items
4. Add a test plan explaining how you've verified the functionality

The code implementation looks good and matches the scope stated in the title. I can see you've:

• Added a new BuilderSearch model in Prisma with appropriate relationships
• Created migration for the new database table
• Implemented functions to track and retrieve search history
• Updated the search endpoint to work with persistent search history
• Modified frontend components to utilize the search history features

Once you update the PR description with the missing information, we can proceed with the review process.

[qodo-code-review]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Caching Key/Invalidation The cached search results function _build_cached_search_results uses user_id, search_query, filters, and by_creator as keys, but pagination is applied outside the cache in the route. Verify that cache invalidation occurs correctly when underlying data (new agents, updated agent names/descriptions, blocks changes) changes, and that large result sets don't cause memory pressure since full lists are cached for 5 minutes per unique key. @cached(ttl_seconds=300, shared_cache=True) async def _build_cached_search_results( user_id: str, search_query: str, filters: tuple[FilterType, ...], by_creator: tuple[str, ...], ) -> _SearchCacheEntry: normalized_query = (search_query or "").strip().lower() include_blocks = "blocks" in filters include_integrations = "integrations" in filters include_library_agents = "my_agents" in filters include_marketplace_agents = "marketplace_agents" in filters scored_items: list[_ScoredItem] = [] total_items: dict[FilterType, int] = { "blocks": 0, "integrations": 0, "marketplace_agents": 0, "my_agents": 0, } block_results, block_total, integration_total = _collect_block_results( normalized_query=normalized_query, include_blocks=include_blocks, include_integrations=include_integrations, ) scored_items.extend(block_results) total_items["blocks"] = block_total total_items["integrations"] = integration_total if include_library_agents: library_response = await library_db.list_library_agents( user_id=user_id, search_term=search_query or None, page=1, page_size=MAX_LIBRARY_AGENT_RESULTS, ) total_items["my_agents"] = library_response.pagination.total_items scored_items.extend( _build_library_items( agents=library_response.agents, normalized_query=normalized_query, ) ) if include_marketplace_agents: marketplace_response = await store_db.get_store_agents( creators=list(by_creator) or None, search_query=search_query or None, page=1, page_size=MAX_MARKETPLACE_AGENT_RESULTS, ) total_items["marketplace_agents"] = marketplace_response.pagination.total_items scored_items.extend( _build_marketplace_items( agents=marketplace_response.agents, normalized_query=normalized_query, ) ) sorted_items = sorted( scored_items, key=lambda entry: (-entry.score, entry.sort_key, entry.filter_type), ) return _SearchCacheEntry( items=[entry.item for entry in sorted_items], total_items=total_items, ) Scoring Threshold _should_include_item drops items when score < MIN_SCORE_FOR_FILTERED_RESULTS (10.0). With short queries (e.g., 1-2 chars) this may filter out too aggressively or conversely include too much when empty query returns 0 score but is allowed. Validate UX: empty query returns everything unsorted (score 0), but then sorted comparator uses -score first—ensure desired ordering for empty search and that short queries still surface expected items. def _should_include_item(score: float, normalized_query: str) -> bool: if not normalized_query: return True return score >= MIN_SCORE_FOR_FILTERED_RESULTS def _get_item_name(item: SearchResultItem) -> str: if isinstance(item, BlockInfo): return item.name.lower() Backward Compatibility The SearchResponse shape changed (added search_id, pagination; removed page and more_pages). Confirm all frontend consumers are updated. Also, the filter query now uses typed values; ensure OpenAPI clients and existing callers pass correct enums. "/search", summary="Builder search", tags=["store", "private"], response_model=builder_model.SearchResponse, ) async def search( user_id: Annotated[str, fastapi.Security(get_user_id)], search_query: Annotated[str | None, fastapi.Query()] = None, filter: Annotated[list[builder_model.FilterType] | None, fastapi.Query()] = None, search_id: Annotated[str | None, fastapi.Query()] = None, by_creator: Annotated[list[str] | None, fastapi.Query()] = None, page: Annotated[int, fastapi.Query()] = 1, page_size: Annotated[int, fastapi.Query()] = 50, ) -> builder_model.SearchResponse: """ Search for blocks (including integrations), marketplace agents, and user library agents. """ # If no filters are provided, then we will return all types if not filter: filter = [ "blocks", "integrations", "marketplace_agents", "my_agents", ] search_query = sanitize_query(search_query) # Get all possible results cached_results = await builder_db.get_sorted_search_results( user_id=user_id, search_query=search_query, filters=filter, by_creator=by_creator, ) # Paginate results total_combined_items = len(cached_results.items) pagination = Pagination( total_items=total_combined_items, total_pages=(total_combined_items + page_size - 1) // page_size, current_page=page, page_size=page_size, ) start_idx = (page - 1) * page_size end_idx = start_idx + page_size paginated_items = cached_results.items[start_idx:end_idx] # Update the search entry by id search_id = await builder_db.update_search( user_id, builder_model.SearchEntry( search_query=search_query, filter=filter, by_creator=by_creator, search_id=search_id, ), ) return builder_model.SearchResponse( items=paginated_items, search_id=search_id, total_items=cached_results.total_items, pagination=pagination, )

[kcze]

comments

[kcze]

I used private @dataclass because are more lightweight than pydantic model as this goes to cache

[Pwuts]

Sure, but any idea how much more lightweight and how this affects performance?

[kcze]

Scoring uses magic numbers for priorities - this need to be refined over time, so search works as best as possible.

[ntindle]

Backend looks fine, have another eval front end

[Pwuts]

can be more specific:
BuilderSearchHistory

[Pwuts]

are these records ever updated?