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Meilisearch Index Sync

Keep a Meilisearch index current so search, filters, and sorting reflect the live data.

involved Integrations

What it adds

A background sync that mirrors app records into a Meilisearch index, with attribute configuration, batched updates, and a swap-based rebuild.

What your agent is told to do

5
  1. 1

    Declare the searchable, filterable, and sortable attributes as configuration that is applied before the first query relies on them, and re-apply it as part of deploy or rebuild. An attribute that is queried but not configured will silently return nothing useful.

  2. 2

    Batch changes through the app's existing background-job system and treat every write as asynchronous: record the task the engine returns, poll it to completion, and only mark the record synced once the engine confirms it. Do not assume a successful submission means the document is queryable.

  3. 3

    Compose the tenant and permission constraint on the server for every search and never merge a filter expression supplied by the client. A user who can inject a filter clause can read outside their workspace.

  4. 4

    Perform schema and attribute changes by indexing into a second index and swapping it into place once it is fully populated, so queries are never served by a half-built index.

  5. 5

    Typesense Index Sync is the same feature for a different engine, and the surrounding search UI, ranking, and result rendering belong to the app's existing search screens. This brief owns only the pipeline from record to index; do not rebuild the search interface here.

Edge cases it handles

8
  • Queries that filter or sort on an attribute the index was never configured for return misleading results rather than an error, so verify the attribute configuration at boot and fail loudly when it drifts from what the code expects.
  • Tenant isolation has to live outside anything the user controls. Filters assembled from query parameters, or passed straight through from the browser, are the standard way this leaks across customers.
  • Because updates are processed asynchronously, a user who saves and immediately searches may not see their own change. Track the engine's task state, retry the ones that fail, and surface a short indexing delay rather than pretending the write was instant.
  • Deletes and permission changes must remove documents, not merely stop linking to them. A record that is unshared, archived, or soft-deleted while its index entry survives is still discoverable by title and snippet.
  • Schema changes must be zero-downtime: build the replacement index alongside the live one, verify counts and sample queries, then swap, keeping the old index available to swap back.
  • Batches that exceed the engine's payload or document size limits must be split, and a single oversized document must be truncated or skipped with a logged reason instead of failing its whole batch.
  • Rate limiting and a queue backlog after an outage need bounded retries with exponential backoff and jitter, plus a dead-letter path for documents that fail repeatedly.
  • When the engine is down, degrade to a database-backed query with a visible notice about reduced result quality. An empty page reads to the user as an absence of data.

Definition of done

9
  • Searchable, filterable, and sortable attributes are configured declaratively and verified before queries depend on them.
  • Record changes reach the index through background jobs whose asynchronous tasks are tracked to completion.
  • Tenant scoping is enforced server-side and no client-supplied filter expression reaches the engine.
  • Deleted records and records the user has lost access to disappear from results promptly.
  • An index rebuild or schema change swaps in without search downtime and can be reverted.
  • Failed and oversized documents land in a dead-letter path with a reason instead of blocking the queue.
  • An engine outage degrades to database search with an explicit notice rather than an empty state.
  • The feature matches the existing design system.
  • No existing functionality is broken.

Related features

How it works

  1. 1

    Copy the link

    Grab the Markdown instruction URL for this feature.

  2. 2

    Give it to your AI

    Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.

  3. 3

    It inspects, then implements

    Your agent reads your existing app first, then adds the feature to fit it.

Works with your stack

These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.

Need it tighter than that? Customize the feature and tell it exactly what you're running.