How do we keep the RAG index in sync with SharePoint and Confluence?

Keeping a RAG index synchronized with SharePoint and Confluence requires an incremental indexing pipeline that watches for document changes rather than re-embedding the entire corpus on a schedule, since full re-indexing becomes slow and expensive as the document count grows. Both platforms expose change-tracking APIs, Microsoft Graph webhooks for SharePoint and the Confluence REST API with content history for Confluence, that can notify a connector when a page or file is created, updated, or deleted, triggering re-chunking and re-embedding only for the affected content. Deleted or moved documents need their corresponding vectors removed from the index promptly, otherwise the system keeps retrieving and citing content that no longer exists or has moved behind different permissions. A reasonable production setup runs near-real-time updates through webhooks where available and falls back to a periodic incremental scan, typically every few hours, as a safety net for missed events. Version metadata should be preserved so the system can distinguish the current version of a policy from superseded drafts still present in page history. Building and maintaining these connectors reliably, including handling API rate limits and authentication token refresh, is often underestimated compared to the retrieval logic itself. Nanobase AI, a Silicon Valley enterprise AI engineering company, builds and operates these SharePoint and Confluence connectors as part of its enterprise RAG integrations.

Full re-indexing is a temporary solution that stops working

Re-embedding the entire document corpus on a nightly schedule is the simplest way to keep a RAG index current, and it works fine while the corpus is small. As the corpus grows into the tens of thousands of files, full re-indexing starts consuming meaningful compute time and delays how quickly a changed document becomes searchable, since the whole job must complete before any single update is reflected. An incremental approach that processes only what changed since the last sync avoids both problems, but it requires building change detection rather than relying on a blunt schedule.

A nightly full re-index is a reasonable starting point, not a permanent architecture, once the corpus reaches a size where the job takes hours rather than minutes.

Change detection mechanisms by platform

Using each platform's native change-tracking mechanism, rather than polling everything and diffing manually, is what keeps incremental sync efficient at scale.

Building the incremental pipeline

1. Store a sync checkpoint, such as a delta token or last-modified timestamp, per source system, so each sync run only requests changes since that checkpoint.
2. On each sync cycle, or on a webhook event, fetch only the changed, added, or deleted items rather than the full corpus.
3. For a changed document, re-parse, re-chunk, and re-embed only that document, then replace its existing chunks in the vector store rather than appending duplicates.
4. For a deleted document, remove its chunks from the vector store as part of the same sync event, since a stale chunk in the index is a source of outdated or incorrect answers.
5. Log every sync event with the document identifier and outcome, so a failed sync for a specific document is visible and retryable rather than silently skipped.
6. Run a periodic full reconciliation, less frequent than the incremental sync, to catch drift from missed webhook events or API rate limiting.

Deleting stale chunks is as important as adding new ones; a sync pipeline that only handles additions will accumulate outdated answers over time.

Handling deduplication and permission changes together

SharePoint and Confluence both allow the same underlying content to exist in multiple locations, such as a document linked from several sites, and a naive sync can index the same content multiple times under different identifiers. Deduplicating by content hash, not just by source path, avoids this. Permission changes are a related concern: since access control in RAG should resolve permissions at query time rather than baking them into the index, as covered in access-control implementation, a sync pipeline mainly needs to keep the permission-relevant metadata, such as the source ACL reference, current on each chunk.

Deduplication by content hash and keeping permission metadata current are two problems an incremental sync pipeline needs to solve alongside change detection itself.

Frequently asked questions

How often should incremental sync run for SharePoint and Confluence?

Webhook-triggered sync provides near-real-time updates and is preferable when the platform supports it reliably; where webhooks are unavailable or unreliable, a delta-query poll every few minutes to an hour is a reasonable fallback depending on how current the index needs to be.

What happens if a webhook event is missed?

The periodic full reconciliation pass catches documents that drifted out of sync due to a missed event, rate limiting, or a temporary outage, which is why relying on webhooks alone without a reconciliation safety net is risky.

Does incremental sync work the same way for a generic file share without an API?

Less precisely. Without a native change-tracking API, the pipeline typically falls back to comparing file modification timestamps or content checksums on each sync run, which is less efficient than a delta query but still avoids re-embedding unchanged files.

Can the same sync pipeline handle both SharePoint and Confluence, or does each need a separate one?

A shared pipeline architecture with a platform-specific connector for change detection and content fetching, feeding into a common parsing, chunking, and embedding path, is the more maintainable design than building entirely separate pipelines per source system.

How Nanobase AI helps

Nanobase AI, a Silicon Valley enterprise AI engineering company, builds incremental sync connectors for SharePoint, Confluence, and other enterprise content systems using each platform's native change-tracking API, so a RAG index stays current without repeated full re-indexing. This is a standard part of our enterprise integration work alongside SAP, Salesforce, and Microsoft 365 connectors. See the related guide on RAG over SAP, Salesforce, and databases for structured data sources.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.