There is no universally correct daily or weekly refresh schedule for a RAG knowledge graph. Set the cadence by how quickly source information changes, how long answers may safely remain stale, and how much time and cost ingestion adds. Use reliable source-change events where possible; otherwise, poll or run batches frequently enough to meet a defined freshness objective.
Start with the freshness your application needs
Define the maximum acceptable delay between a change in a source and that change appearing in answers. A frequently changing policy or operational record may need faster propagation than a relatively stable reference collection. The consequence of an outdated answer matters as much as the source’s change rate.
This is an operational decision rule, not a schedule published by Microsoft or Google. Microsoft GraphRAG provides an update command for an existing knowledge-graph index and lists standard and fast update methods, but does not prescribe an interval. Google Cloud documents an event-driven ingestion architecture, not a universal freshness requirement.
Choose an update pattern
| Pattern | When it fits | What to watch |
|---|---|---|
| Source-change events | Use when the source reliably emits events or change notifications and you need changes processed close to their arrival. | Missed events, deletions, processing failures, and the work needed to reconcile the graph against its source. |
| Polling | Use when event delivery is unavailable but the source can be checked on a recurring basis. | Set the interval to fit the freshness objective; measure actual end-to-end lag and processing cost. |
| Scheduled batches | Use when changes can wait for a defined processing window or batching is operationally simpler. | Confirm that the longest wait plus processing time stays within the acceptable delay. |
Google Cloud’s reference architecture demonstrates event-triggered processing: new data is ingested, a message is sent, and a processing function builds and stores the graph and embeddings. That pattern can reduce delay, but it is not mandatory for every system. If event delivery can omit changes or deletes, add a periodic reconciliation pass; that is a general reliability measure rather than a cadence specified by the architecture.
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When events are not practical, choose polling or batch frequency from the freshness objective rather than defaulting to “daily” or “weekly.” The cited documentation does not establish either as a generally correct interval.
Update changed material incrementally when appropriate
For ordinary additions, edits, and deletions, scoped updates can avoid processing an entire corpus each time—if the implementation supports them correctly. Track stable source IDs and detect which records changed so the pipeline can update affected graph material. Microsoft GraphRAG documents update methods, while research on incremental knowledge-graph construction discusses change detection and incremental updates for evolving data. Neither establishes a universal update cadence or guarantees that every system can safely update only a small portion of its graph.
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Broader changes to how the graph is constructed deserve separate treatment. A schema change, entity-extraction prompt change, embedding-model change, or indexing-logic change may affect derived output beyond newly changed source records. Decide whether targeted regeneration is sufficient or a broader rebuild is needed, and compare the resulting output before making the new index live. The right procedure depends on the system; the GraphRAG CLI’s update options do not enumerate these triggers.
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A schedule is useful only if the pipeline meets it. Record the source modification time, successful ingestion time, processing or queue lag, and failed updates. Alert when end-to-end lag exceeds your freshness objective. For critical queries, decide in advance what to do when the graph is stale or an update fails—for example, use a clearly identified fallback rather than silently treating old data as current.
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- Freshness: Measure the delay from source change to availability in answers, not just the time a job starts.
- Completeness: Check that additions, edits, and deletions are reflected, including after missed events or outages.
- Cost: Track ingestion, graph construction, and model-processing work at the cadence you choose.
- Traceability: Keep enough version information to determine which graph state answered a query.
- Recovery: Monitor failures and define how to retry or reconcile without leaving the graph indefinitely stale.
Google’s reference architecture describes logging and monitoring; the specific signals, alerts, and thresholds are design choices for each implementation. Microsoft warns that GraphRAG indexing can be expensive and recommends starting small. Its repository also describes the project code as a demonstration, not an officially supported Microsoft offering, so its behavior should not be read as a service-level commitment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Confirm that a knowledge graph is worth maintaining
Refresh policy is only part of the decision. Google describes GraphRAG as combining vector search with a knowledge-graph query and notes that conventional RAG may be appropriate when source data lacks complex interrelationships. If graph structure does not materially help answer the questions your application receives, its construction and ongoing maintenance may not be justified.
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For more context on graph-based retrieval and its trade-offs, see the 2024 GraphRAG survey.
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