What data for this category looks like
Data for this category comes from backup systems, database logs, object storage, and disaster recovery platforms. Four types of documents are produced regularly: backup policies, recovery drill reports, disaster recovery switchover plans, and data verification records. Update frequencies range from daily, monthly, to quarterly. Core fields include recovery point objective, recovery time objective, drill execution time, and data verification results. Field dimensions vary across different documents. Backup policies center on cycle and storage path. Recovery drill reports focus on execution steps and verification results. Disaster recovery switchover plans include process descriptions for cross-system switchover.
What constraints do these characteristics impose on the context and token workflow
Scattered data sources and widely varying document structures require that the context recall scope be limited to disaster recovery-related documents, to prevent mixing in irrelevant operational data. Differences in document update frequencies require that recently updated drill reports and verification records be prioritized during context calls. Strong associations between core fields require that field associations be retained when splicing context, to avoid token overflow. Cross-system recoverable attributes require that context cover associated data from multiple sources, which increases token quota usage pressure. This means the effective scope of context must be strictly controlled. FastGPT v4.17.0 context configuration must adapt to these characteristics, to prevent invalid data recall or token limit errors.
How to set configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 10000–15000 token | Dense field associations for category documents require retaining sufficient associated information while avoiding token overflow |
quoteMaxToken | 3000–5000 token | Single disaster recovery document content is lengthy, so token usage per single quote must be limited |
| Context recall scope | Only disaster recovery-related documents | Scattered data sources require excluding interference from irrelevant operational data |
| Update time filter threshold | Last 90 days | Category data update frequencies cover daily, monthly, and quarterly cycles, so prioritize recent drill reports and verification records |
| Single recall count | Top 3 entries | Associated data for core links is concentrated; too many recalls increase token usage and reduce accuracy |
Three common mistakes
- Phenomenon: Document classification results always match the default fallback category, with no classified results for associated disaster recovery documents. Cause: The context recall scope is not limited to disaster recovery-related documents, leading to recall of irrelevant operational data that interferes with classification logic.
- Phenomenon: System prompts for token overflow errors, or generated document content is truncated. Cause: Values for
maxContextandquoteMaxTokenare not set reasonably, leading to exceeding token quotas during context splicing. - Phenomenon: Historical data from recovery drill reports is not included in the context, and generated plan content lacks historical verification results. Cause: The update time filter threshold is set too short, failing to cover historical drill data that needs to be referenced.
How to confirm the configuration is correct
- View context recall logs to confirm only disaster recovery-related documents are recalled, with no data from other categories.
- Test token usage when splicing context, adjust values for
maxContextandquoteMaxTokento ensure no token overflow errors are triggered. - Import disaster recovery documents with different update times to verify whether recall results match the set update time filter threshold.
- Draft test documents, and check whether generated content includes expected historical drill reports and verification records.
The values provided are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.