What data for this category looks like
Data for this category originates from backup systems, database logs, object storage, and disaster recovery platforms. Produced documents include backup policies, recovery drill reports, disaster recovery switchover plans, and data verification records. Data update frequencies fall into three categories: daily, monthly, and quarterly. Different documents follow distinct update schedules. Each individual document includes fields such as backup timestamp, recovery point identifier, drill execution status, and data verification results. The primary units for these fields are timestamps, status codes, and numeric parameters. No additional custom statistical fields are included.
What constraints these characteristics impose on context and token processing
Multi-source data and layered update schedules for this category create multiple constraints for context and token handling. Multi-source, heterogeneous document formats require unified preprocessing to avoid wasting token quota. Documents with different update frequencies need differentiated loading priorities. Daily updated backup logs should be prioritized for context inclusion. Quarterly updated plans can serve as supplementary items. Individual documents have a large number of fields. Full direct import will exceed the large model token limit. Precise screening of fields related to disaster recovery drills and release notes is necessary. Additionally, token consumption varies widely across different scenarios. The context for the recovery point confirmation step needs reserved additional token space to carry complete verification data.
How to configure settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the average token consumption of core documents for this category, avoids exceeding the large model context limit |
quoteMaxToken | 2000–3000 token | Limits token usage for a single referenced document, prevents single data entries from excessively occupying context space |
| Single search limit | Top 6 entries | Matches the average recall volume for daily documents of this category, avoids interference from redundant data |
| Reranking switch | Enabled | Prioritizes filtering context content strongly related to disaster recovery drills and release notes |
| Maximum context duration | 7 days | Focuses on recent backup logs and drill reports, filters expired historical data |
Three common configuration errors
- Phenomenon: When
quoteMaxTokenis set above 3000 token, the knowledge base context preview displays as empty, and the large model cannot receive context content. Cause: The single-reference token limit is exceeded. The system automatically filters excess context data. - Phenomenon: After deploying the token service offline, an error occurs when calling related APIs: cl100k.tiktoken request failed. Cause: The local token model file is not configured. The system attempts to request external resources by default, causing call failures in offline environments.
- Phenomenon: When querying release notes for disaster recovery drills, recalled documents include historical records outside the allowed update frequency range. Cause: No rule to filter context by update time is set. Expired historical documents are recalled.
How to confirm the configuration is correct
- View the knowledge base context preview interface. Confirm that recalled document types relate to disaster recovery drills and release notes. Adjust filtering rules based on business scenarios.
- Initiate a simulated query. Check whether large model return results include core fields of backup policies and drill reports. Adjust token configuration parameters based on returned content.
- Check system operation logs. Confirm that no error messages for token request failures or context truncation appear. Optimize configurations based on error prompts.
- Simulate queries for different business scenarios. Confirm that recalled context matches current scenario requirements. Adjust context loading rules based on scenarios.
The values provided are common starting points. They 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.