What data for this category looks like
Data for this category primarily originates from WAF, IDS, firewalls, and threat intelligence platforms. Daily generated documents include rule change tickets, intercept logs, false positive analysis reports, and policy rollback records. Data updates occur daily or when triggered by protection events. Document fields include rule ID, effective time, intercepted request count, false positive sample count, rollback operator, and others. Common units are entries, times, and pieces. Security operation engineers use this data to draft requirement and design documents for rule optimization and changes. This work supports iterative review of protection policies.
Constraints for context and token workflows
Documents for this category are mostly structured operational data. Drafting a single requirement document requires associating large volumes of historical rules, intercept logs, and false positive analysis content. This easily exceeds the model’s context window quota. Data updates happen frequently. Context must synchronize latest policy changes and event records in a timely manner. Using expired data to generate documents that do not match current protection status must be avoided. False positive analysis samples have fine granularity. Sufficient context details must be retained to support rule optimization requirements. Excessive historical data increases token consumption, leading to higher call costs or response timeouts. Most document fields are identifier content. Context recall ranges must be matched precisely. Unrelated data occupying token quotas must be avoided.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum context window | 8000–12000 token | Adapts to the model call limit of FastGPT v4.17.0, meets total token consumption requirements for multi-source logs and rule documents of security products |
maxContext | Calibrated based on the number of historical documents associated with the business | Matches the total length of rule changes and intercept logs required for security operations, avoids exceeding model limits |
| Maximum reference token | 2000–3000 characters | Ensures complete loading of single key log or rule document content, no truncation of core information |
quoteMaxToken | Adjusted based on the average length of false positive analysis samples | Covers typical text length of false positive samples, supports context requirements for rule optimization |
| Context recall count | Top 6 entries | Matches the common number of rule changes and intercept logs requiring daily review, balances information completeness and token consumption |
| Single call token limit | 15000 token | Adapts to total token consumption after multi-link document splicing, avoids triggering model call restrictions |
Three common configuration errors
- Phenomenon: Expired policy rollback records appear in generated requirement documents, with content inconsistent with the latest protection policies. Cause: No real-time context synchronization mechanism is configured. Calls use cached old data, and daily updated rule change tickets and intercept logs are not pulled.
- Phenomenon: Model call errors prompt token limit exceeded, or generated documents are forcibly truncated. Cause: Values of
maxContextandquoteMaxTokenare not set reasonably. Excessive historical rules and event data are loaded at one time, exceeding the model’s token quota. - Phenomenon: The online model runtime displayed on the interface only shows the first token latency, while offline models show total runtime, which does not match actual business statistics logic. Cause: Token calculation and latency statistics rules for online and offline models are confused. Configurations are not adjusted for the multi-source data scenario of security product rule operations.
How to confirm configurations are correctly set
- Verify configuration values match the data update frequency of the category. Ensure context synchronizes daily or real-time rule change and intercept log data.
- Run test calls. Check that generated requirement documents include the latest false positive analysis and policy rollback records, with no expired or missing core fields.
- Review model call token consumption statistics. Confirm that preset
maxContextandquoteMaxTokenlimits are not exceeded, and no frequent token overflow errors occur. - Compare latency display logic for online and offline models. Ensure it aligns with business statistics requirements for security product rule operations, with no abnormal display differences.
The values provided are common starting points and should be evaluated using sample data tailored to the specific deployment scenario.
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.