What this category of data looks like
This category of data mainly comes from the daily business outputs of cybersecurity level protection assessment institutions and security consulting firms. The core documents include four types: classification determination report, gap analysis report, rectification plan, and assessment report. Data is collected from assessment tools, interview records, system inventory, and security device logs. Data updates follow individual project cycles, with one full update completed per project cycle. Document structures are organized around the control items of cybersecurity level protection standards, and include fields such as control item number, current status description, rectification requirements, and evidence list.
What Constraints These Characteristics Impose on Context and Token Workflows
Core documents are a mix of structured and semi-structured content. The length of single project documents varies widely, leading to fluctuating context window usage. Data updated per project must be synchronized during each assessment cycle. If recall scope is not properly controlled, irrelevant historical project data will be introduced, increasing token consumption. The precise recall requirement for control item-related fields means the context should only retain valid data from the current project, avoiding interference from cross-project data. Additionally, frequent context call demands arise from high-frequency tasks such as sorting out classification materials and checking control item gaps, requiring a balance between recall accuracy and token usage.
How to Set the Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the average length of a single cybersecurity level protection project document, avoids exceeding the model's context limit for a single call, and adapts to the context processing logic of FastGPT v4.17.0 |
quoteMaxToken | 1500–2000 characters | Limits the token consumption of a single recalled document, prevents excessive context usage by a single data entry, and aligns with the content length range of a single control item in cybersecurity level protection documents |
| Single search upper limit | Top 8 entries | Controls the total amount of data recalled in a single search, avoids introducing too many non-core control item materials, and reduces invalid token consumption |
| Context filtering threshold | 0.75–0.85 | Filters historical project data with low relevance to current requirements, ensures the context only contains valid content from the current assessment project, and adapts to the precise needs of control item gap checks |
| Reranking switch | Enabled | Prioritizes recalling highly relevant data related to control item verification and rectification evidence collection, and optimizes the overall quality of the context |
Three Common Misconfigurations
- Abnormally high token consumption logs appear after a conversation. The root cause is that no context filtering threshold is set, and irrelevant documents from multiple historical projects are recalled, causing the total context token count to exceed expected values.
- Model output is truncated, and rectification plan content cannot be fully generated. The root cause is that the
maxContextsetting value is lower than the total token usage of the current context and generation requirements, leaving insufficient token space for generation. - Recalled results only include system inventory fields and lack control item current status descriptions. The root cause is that the single search upper limit is set too low, and a sufficient number of related documents are not recalled, failing to cover complete assessment materials.
How to Verify Proper Configuration
- Import a single complete cybersecurity level protection project document, trigger a context recall, and confirm that the recalled results only include relevant materials from the current project with no cross-project data.
- After adjusting the
maxContextconfiguration, check that the token consumption of a single recalled text meets expectations. This can be verified using the built-in token statistics tool in FastGPT. - Simulate a complete requirements drafting process, and confirm that the length of the model output meets document generation requirements with no unexpected truncation.
- Trigger context calls multiple times, and confirm that token consumption logs fall within the preset configuration range with no abnormally high or low values.
The values provided are common starting points and should be measured 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.