What the Data for This Category Looks Like
Core data for vulnerability scanning services comes from vulnerability scanner outputs, enterprise asset inventories, public CVE vulnerability databases, and official patch announcements. Regularly produced standardized documents include scanning plans, vulnerability lists, remediation work orders, and rescan reports. A single vulnerability list typically includes standardized fields such as vulnerability ID, affected assets, CVSS score, and remediation suggestions.
Data update cycles follow weekly or monthly schedules. Scanning plans and asset inventories are adjusted as enterprise assets change. Vulnerability lists are refreshed synchronously with updates to the CVE database.
What Constraints Do These Characteristics Impose on Context and Token Workflows
Documents for vulnerability scanning services have multi-dimensional associated attributes. A single rescan report may link to vulnerability remediation records for multiple assets. Context retrieval must accurately match assets, vulnerability IDs, and remediation statuses.
Frequently updated CVE databases and scanning plans require the context window to quickly adapt to the latest data. This prevents vulnerability troubleshooting errors caused by outdated data.
Single vulnerability lists have high field density. Overlong document segments will occupy excessive token quota. The length and number of retrieved segments must be limited to avoid exceeding the model's context upper limit.
Additionally, the closed-loop remediation work order process requires the context to retain historical scanning and rescan associated records. This prevents process interruptions caused by broken context.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0, and are adapted to the document characteristics of vulnerability scanning services.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the field density and associated complexity of vulnerability scanning documents, avoiding exceeding the model's capacity limit |
quoteMaxToken | 1000–1500 characters | Adapts to the average length of a single vulnerability list, preventing a single quoted segment from occupying excessive context quota |
| Chunk overlap length | 50–100 characters | Retains associated context between scanning plans and vulnerability lists, avoiding loss of critical information due to chunk breakage |
| Single search limit | Top 8 entries | Matches the number of associated documents used in daily vulnerability troubleshooting, reducing unnecessary token consumption |
| Maximum number of retrieved segments | 6–10 entries | Adapts to the number of associated records for rescan reports and remediation work orders, ensuring complete and non-redundant context |
Three Common Misconfigurations
- Context-referenced document segments do not render Markdown formatting correctly, only displaying original delimiters. This occurs because format markers from the original document are not retained during chunk processing, leading to lost typesetting information in retrieved segments.
- The number of context entries displayed on the frontend page does not match the number actually passed to the model. For example, the page shows 30 entries but 310 are actually passed. This occurs because configured retrieval filtering rules are not synchronized between frontend and backend logic, and duplicate or redundant segments are not processed uniformly.
- A discrepancy exists between knowledge base token calculation results and the actual token consumption of the model. This occurs because the single-segment token quota is not adjusted for the high field density of vulnerability scanning documents, leading to a mismatch between the calculation baseline and actual consumption.
How to Confirm Configurations Are Properly Applied
- Upload a vulnerability list document containing Markdown formatting, trigger context retrieval, and check whether returned segments correctly restore the original typesetting.
- Call the API to view context parameters, and compare the number of retrieved entries displayed on the frontend page with the actual number returned by the API to confirm consistency.
- Manually count the token consumption of a single standard vulnerability list, and compare it with the token statistics results from the knowledge base to confirm that the calculation logic matches.
- Adjust the value range of configuration items, and verify whether the context window can correctly retrieve multiple associated scanning plans and remediation work orders.
Note: 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.