What this category of data looks like
Data for this category originates from package managers, code repositories, public vulnerability databases, and license databases. It regularly produces four types of documents: SBOM lists, dependency risk reports, license lists, and repair tickets. Data updates typically occur via daily syncs or when software versions are released. Core fields include third-party dependency package names, version numbers, associated vulnerability IDs, license types, repair priorities, and ticket statuses. There is no unified mandatory format, but the structure must align with the business logic of dependency asset identification and risk assessment.
What constraints do these characteristics impose on the context and token processing link
This category of data is scattered and has flexible update rhythms. Context and token processing must balance data timeliness and quota efficiency. Multi-dimensional document fields require precise filtering of core information for dependency packages, vulnerabilities, licenses, and tickets during context loading, to avoid redundant content consuming excessive tokens. Daily or version-based update frequencies require context caches to match the corresponding sync cycles. Otherwise, outdated data may be loaded, leading to incorrect risk judgments. Additionally, the logic linking repair tickets and risk reports requires complete field matching. Excessively long context content will exceed the token quota limit, resulting in critical information about high-risk dependencies or license violations being truncated.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the core field length of SBOM lists and dependency risk reports, avoids truncation of critical risk information |
quoteMaxToken | 2000–3000 token | Focuses on vulnerability details and license compliance snippets, reduces unnecessary token consumption |
| Context refresh cycle | 00:00 daily | Matches the daily data sync update rhythm, ensures loading of the latest dependency assets and risk data |
| Recall matching count | Top 3 entries | Prioritizes core reports of high-risk vulnerabilities and license violations, avoids redundant content occupying tokens |
| Context cache retention duration | 7 days | Balances data timeliness and token usage, avoids frequent pulls exceeding quota |
Three Common Misconfigurations
- The question-and-answer interface only displays the total token consumption value, and cannot split the specific usage of input and output tokens. This occurs because the token detail statistics configuration switch is not enabled, and only the aggregated consumption interface is called.
- Frequent connection timeouts or interface errors occur during calls. This occurs because the context refresh cycle is set too short, and frequent pulls of scattered data sources such as vulnerability databases and license databases trigger current limiting rules of external interfaces.
- Some vulnerability details of high-risk dependencies are missing in the generated release notes. This occurs because the
quoteMaxTokensetting value is too small, and core risk snippets are truncated and not included in the context scope.
How to Verify Successful Configuration
- Enter the supply chain security scenario configuration page of FastGPT v4.17.0, check the token statistics related configuration items, and confirm that the detail statistics switch is enabled.
- Manually trigger a context load for a dependency risk report, check the returned token consumption data, and confirm that input and output tokens are displayed separately.
- Check the context refresh cycle setting to match the synchronization frequency of the data source, and verify that the vulnerability data updated on the day can be loaded normally.
- Simulate loading a complete SBOM list, confirm that no token limit error occurs, and core fields are not truncated.
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.