What this category of data looks like
Vulnerability scanning service data originates primarily from scan plans and vulnerability lists generated by vulnerability scanners. It combines asset lists, CVE databases, and patch announcements to create remediation work orders and rescan reports. Data updates occur weekly or monthly. Each individual data entry includes fields such as asset IP address, vulnerability CVE ID, risk level, remediation status, and rescan results. Most documents use structured tables and plain text reports. Fields have high correlation and relatively consistent formats.
What constraints these characteristics impose on the vector model and indexing link
Vulnerability scanning service data has scattered sources and fixed update cycles. Use incremental indexing strategies to align with weekly or monthly data update rhythms. This avoids the computational overhead of full index rebuilding. Data fields have strong correlation, including associated information such as asset IP, CVE ID, and remediation status. Retain contextual connections during indexing to prevent loss of business logic after splitting. Individual data entries have clear structures. Apply fixed chunking rules, no need for dynamic adaptation to complex formats. Additionally, strongly classified fields require indexing configurations to balance semantic recall and precise field matching. This improves the business relevance of recall results.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
defaultToken | 1000 | Matches the average token length of single vulnerability details, ensuring completeness of basic semantic recall |
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text structure of vulnerability scanning reports, avoids splitting that damages business information |
| Overlap Length | 100–200 characters | Retains asset and vulnerability association information across chunks, improves contextual coherence |
maxToken | 4096 | Matches the official context limit of FastGPT v4.17.0, ensures complete recall of long documents |
| Single Search Limit | Top 10 entries | Adapts to daily troubleshooting needs of security operation teams, filters redundant recall results |
| Similarity Threshold | 0.78 (calibrated via actual testing) | Adapts to semantic differences in vulnerability risk levels, enables precise recall of relevant business documents |
Three common mistakes
- Creating a QA split collection via the knowledge base interface triggers index timeout, returning status code 504. The cause is failure to configure incremental indexing to match the weekly update rhythm of vulnerability scanning services. Full indexing covers all historical scan reports and CVE data, exceeding single-processing limits.
- Changing the knowledge base index model from embedding-ada-002 to embedding-3 without updating configurations leads to unexpected behavior. The cause is failure to fixedly bind the specified embedding model. The platform defaults to updating public model versions, leading to a mismatch between the original vector space and the new model.
- Imported historical vulnerability lists cannot be accurately matched by new retrieval requests. The cause is failure to perform a reindex operation on the imported knowledge base. Recalls still use vectors generated by the old model, causing semantic space drift.
How to confirm configurations are correct
- View the index configuration page to confirm the embedding model matches the business required version, and does not use the platform's default automatically updated model.
- Run an incremental index test, verify that index duration matches the preset single-processing limit, and no timeout errors occur.
- Retrieve a known vulnerability CVE ID, verify that the recall results include corresponding asset information and remediation status, meeting business association requirements.
- Check imported historical documents to confirm that reindex operations have been performed, and the vector space matches the currently configured embedding model.
All configuration values listed here are common starting points. Adjust them based on your own specific samples and testing.
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.