What data for this category looks like
Cybersecurity research report data mainly comes from public research reports of professional cybersecurity vendors, vulnerability announcements released by national information security vulnerability sharing platforms, industry compliance security guidelines, and internal enterprise security audit documents. Reports targeting the financial industry will also include exclusive content such as payment system vulnerabilities and customer data compliance requirements. Update cycles vary significantly by content type: vulnerability announcements are pushed in real time, vendor research reports are updated quarterly, and compliance documents iterate alongside regulatory requirements. Document structures typically include fields such as vulnerability ID, CVSS risk score, affected asset scope, remediation measures, and associated threat intelligence. Some documents also include technical details such as attack sample hashes and affected version numbers.
What constraints do these characteristics impose on knowledge base retrieval and recall
Multi-dimensional characteristics of cybersecurity research reports create multiple constraints for retrieval and recall. Real-time updated vulnerability announcements require the knowledge base sync frequency to align with business needs, to avoid recalling outdated content. Structured fields such as CVSS risk score and affected version number must support numeric range and exact match retrieval. Full-text fuzzy matching alone cannot meet requirements. Long-form research report content is prone to context truncation, so segmentation rules must be adjusted to retain complete technical logic. Multi-field combined retrieval requirements, such as matching both vulnerability ID and affected asset type simultaneously, require the retrieval chain to support multi-condition combinations. Without this capability, target security documents cannot be accurately located.
Configuration Recommendations
| Configuration Parameter | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Cybersecurity research reports often contain technical details. An overly long context will exceed the model window, while an overly short context will lose key information such as remediation measures |
recallTopK | Top 8–12 results | Security issues require coverage of solutions and vulnerability details across different dimensions. Too many results will increase the model inference load, while too few will miss critical information |
similarityThreshold | 0.72–0.85 | Security documents have strong technical relevance. A threshold that is too low will introduce irrelevant content, while a threshold that is too high may miss valid vulnerability remediation solutions with slightly lower matching scores |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large cybersecurity research report PDFs takes significant time. A timeout will cause upload failures |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Some vendor research reports include multi-page technical attachments, so large file upload support is required |
workflowRecallTopK | Top 3–5 results | Workflow scenarios require quick location of core security issues to avoid redundant information interfering with process execution |
The parameter values provided on this page are common recommendations to use as a starting point for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: Knowledge base retrieval results display document download links in debug mode, but the links are empty or not shown after the application is published. Cause: The "Allow downloading associated documents" permission is not enabled in the application configuration, or the document association configuration is not updated synchronously during publishing.
- Phenomenon: After uploading a cybersecurity research report PDF, the vectorization task remains in the queue for a long time with no progress updates. Cause: Vectorization queue expansion parameters are not configured, or the uploaded file size exceeds the single-task processing limit of the current cluster, leading to task backlogs.
- Phenomenon: The reference limit for knowledge base search configuration in simplified Chinese applications is 13000, while the reference limit for knowledge base search nodes in workflows is only 3000, resulting in insufficient recalled documents in workflow scenarios. Cause: FastGPT uses different resource allocation strategies for application and workflow scenarios. Workflows prioritize ensuring process execution stability, so the single-node recall limit is restricted.
How to Confirm Your Configuration Is Set Correctly
- Upload a typical cybersecurity research report PDF, check if the parsed segments retain core fields such as vulnerability ID and CVSS score. Adjust the corresponding parameters until parsing is complete.
- Initiate a simulated retrieval request, verify that target documents can be accurately recalled based on fields such as CVSS score and vulnerability ID. Adjust the retrieval configuration until matching results meet business expectations.
- Perform retrieval in both application debug mode and published mode, confirm that document download links display normally. Check that the application permission configuration covers document download permissions.
- Trigger the knowledge base search node in the workflow, verify that the number of recalled documents meets business requirements. If insufficient, adjust the corresponding recall parameters.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.