Citation Sources and Traceability for Cybersecurity

What the data for this category looks like

Cybersecurity investment research data primarily comes from public vulnerability databases, threat intelligence platforms, industry compliance documents, and offensive and defensive exercise reports. Update cycles vary significantly: vulnerability information is pushed in real time, compliance documents are updated quarterly, and offensive and defensive analysis reports are released irregularly. Documents include structured fields such as CVE IDs, CVSS scores, and affected components, as well as unstructured attack traceability logs and compliance rule texts. Some documents contain long paragraphs of technical details, and fields must follow standard naming conventions in the security domain.

What constraints these characteristics impose on the citation sources and traceability link

Structured fields including CVE IDs and CVSS scores require precise matching of corresponding entries during traceability to avoid generalized citations. Real-time updated vulnerability data requires the traceability workflow to synchronize source data timestamps, ensuring citations use the latest available versions. Long paragraphs of technical detail text must be split while retaining original anchors, preventing loss of original content positions during traceability. Different versions of industry compliance documents require version identifiers added to traceability information, preventing citations of outdated clauses. Unstructured attack log data must be linked to unique event identifiers, ensuring traceability back to specific offensive and defensive scenarios.

How to configure the settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Cybersecurity documents often include long log snippets and full offensive and defensive reports, requiring support for large single-file uploads
`PARSE_FILE_TIMEOUT_SECONDS`	`1200 seconds`	Parsing long logs and compliance documents takes significant time, avoiding parsing failures due to timeout
`Chunk Length`	`800–1200 characters`	Balances completeness of technical details in security documents and retrieval accuracy
`Retrieval Count`	`Top 8–10 results`	Covers investment research needs across multiple dimensions of threat intelligence and vulnerability information
`Similarity Threshold`	`0.72–0.85`	Distinguishes differences between affected versions and attack tactics for similar vulnerabilities
`Reranked Return Count`	`Top 3–5 results`	Avoids excessive redundant citations of homologous threat intelligence

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.

Three common configuration mistakes

Phenomenon: Some imported security documents do not generate question-answer pairs and are stored directly in the knowledge base as original text fragments. Cause: The ENABLE_QA_PAIR_GENERATE parameter is not enabled, or PARSE_FILE_TIMEOUT_SECONDS is set too short, leading to interruption of long document parsing.
Phenomenon: An error pops up when testing after configuring a third-party model, but normal citation generation works after forcefully ignoring the error. Cause: The model interface return format does not adapt to long-text output in the security domain, and the verification logic incorrectly identifies the request as abnormal. Common error messages include the 400 Bad Request status code.
Phenomenon: When the knowledge base contains multiple documents, a simple application only returns 1 citation result. Cause: The Retrieval Count setting is too low, and the global configuration for cross-document retrieval is not enabled. For FastGPT v4.8.22 and above, additional adjustment of the corresponding switch is required.

How to confirm the configuration is complete

Upload a single security document containing a CVE ID, and check if the parsed metadata in the knowledge base includes the corresponding standard fields.
Initiate a test query, and verify that the citation column of returned results includes the document name, original text anchor position, and update-related identifiers.
Import multiple different types of security documents, and verify that citation information from different documents can be correctly distinguished.
Adjust the Retrieval Count parameter, initiate a batch query, and confirm that the number of returned citations matches the configured expectation.

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.