What Data for This Category Looks Like
Cybersecurity investment research data primarily comes from public vulnerability databases, commercial threat intelligence platforms, red and blue team exercise recap documents, and industry security bulletins. Update cycles cover real-time vulnerability disclosure, daily threat aggregation, and weekly industry recap documents.
Single document structure includes fields such as CVE-ID, CVSSv3 score, disclosure time, attack vector, and remediation plan. The score unit is a floating-point value between 0 and 10. Time fields use the ISO 8601 format. Most single documents range from 10KB to 500KB in length.
What Constraints Do These Characteristics Impose on Conversation Logs and Auditing?
Multi-source heterogeneous data sources require conversation logs to fully record called intelligence source identifiers, to avoid cross-source data confusion.
High-frequency update characteristics require the audit workflow to track the correspondence between knowledge base versions and conversation triggers. This ensures conversations use the latest compliant security data.
Large single-document length requires logs to retain key fragments of recalled documents and field reference paths, to avoid full-volume storage.
High standardization but numerous fields require audit rules to verify that fields called in conversations comply with investment research scenario compliance requirements. For example, check whether CVSS score value ranges conform to industry norms.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_LEVEL | info (production environment) / debug (debug phase) | Cybersecurity investment research requires tracking complete conversation call links. Debug level records detailed information about model calls and document recall, while info level balances storage and troubleshooting needs |
AUDIT_RECORD_RETENTION_DAYS | 90 days | Financial investment research scenarios must meet compliance audit retention requirements, and 90 days aligns with the basic retention cycle of most industry regulations |
RECALL_DOCUMENT_MAX_LENGTH | 800–1200 characters | Cybersecurity investment research documents often contain long-text remediation plans and attack details. This range retains key information while avoiding log redundancy |
API_LOG_INCLUDE_FIELDS | ["query", "recall_docs", "model_response", "timestamp"] | Core audit fields to be recorded cover user queries, recalled security documents, model outputs, and timestamps, meeting investment research traceability requirements |
ERROR_LOG_TRIGGER_THRESHOLD | 500 / 400 status codes | Cybersecurity scenarios require prioritizing tracking of abnormal requests, capturing 500-level service errors and 400-level parameter errors to quickly locate vulnerabilities in intelligence call anomalies |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require on-site analysis, and it is recommended to test against your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: A 500 error is returned when calling the security investment research knowledge base, and the specific error stack is not displayed in system logs. Cause:
LOG_LEVELis set towarnorerrorlevel, and debug-level logging is not enabled. Detailed error information is filtered out. - Phenomenon: The model used in a conversation does not match the model configured for knowledge base association. Audit logs cannot trace model version differences. Cause: The
model_versionfield is not included inAPI_LOG_INCLUDE_FIELDS, so model call version information is not recorded. - Phenomenon: No chain-of-thought process is returned during API calls, and no relevant output fields appear in audit logs. Cause: The
ENABLE_CHAIN_THINK_LOGparameter is not enabled, orchain_thoughtis not included inAPI_LOG_INCLUDE_FIELDS. Chain-of-thought content is not captured.
How to Confirm Configuration Is Correct
- Initiate a test query containing specific security vulnerability keywords. Check whether system logs fully record the query content, recalled document fragments, and timestamps.
- Construct a test request that violates parameter specifications, such as passing query parameters outside a reasonable range. Confirm that error logs capture the corresponding status codes and detailed error information.
- Verify the audit log retention period. Confirm that the log retention duration meets the compliance requirements preset for the scenario.
- Initiate a test call with chain-of-thought enabled. Confirm that both the API return result and audit logs contain the chain-of-thought process fields.
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.