What This Category’s Data Looks Like
Cybersecurity investment research data primarily comes from public vulnerability databases, official threat intelligence platforms, vendor security advisories, archived penetration test reports, and system log audit data. Update schedules adjust dynamically alongside vulnerability disclosures and threat event outbreaks. Emergency vulnerability advisories can update in real time, while regular intelligence syncs occur daily. Document structures typically include fields such as vulnerability ID, CVSS score, affected asset scope, repair patch links, and associated APT groups. Field units include CVSS scores, asset counts, and threat levels, among others.
Constraints on Multi-turn Dialogue and Prompt Engineering Workflows
Real-time updates to vulnerabilities and threat intelligence require multi-turn dialogue to pull the latest knowledge base entries in real time. This prevents returning outdated repair solutions. The specialized nature of multiple fields requires prompts to explicitly specify extraction of designated fields such as CVSS scores and affected asset scope. This avoids vague responses. Long documents, such as penetration test reports, occupy large context window space. This requires limiting context retention length per dialogue turn. For investment research questions involving cross-vulnerability correlations, multi-turn dialogue must track previously mentioned vulnerability IDs. This prevents repeated retrieval of irrelevant entries.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Cybersecurity investment research documents are mostly long-form text. Sufficient context must be retained to correlate multiple vulnerability details and avoid truncating critical content |
recallTopK | 6-10 entries | Security intelligence entries are numerous. Highly relevant results must be filtered to avoid excessive low-value content interfering with dialogue |
similarity threshold | 0.75-0.85 | Terminology in the security field is highly specialized. A high matching threshold is required to retrieve accurate vulnerabilities and threat intelligence |
promptTemplate | Prioritize extracting CVSS scores, affected asset scope, and repair solution fields, then organize and sort by risk level | Matches the core query needs of security investment research, and clarifies output format to adapt to subsequent analysis |
streamResponse | Calibrate based on actual testing | Security intelligence loading may involve pulling data from multiple sources. Streaming output can optimize interactive waiting experience |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When a specific security investment research question triggers a workflow, the process terminates directly after the knowledge base search, cannot enter the AI dialogue phase and is reproducible. Cause: No reasonable
similarity thresholdis set, resulting in no matching security intelligence entries being retrieved. The subsequent AI dialogue module has no valid input and terminates. - Phenomenon: When calling the dialogue interface via API, no chunked streaming return data is obtained, and the front-end interface only displays static final results. Cause: The
streamResponseconfiguration item is not enabled, or the chunked data receiving and rendering logic is not implemented according to the interface specification. - Phenomenon: The dialogue context window frequently overflows, and multi-turn dialogue cannot associate previously mentioned vulnerability IDs. Cause: The character length of
maxContextis not limited, and the superposition of long documents and multi-turn historical contexts exceeds the model's supported upper limit.
How to Verify Proper Configuration
- Initiate a multi-turn query containing multiple vulnerability IDs. Confirm the model can associate vulnerability information mentioned in the previous turn to verify the context tracking configuration is active.
- Call the API interface. Check if returned data includes chunked streaming markers to verify the streaming output configuration is correct.
- Import a long-form penetration test report and initiate a query. Confirm no context window truncation prompt appears to verify the
maxContextconfiguration is reasonable. - Initiate a high-risk vulnerability query. Confirm returned results include designated fields such as CVSS scores and affected scope to verify the prompt template configuration is active.
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.