What data looks like for this category
Cybersecurity research report data sources include public security vendor technical reports, National Information Security Vulnerability Sharing Platform, and open-source vulnerability databases. There are two update cadences: vulnerability detail documents are updated in real time, while industry analysis documents are updated weekly or monthly. Document structure includes fields such as vulnerability ID, affected asset type, attack vector, CVSS score, remediation steps, and associated threat intelligence. Vulnerability ID uses standard CVE string format. CVSS score uses a 0-10 scale. Release time uses ISO 8601 format. Single document lengths vary widely. It is recommended to count or test with your own samples before finalizing settings.
What constraints do these characteristics impose on model access and configuration
Cybersecurity research reports have many structured fields, high real-time update frequency, and long document lengths. These characteristics impose clear constraints on model access and configuration. Standardized fields such as vulnerability ID require precise matching. Priority rules for structured recall must be configured. Real-time updated data sources require adaptation to short-cycle caching strategies. This prevents returning outdated vulnerability information. Long document structures contain multiple technical details. Chunk length must be adjusted to prevent key remediation steps from being truncated. Numeric fields such as CVSS score require the model to support numeric comparison. Weight parameters for numeric features must be configured.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the long-text structure of cybersecurity research reports, avoids truncating key remediation plans and threat analysis content |
rerank_top_n | Top 3–5 results | Cybersecurity scenarios have high requirements for result accuracy, limiting the number of returned results reduces interference from redundant information |
similarity_threshold | 0.75–0.85 | Filters low-match irrelevant security documents while retaining potential vulnerability-related research reports |
parse_chunk_size | 1000–1500 characters | Balances accuracy and efficiency of long-text processing, avoids damaging the logical integrity of security technical descriptions |
api_request_timeout | 30–60 seconds | Adapts to the API response speed of some security data sources, prevents loss of valid retrieval results due to timeout |
enable_structured_recall | Enabled | Optimizes recall accuracy for structured fields such as vulnerability ID and CVSS score, improving retrieval matching efficiency |
The parameter values provided on this page are standard recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing the values.
Three Common Configuration Mistakes
- Rerank model loading fails, and the console returns a
500 internal errorstatus code. The model tokenization configuration is not adjusted for cybersecurity domain-specific terminology, leading to vocabulary mismatch issues during model initialization. - There is a significant discrepancy between token consumption records for model calls and actual consumption returned by the API. The
count_prompt_tokens_onlyparameter is not configured, causing redundant research report fragments in the context to be repeatedly counted in token statistics. - The chat window embedded via iframe cannot capture model return results. Front-end message listening events are not configured, and FastGPT's output callback interface is not correctly bound.
How to Confirm Configuration is Complete
- Enter a known CVE number as a query term, and verify that retrieved results include matching vulnerability-related research report content.
- Check the rerank model status on the model configuration page, confirm it shows normal operation with no loading failure prompts.
- Initiate a complete research report retrieval process, and verify that token consumption in background logs matches consumption returned by the API.
- Send a security-related query to the embedded chat window, confirm the front-end page can correctly receive and display analysis results returned by the model.
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.