What Data for This Category Looks Like
Cybersecurity marketing content sources include public CVE vulnerability announcements, red team and defense exercise recap reports, enterprise security compliance documents, customer-facing security popularization tweets, and customized security marketing plans. Update cadences fall into three categories: ad-hoc updates (e.g., when a CVE is released), periodic updates (e.g., after a red team exercise), and scheduled periodic updates (e.g., quarterly compliance document updates). Document structure includes title, CVE ID (for vulnerability-related content), risk level, affected asset scope, remediation configuration steps, and applicable customer scenarios. Fields include string identifiers, enumerated risk levels, numeric affected asset count (unit: devices), and timestamped release time.
Constraints for Vector Models and Indexing
Content contains structured identifiers such as CVE IDs and long-form remediation steps, requiring vector models to support both specialized terminology encoding and long-text semantic splitting. Update cadences are uneven, with some content being ad-hoc and others periodic, requiring indexing to support a combination of incremental and full update strategies. Fields include enumerated risk levels and numeric affected asset counts, requiring joint vector encoding of structured fields and unstructured text to avoid losing critical identifier information when using single-text encoding. Document lengths vary significantly, requiring flexible adjustment of chunking parameters to avoid over-truncating long texts or over-splitting short texts.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Prioritize qwen3-embedding-8b or locally deployed M3E series models | Cybersecurity content contains a large number of specialized terms and structured identifiers. These models have strong professional semantic encoding capabilities, adapting to the text understanding needs of security scenarios |
chunk_max_length | 800–1200 characters | Cybersecurity marketing content includes both short risk alerts (approximately 200 characters) and long remediation plans (up to 1500 characters). This range balances semantic completeness and indexing efficiency |
chunk_overlap | 100–150 characters | The technical logic of security content is coherent. Overlapping segments avoid semantic breaks and improve the accuracy of recall results |
top_k | Top 8–12 entries | Audience for cybersecurity marketing content needs clear risk levels and corresponding solutions. A small number of highly relevant entries meets requirements, while too many increase the burden of context processing |
score_threshold | 0.75–0.85 | Security content has high requirements for professional semantic matching. A threshold that is too low will introduce irrelevant non-security content, while a threshold that is too high may miss valid matching results |
index_refresh_strategy | Incremental updates + weekly full updates | Content such as CVE vulnerability announcements is updated ad-hoc. Incremental updates can synchronize the latest content in a timely manner; weekly full updates ensure consistency of historical data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- When configuring multiple vector models with the same name, subsequent configurations overwrite previous ones, and models with the same name across different deployment methods cannot be used simultaneously. Reason: In FastGPT v4.9.11 and later versions, vector model configurations use the name as the unique identifier, and multiple instances with the same name are not supported for differentiation.
- After building a knowledge base, recall results are empty or far fewer than expected. Reason: The
chunk_max_lengthparameter was not adjusted based on the field characteristics of cybersecurity content, leading to excessive truncation of long texts and loss of critical technical semantics. - Public cloud version knowledge base recall results show abnormal Markdown formatting, while local deployment versions do not have this issue. Reason: There are differences in text preprocessing rules between the public cloud and local versions, and separate adaptation was not performed for code blocks and risk level identifiers in security content.
How to Verify Proper Configuration
- Navigate to the FastGPT knowledge base configuration page, check if the
embedding_modelfield matches the target configured model name, and confirm no residual overwrites exist. - Upload a single typical cybersecurity marketing document, trigger index construction, view the chunking preview interface, and confirm that chunk lengths fall within the configured range with no excessive truncation or invalid splitting.
- Initiate a knowledge base recall test, input a test query such as "small and medium enterprise cybersecurity marketing solutions", and verify that the number of recalled entries matches the range specified by the
top_kconfiguration. - View the index refresh logs, confirm that incremental update tasks execute according to the configured cycle, and no failed error records are present.
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.