What the data for this category looks like
Data sources include the CVE vulnerability database, public security vendor technical bulletins, industry security emergency response reports, and internal enterprise security audit documents. Updates align in real time with security events. Updated documents are available within hours after major vulnerability disclosures. Document structures include fields such as vulnerability ID, CVSS score, attack vector, affected asset scope, remediation measures, and associated attack cases. Some documents are pure technical text paragraphs with no fixed format. Units include CVSS scores (0-10), affected asset counts (units/systems), and release timestamps (ISO 8601 format).
What constraints these characteristics impose on vector models and indexing
Multi-source heterogeneous data formats require vector models to handle both structured field encoding and non-technical text semantic matching, to avoid technical term encoding bias. Real-time updated security events require indexes to support incremental refreshes, avoiding performance losses from full index rebuilding. Long-text attack process analysis requires appropriate segmentation strategies, to avoid breaking cross-paragraph attack chain context associations. Multi-field associated retrieval requirements require indexes to support metadata filtering, enabling accurate location of specific types of security research reports.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Cybersecurity research reports often contain long sections of vulnerability analysis and attack processes. Excessive length will lose context, while insufficient length will break attack chain logic |
chunk_overlap | 150–200 characters | Cross-segment attack step association information must be retained, to avoid breaks during retrieval |
recall_top_k | Top 10–15 results | Security issues often relate to multiple vulnerabilities or attack events, requiring sufficient recall volume to cover associated information |
vector_db_batch_size | 32–64 entries per batch | Cybersecurity research report data volume grows rapidly. Batch processing balances indexing speed and memory usage |
embedding_model | bge-m3 or text-embedding-3-large | These models perform well in encoding technical text, and support semantic matching across multiple fields such as CVSS scores and attack vectors |
index_refresh_interval | 5 minutes | Security events update frequently. Near-real-time index refresh ensures retrieval timeliness |
The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: Index tasks take an unusually long time to complete in a docker-compose deployment environment, and logs show an
OOM killederror. Cause: Thevector_db_batch_sizeparameter was not adjusted. Memory usage during batch processing exceeds the container quota. - Phenomenon: The ollama qwen2.5 and bge-m3 models have been added to the model channel, but no corresponding options appear in the text understanding model dropdown when creating a knowledge base. Cause: The embedding model was not bound to the vector retrieval configuration item of the knowledge base, and only the large language model channel was configured.
- Phenomenon: Retrieval of the vectorized dataset returns empty results, and the interface returns a
status_code: 400error. Cause: Thechunk_sizeparameter was not adjusted based on report length. Long texts are excessively truncated, leading to semantic breaks.
How to confirm the configuration is correct
- Check the vector database container logs for no
OOM, connection timeout, or index failure error messages. - Upload a single cybersecurity research report, view the segmentation preview interface, and confirm that key information such as complete attack steps and vulnerability IDs is retained in the segments.
- Initiate a retrieval test, enter a test keyword, and check that the recall results include core content such as matching vulnerability IDs and remediation plans.
- Upload a new security research report, wait for the index task to complete, then initiate retrieval again, and confirm that the new content has been included.
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.