What the data for this category looks like
Cybersecurity investment research data sources include public vulnerability databases, real-time threat intelligence platforms, security device operation logs, industry compliance documents, and penetration test and red team exercise reports. Update frequencies vary widely: vulnerability database information updates in real time as disclosures are made, threat intelligence syncs hourly, and compliance documents are revised quarterly or annually. Document structures include structured fields such as CVE IDs, CVSS scores, and affected components, semi-structured log snippets, and unstructured penetration test analysis reports. Fields include CVSS scores, attack vectors, fixed versions, log timestamps, and some documents include multilingual technical descriptions.
What constraints do these characteristics impose on vector models and indexing workflows?
Structured fields are numerous and include numeric metrics. Combine vector indexing and metadata filtering capabilities to avoid insufficient precision from relying solely on text semantic recall. High-frequency updated threat intelligence and vulnerability data require support for incremental index construction, to avoid long delays caused by full index rebuilding. Document length varies widely, from hundreds of characters of vulnerability descriptions to tens of thousands of characters of penetration test reports. Adapt flexible chunking strategies while retaining semantic connections across chunks. Multi-source data association needs require indexes to support group filtering by meta fields such as CVE IDs and release times, to accurately locate target security information.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Prioritize bge-large-zh-v1.5 or text-embedding-ada-002 | Cybersecurity documents contain specialized terminology and long passages. Long-text embedding models retain more semantic details |
chunk_size | 800–1200 characters | Balance single-chunk semantic completeness and recall granularity, adapting to typical lengths of vulnerability descriptions and penetration test reports |
chunk_overlap | 100–150 characters | Avoid semantic breaks across chunks, adapting to the coherence of technical details in security documents |
index_type | Select the HNSW vector index type | Balances high recall accuracy and low query latency, adapting to high-frequency updated threat intelligence data |
recall_top_k | Top 10–15 results | Covers the association recall needs of multi-source security intelligence, avoiding missed critical vulnerability or threat information |
similarity_threshold | 0.75–0.85 | Filter low-relevance security alerts or documents, avoiding false recall of unrelated vulnerability reports |
metadata_filter_enabled | Enable | Allow filtering recall results by meta fields such as cve_id and publish_time, accurately locating target security data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Analyze specific issues individually. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Vector index status shows not ready, and retrieval functions cannot be triggered. Causes: Incremental index switch is not enabled, full index rebuilding takes too long, or metadata field configuration errors cause index construction failure.
- Phenomenon: Embedding model calls return a
400 Bad Requesterror. Causes: API key for the corresponding model is not configured, or input text length exceeds the maximum limit supported by the model. - Phenomenon: Retrieval results do not include source document traceability information. Causes: The
metadata_include_sourceconfiguration item is not enabled, or source document paths, titles and other meta fields are not retained during chunking.
How to verify successful configuration
- Check the status indicator in the vector index management interface, confirm the status is ready. Adjust the wait time for ready determination based on actual data volume.
- Upload a test security document, trigger retrieval and check if the similarity scores of returned results match the expected threshold range.
- Verify the metadata filtering function, enter a specific
cve_idfor retrieval, confirm only associated document chunks are returned. - Check the return fields of retrieval results, confirm traceability information such as source document paths and chunk offsets are 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.