What this category's data looks like
Data sources include SIEM, EDR, NDR, and external threat intelligence feeds. Daily documents include alarm disposal tickets, duty logs, threat intelligence bulletins, and daily/weekly reports. Data updates follow two cadences: real-time and daily. Most document structures include fields such as alarm ID, disposal steps, threat level, and response time limit. Common units are counts, times, hours, and level identifiers. Target users include SOC analysts, security operation outsourcing teams, and enterprise security centers. Data flows through continuous alarm judgment and escalation handling.
What constraints do these characteristics impose on vector models and indexing workflows?
Real-time alarm data requires indexes to support incremental writes, to avoid performance loss from full reconstruction. Multi-source heterogeneous data needs field parsing adapted to different document structures, to ensure consistent vector embeddings. Daily updated weekly reports and bulletins require periodic index updates to preserve threat intelligence timeliness. Structured fields such as alarm ID and threat level must combine with unstructured text for vectorization, to improve retrieval accuracy. Single-operation alarm data volume is large, so limit single-node load of index shards to prevent retrieval timeouts.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of alarm disposal tickets and duty logs, avoids overly scattered or redundant single chunk content |
| Overlap Length | 100–200 characters | Retains contextual association across chunks, ensures complete retrieval of alarm disposal steps |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance alarm data, reduces retrieval interference caused by false positive accumulation |
| Single Search Limit | Top 10 entries | Controls the number of results returned per retrieval, adapts to the rapid judgment needs of SOC analysts |
| maxToken | 4096 | Adapts to the context window of mainstream API models, avoids truncation of critical threat intelligence content |
| defaultToken | 1024 | Reserves sufficient text space for basic query context related to alarms |
Three Common Misconfigurations
- Phenomenon: Mixed retrieval response time stays above 10 seconds consistently, with initial vector retrieval taking about 8 seconds. Cause: Single search limit and chunk parameters are not adjusted based on real-time alarm data volume in SOC scenarios, causing vector calculation and result aggregation load to exceed thresholds.
- Phenomenon: Index creation process stalls at the last set of data, with no progress updates. Cause: Incremental index updates are not enabled, and full reconstruction is disrupted by real-time incoming alarm data occupying resources, or imported file formats are not compatible with supported system types.
- Phenomenon: Some alarm disposal tickets return empty vector results after the knowledge base is created. Cause: Multi-source data field mapping rules are not configured, causing structured alarm fields exported from SIEM to not be properly embedded into vectors.
How to Confirm Proper Configuration
- Import a single alarm disposal ticket, run a simulated retrieval, check whether returned result relevance meets judgment needs, and adjust the similarity threshold to match expected outcomes.
- Submit an incremental update task for real-time alarm data, check whether the index progress bar advances normally, and confirm no stalling occurs.
- Call the interface to test vector generation results, check whether key fields such as alarm ID and threat level are properly embedded, and no null values are returned.
- Compare retrieval results with and without query optimization enabled, confirm that the query optimization function improves matching accuracy for alarm-related queries.
- Confirm that the current operating environment is FastGPT v4.17.0, to ensure configuration parameters are compatible with the version.
The values provided are common starting points and should be evaluated using samples from the specific deployment context.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.