What Data for This Category Looks Like
Data for this category originates from SIEM, EDR, firewall logs, and host images. Updates occur in real time or per incident. Core processed documents include incident response reports, forensic records, disposal plans, and notification materials. Each data entry includes fields such as event timestamp, associated IP, alert level, disposal steps, and evidence chain number. There is no unified fixed length. Data volume for a single incident fluctuates based on attack complexity.
Constraints These Characteristics Impose on Vector Models and Indexes
Real-time or per-incident updates require vector indexes to support incremental writes. This avoids processing delays caused by full index reconstruction. Multi-source heterogeneous log and document structures require vector models to support mixed encoding of unstructured text and structured fields. This ensures consistent vector representations across data from different sources. The core need for evidence chain organization requires indexes to retain association identifiers from original data. This prevents loss of key traceability information during vector retrieval. Fluctuating data volume per single incident requires index sharding strategies to support dynamic expansion. This avoids single shard overload that slows response times.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to average length of incident response logs and documents, avoids overly scattered or redundant single-chunk content |
| Overlap Length | 100–200 characters | Retains cross-chunk contextual associations, ensures logical coherence when restoring attack paths |
defaultToken | Calibrated based on actual measurements | Adapts to token consumption differences across multi-source logs, avoids single-request token limit exceeded |
maxToken | 4096 token | Complies with context window limits of common open-source vector models, balances retrieval accuracy and response speed |
| Single Search Limit | Top 10 entries | Balances comprehensiveness of evidence chain organization and retrieval response speed, avoids interference from redundant data |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation log entries, focuses on high-risk attack correlation information |
Three Common Mistakes
- Phenomenon: The exported knowledge base dataset.csv only includes the index field and no content field. Cause: The original log content field was not correctly associated during chunk configuration, and only metadata was indexed.
- Phenomenon: Vector retrieval returns a large number of low-correlation log entries, making it impossible to quickly locate core attack evidence. Cause: The similarity threshold was set too high, failing to filter irrelevant normal log data.
- Phenomenon: The index model fails to load properly in a Docker deployment environment. Cause: The model storage directory was not mounted in the docker-compose configuration, or the vector model call parameters were not aligned with the deployment environment.
How to Verify Successful Configuration
- Upload a single incident response log sample, run a vector retrieval, and check if returned results include associated attack path and disposal plan information.
- Export the knowledge base dataset.csv, confirm the file contains both index and content fields, and that field content matches the original log text.
- Check vector model loading logs in the system monitoring of FastGPT v4.17.0, confirm no error messages are present.
- Simulate real-time incident writes, check if index update delay meets business timeliness requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.
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.