What Data for This Category Looks Like
This category’s data primarily comes from SIEM, EDR, firewall logs, and host images. Updates happen in real time or trigger per individual incident. Daily processed documents include incident response reports, forensics records, disposal plans, and notification materials. Document fields include attack source IP addresses, disposal times, impact scopes, evidence hash values, and more. Individual document lengths vary widely. Some forensics records contain binary image fragments, with no unified fixed format specification.
Constraints for Re-ranking and Hybrid Retrieval
Dispersed, real-time data sources require the retrieval pipeline to support multi-format logs and report fragments. This avoids losing valid information due to format incompatibility. Wide variance in single-incident data lengths requires limiting retrieval result length and count. This prevents context overload that hinders attack path reconstruction. Forensics records and binary image fragments have weak semantic relevance. Use re-ranking models to strengthen semantic matching. This avoids irrelevant fragments interfering with evidence chain organization. Real-time updated data requires the retrieval pipeline to have low latency. This ensures quick access to latest disposal basis during incident response.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | This category requires enhanced semantic matching to filter irrelevant logs and report fragments, improving retrieval result relevance |
similarity | 0.65–0.75 | Balances retrieval recall rate and relevance, adapting to semantic differences between forensics records and logs |
limit | Top 8 entries | Controls the number of retrieval results, avoiding context overload that impacts attack path reconstruction and evidence chain organization |
searchMode | hybrid | Combines vector retrieval and keyword retrieval, meeting retrieval needs for structured logs and unstructured reports |
Three Common Configuration Mistakes
- Retrieval results return an empty array or no valid reference fragments. Cause: Docker environment variables for the re-rank model are not configured correctly. This causes the re-ranking model to fail to load, interrupting the retrieval pipeline.
- Retrieval results contain large numbers of irrelevant firewall logs or forensics record fragments. Cause: The
similarityparameter is not adjusted to the range suitable for the category’s data. Or thelimitparameter value is not restricted, leading to excessive redundant information. - Docker deployment of bge-reranker results in container startup failure. Cause: Correct configuration items are not added to the environment field in docker-compose.yml. This prevents the model from initializing normally.
How to Confirm Successful Configuration
- Access the FastGPT v4.17.0 test console. Input simulated incident response consultation questions. Check if retrieval results include target document fragments such as incident response reports and disposal plans.
- View the re-ranking model’s operation logs. Confirm that the
usingReRankparameter is active. Check that there are no error messages for failed model calls. - Adjust the
similarityparameter value. Observe changes in retrieval result relevance. Confirm that the threshold setting meets the semantic matching requirements of the category’s data. - Verify hybrid retrieval mode. Input queries containing IP addresses and attack behavior descriptions. Confirm that both structured logs and unstructured report fragments are retrieved.
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.