What data for this category looks like
Data for this category primarily comes from WAF, IDS, firewalls, and threat intelligence platforms. Updates occur daily or when a protection event is triggered. Daily produced documents include rule change tickets, intercept logs, false positive analysis reports, and policy rollback records. Fields typically include rule ID, trigger time, matching characteristics, and business impact scope. There is no unified mandatory format, and updates are adjusted flexibly based on enterprise protection policies.
What constraints these characteristics impose on model integration and configuration
Scattered data sources and unfixed update batches require model integration to support batch parsing of multi-source logs. False positive analysis and policy rollback require association with historical events, so a sufficient context window must be retained. Review and effect verification of rule change tickets require the model to quickly match the historical rule library, so token consumption for single calls must be limited. The real-time requirements of intercept logs determine that reasonable thresholds must be set for model calls to avoid slowing down the speed at which protection policies take effect. Multi-dimensional analysis of false positive samples requires the model to associate multi-source data simultaneously, so appropriate context length must be configured to avoid truncating key associated information. Effect verification of policy gray release requires the model to quickly compare the intercept performance of different rule versions, so parameters must be adjusted to balance analysis stability and flexibility.
How to set configurations
The following configuration items are formulated based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Requires association with multi-source protection logs and historical false positive records; sufficient context preserves the complete feature chain associated with rules |
maxTokens | 2000–3000 token | Single rule analysis or false positive review output must cover policy adjustment recommendations, avoiding truncation of key conclusions |
temperature | 0.2–0.4 | Rule operations require stable analysis results; lower temperature reduces irrelevant conclusions and ensures consistency of policy adjustments |
toolChoice | auto | Automatically select whether to call tools for log parsing or rule matching, adapting to the dynamic processing needs of multi-source data |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. This is not a single-model address for VLLM/Ollama/Xinference | Used as an environment variable for the App/Admin server; configuring this ensures the service can properly connect to the AI Proxy and avoids request failures due to incorrect addresses |
AIPROXY_API_TOKEN | AI Proxy administrator token, consistent with the AI Proxy server configuration and cannot be empty. This is not an application API key or model vendor key | Used as an environment variable for the App/Admin server; completes identity verification with the AI Proxy service and ensures valid call permissions |
Three common configuration errors
- A "connection disconnected" prompt appears in model responses. Troubleshoot by verifying: whether the
AIPROXY_API_ENDPOINTenvironment variable is configured correctly, whether network policies restrict access to the AI Proxy service port, or whether this required environment variable is missing, preventing the service from connecting to the AI Proxy. - Reply effects under the same prompt differ from those of the native model. This is caused by improper
temperatureparameter setting, ormaxContexttruncating key historical rule context information. - No call logs are displayed on the model vendor’s page. This is caused by disabled call log persistence configuration, or a log storage period set too short, resulting in automatic data clearance.
How to confirm successful configuration
- Confirm that the App/Admin server has correctly configured the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables, the service startup verification passes, and test calls do not return identity verification or address error prompts. - Adjust the
maxContextandmaxTokensparameters, upload a rule change ticket and intercept logs, and confirm that the complete content is correctly parsed without truncation prompts. - Set
temperatureto 0.3, call the same prompt multiple times, and confirm that the consistency of output results meets expectations. - Access the model vendor’s page, check whether call log records from the past hour are generated, and confirm that the log storage configuration is effective.
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.