What Data for This Category Looks Like
Data mainly comes from employee contact lists, email gateways, internal training platforms, and user click statistics systems. Daily generated documents include training courseware, phishing drill plans, standardized email templates, and drill result reports. Data update cycles are mostly quarterly or annual. Core fields cover drill audience scope, email delivery records, employee click feedback, and rectification results. Data is generated across the full lifecycle of personnel security awareness training and phishing drills, covering plan design to result review. There is no unified fixed format, so adaptation to multiple document structures is required.
Constraints Imposed on Model Integration and Configuration
Decentralized data sources require the model integration link to support standardized mapping of multi-source fields. This avoids parsing errors caused by inconsistent field formats. Diverse document types, including training courseware, email templates, and result reports, require the model to adapt text processing logic for different structures. Low update frequency means the model does not need high-frequency synchronization of training data. It must support adaptation to quarterly document content changes. Drill audience screening and template design are the most time-consuming steps. Model integration must configure sufficient context processing capabilities and timeout thresholds. This supports batch processing of large-scale employee data and personalized content generation.
Configuration Settings
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Phishing email templates, drill plans, and result reports usually contain multiple sections of scenario descriptions and rules. Sufficient tokens are required to support complete content generation |
temperature | 0.6–0.8 | Balance content standardization and flexibility. Generated training content must meet security requirements while having scenario adaptability |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000 | App/Admin server environment variable. Configure the root address of the AI Proxy service. Do not append paths such as /v1 or /chat/completions. This is not a single-model address |
AIPROXY_API_TOKEN | Consistent with the AI Proxy server configuration and non-empty | App/Admin server environment variable. This is the AI Proxy administrator token. It must match the AI Proxy server configuration and cannot be empty. This is not an application API key or model vendor key |
maxContext | 500–700 seconds | Reserve sufficient task execution time when processing large-scale employee contact list data and click statistics |
toolChoice | auto | Automatically call applicable tools to complete audience screening and data aggregation. This simplifies configuration complexity |
Three Common Misconfigurations
- Phenomenon: Generated phishing email templates have empty fields such as employee names and departments. Cause: No reasonable
maxContexttimeout threshold configured. Batch processing of employee data causes task interruption mid-process, and field mapping is not completed. - Phenomenon: Direct error occurs when uploading screenshots of drill scenarios to the model processing link. Cause: The
visionconfiguration item is not enabled, or the incoming image format does not comply with model-supported specifications. - Phenomenon: No response occurs after exceeding the set threshold during concurrent testing. Cause: The concurrency upper limit is not adjusted based on the
maxTokensvalue, or service connection exceptions occur due to incorrect configuration ofAIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables.
How to Confirm Proper Configuration
- Submit batch employee data for testing. Check if generated email templates correctly fill all preset fields. Confirm that the field mapping logic takes effect.
- Upload a test image in compliant format. Verify that the
visionconfiguration item normally triggers the visual processing flow, with no error returned. - Adjust the
temperatureparameter to different ranges. Compare generated email template content. Confirm that parameter adjustments affect output results as expected. - Simulate multiple concurrent requests. Observe interface return status. Confirm that requests do not experience no response due to configuration restrictions.
The values given are common starting points and should be measured 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.