What this category of data includes
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints for model access and configuration
Real-time monitoring data updated daily requires model access to support stable high-frequency calls, and prevent single request timeouts. Handling mixed multi-type documents requires configuring context parameters adapted to different formats. This ensures accurate recognition of both colloquial on-site record content and standardized fields from formal law enforcement documents. Quantified data with units requires the model to correctly match values and pollutant types. Set explicit context token limits to avoid truncating critical data. The rigor of law enforcement documents requires model output to link back to original data sources. Configure relevant citation parameters to ensure review results are traceable.
Recommended Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate complete content from multiple on-site records and monitoring reports, avoid truncating critical law enforcement data |
maxTokens | 2000–3000 token | Used to generate review conclusions or supplementary content for law enforcement documents, matches standard output length for single documents |
temperature | 0.1–0.3 | Review scenarios require precise output, reduce randomness to ensure consistency between monitoring data and judgment results |
AIPROXY_API_ENDPOINT | Fill in the proxy interface address deployed for FastGPT | Adapt to self-hosted or hosted model call links, ensure data is not directly exposed |
AIPROXY_API_TOKEN | Use a key injected via environment variables | Avoid hardcoding credentials, comply with security and compliance requirements |
toolChoice | auto | Automatically select whether to call tools, adapt to multiple requirements such as online data comparison and document parsing |
Three Common Configuration Errors
- Phenomenon: An error indicating unsupported parameters is returned when calling certain commercial AI models. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the proxy link does not adapt to the protocol format of the corresponding model. - Phenomenon: No logs are generated during model calls, making parameter abnormality troubleshooting impossible. Cause: Request log printing is not enabled in the configuration, and no link log middleware is added.
- Phenomenon: Parameter verification fails when calling multimodal models. Cause: The value range of
maxTokensortemperatureis not set correctly, and values exceed the parameter ranges supported by the model.
How to Verify Successful Configuration
- Initiate a single test call, pass preset on-site inspection records and corresponding monitoring data, and confirm that the returned review conclusion links to original data fields.
- View interface call logs to confirm that proxy interface and key parameters are correctly included in request headers.
- Adjust the
temperatureparameter value, observe changes in output stability, and verify that the parameter takes effect. - Submit batch test requests to confirm that the call link has no persistent errors, and adapts to high-frequency data update scenarios.
The values provided here 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 2026-09-28.