What data for this category includes
Data for this category comes from online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports, with a daily update frequency. Daily produced documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. On-site inspection records include fields for law enforcement personnel information, inspection locations, and working condition descriptions; sampling records include fields for sample number, sampling time, and test items; monitoring reports include pollutant concentration values and corresponding testing units; law enforcement documents include structured content such as facts of violations and rectification requirements.
Constraints for multi-turn dialogue and prompt engineering
Data sources for this category are scattered and updated daily, requiring multi-turn dialogue to support real-time retrieval of the latest daily online monitoring, law enforcement system, and third-party test data, to avoid using outdated or mismatched information. Daily documents include multiple types of structured fields, so prompt engineering must clearly specify required fields and association rules for each document, for example, binding the pollutant concentration of a monitoring report to the sample number of a corresponding sampling record. Law enforcement documents must strictly match evidence data collected on site, and multi-turn dialogue must verify consistency between fields and data sources after generating intermediate content, to prevent mismatched evidence. At the same time, the process must support hiding unnecessary intermediate processing steps to ensure final output documents only contain compliant content.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind a dedicated variable group for daily online monitoring, automatic pollution source monitoring, and law enforcement systems | Real-time calls to daily updated category data are required to ensure document content matches the latest monitoring and law enforcement information |
messages | Retain 8–12 rounds of previous conversation context | Drafting law enforcement documents requires associating on-site data collected across multiple rounds; this range ensures logical coherence without occupying excessive resources |
| Maximum context length | 8000–12000 tokens | Structured content from multiple monitoring reports and sampling records must be accommodated, to avoid information loss caused by context overflow |
| Hide intermediate dialogue output | Enable hiding of non-essential steps | Law enforcement processes only require final compliant documents; hiding intermediate outputs such as data verification and format adjustment simplifies delivered content |
chatId | Bind a unique identifier for a single law enforcement task | Ensure multi-turn dialogue contexts for the same law enforcement task are not confused, preventing data mixing between different law enforcement tasks |
| Call timeout threshold | 600 seconds | Multiple scattered data sources need to be called; a longer timeout ensures complete retrieval of daily monitoring and law enforcement data |
Three common configuration mistakes
- Hiding intermediate dialogue output in configuration still results in intermediate processing steps appearing in the final result. Cause: Output filtering rules for dialogue are not correctly configured, or execution logic for skipping intermediate steps is not set in the workflow.
- A
connection errorprompt appears when calling the model, and the issue persists after enabling a proxy. Cause: Network parameters for model calls are not correctly configured, or access permissions for data source interfaces are not opened to the corresponding environment. - The generated monitoring report does not match the sample number of the corresponding sampling record. Cause: Prompt engineering does not clearly specify association rules for bound data source variables, or complete field information for sampling records is not entered in
variables.
How to verify correct configuration
- Initiate a simulated law enforcement task dialogue, check whether intermediate processing steps are hidden as configured, and confirm that output only includes final document content.
- Trigger a model call, observe whether a
connection errorprompt appears, and troubleshoot network and configuration parameters if the prompt appears. - Generate a test document, verify that fields in the output match the content of bound data source variables, and confirm that association rules are effective.
- Check the
chatIdidentifier of the dialogue, confirm that multi-turn dialogue contexts for the same task are not confused with those of other tasks.
The values provided 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 2026-09-28.