What this category of data looks like
Data sources for this category include online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. The update frequency is daily. Daily documents include on-site inspection records, sampling records, monitoring reports, order for rectification decisions, and administrative penalty decisions. Data fields include monitoring point numbers, pollutant detection values, law enforcement implementation time, rectification requirements, and others. Units comply with the legal metrology specifications for corresponding pollutants. The length of individual documents varies widely, ranging from hundreds to thousands of characters.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
The daily update feature of the data requires multi-turn dialogue to retrieve the latest online monitoring and law enforcement system data in real time, and avoid using expired information. There are various document types including formal law enforcement documents, so prompts need to clearly distinguish the call priority of monitoring data, on-site records, and penalty decisions. Multi-source data needs to be bound to variables corresponding to law enforcement scenarios in the dialogue. variables can be bound to the current law enforcement point number to achieve data filtering. messages need to retain the context of previous law enforcement links to ensure coherent dialogue logic and avoid cross-scenario information confusion.
How to set up configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current law enforcement task | Ensure that multi-turn dialogue context for the same law enforcement operation is not confused with other tasks |
messages | Retain the first 3 rounds of law enforcement-related dialogue content | Match the short-process characteristics of on-site law enforcement, avoid information overload caused by overly long context |
variables | Bind law enforcement point number and detection date | Filter monitoring and law enforcement data for the corresponding scenario, narrow the recall scope |
| Maximum context per single dialogue | 8000–12000 characters | Adapt to the total length of multiple law enforcement documents and monitoring data, calibrated based on actual testing |
| File upload storage | Use MinIO object storage as the priority option | Adapt to large-volume files such as sampling photos and monitoring reports uploaded on-site during law enforcement |
| Dialogue timeout threshold | 60 seconds | Match the real-time response requirements of on-site law enforcement, avoid excessive waiting that disrupts the law enforcement process |
Three Common Misconfigurations
- Symptom: An error occurs when calling the document parsing tool after uploading a file via dialogue. Cause: The file storage path is not configured correctly, or the uploaded file is not associated with the
variablesparameter of the current law enforcement task. - Symptom: After initiating a dialogue via API, the reply does not include law enforcement document content from the knowledge base. Cause: The context of the law enforcement scenario is not bound in
messages, or monitoring data for the corresponding point is not filtered viavariables. - Symptom: An uncaught exception is thrown when running the AI dialogue component in a workflow. Cause: The docking address of the law enforcement data source is not correctly specified in the configuration file, or
chatIdis not bound to a unique task identifier, leading to context conflicts.
How to Confirm Proper Configuration
- Initiate a simulated law enforcement dialogue, check that
chatIdmatches the unique identifier of the current task, ensuring that context does not cross tasks. - Upload a test sampling record file, confirm that the file storage path is correct and can be read by the document parsing tool.
- Initiate a dialogue via API, verify that the reply includes monitoring data for the specified law enforcement point, confirming that
variablesfiltering takes effect. - Run a workflow test for the dialogue component, check that no uncaught exceptions occur, confirming that configuration file parameters are correct.
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.