What the Data for This Category Looks Like
This category of data comes from administrative law enforcement case handling systems, law enforcement recorder footage from market supervision, ecological environment, transportation and other law enforcement agencies, the national enterprise credit information publicity system, and departmental supervision databases. Update frequency is daily. Daily documents include on-site inspection records, interrogation records, evidence lists, administrative penalty prior notice letters, penalty decisions, service receipts and more. Each document type has fixed fields such as law enforcement officer ID, inspected entity information, description of illegal facts, reference of discretion standards, and service date. A complete traceable penalty evidence chain must be formed.
What Constraints These Characteristics Impose on the "Workflow Orchestration" Link
Multi-source daily updates require the workflow to include scheduled pull nodes. This ensures evidence and document data stays timely. Multiple fixed fields and the need for an evidence chain require binding core fields of all document types through variables to prevent missing fields. Serious error consequences require adding verification nodes to block non-compliant content. Law enforcement scenario process continuity requires support for cross-node context persistence and data synchronization. It also requires isolation of case handling data across different law enforcement officers.
How to Configure the Settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables | Preset 12 core variables according to law enforcement scenarios | Matches fixed fields of more than 10 document types including on-site inspection records and penalty decisions |
nodeId | Bind to the evidence verification node and document generation node | Enables cross-node evidence chain data synchronization |
| Global variable scope | Isolate by law enforcement officer ID | Prevents case handling data of different law enforcement officers from interfering with each other |
| Context retention duration | 7200 seconds | Covers the complete process cycle of a single law enforcement case |
| Custom prompt configuration | Enable and bind to the AI reply node | Adapts to prompt requirements before legal review |
| Node call timeout | Calibrate based on actual measurements | Adapts to time requirements for law enforcement data pulling and document generation |
Three Common Misconfigurations
- Phenomenon: After calling the FastGPT workflow API, the returned result does not carry preset context content. The status code returns 200 but the result lacks pre-set law enforcement scenario information. Cause: Global context data bound by the
variablesparameter is not correctly passed in the request. - Phenomenon: When calling across conversations, the global variable fields of the corresponding node are empty. Previously saved case handling data cannot be loaded. Cause: Global variable scope is not configured to isolate by user ID. This leads to mutual overwriting of data from different law enforcement officers.
- Phenomenon: After the AI generates law enforcement documents, the configured custom prompt does not appear. Only the system default "questions you may want to ask" content displays. Cause: The custom prompt is not bound to the correct node ID.
How to Verify a Correct Configuration
- Initiate a simulated law enforcement case test call. Check if the returned result includes field content bound by the preset
variablesparameter. - Initiate calls using different simulated user IDs. Confirm identically named global variables do not interfere with each other.
- Trigger the AI reply node. Check if the configured custom prompt appears after the output result.
- View workflow operation logs for FastGPT v4.17.0. Confirm data synchronization between nodes bound by
nodeIdworks normally.
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.