What this category of data looks like
This category of data typically comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders. The update frequency is daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Core fields cover case numbers, coordinates of illegal points, party subject information, evidence collection time, rectification requirements, and other related content. Some documents include multimedia evidence materials.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Data sources are scattered and include unstructured materials. Multi-turn dialogue must support context association across data sources. Daily updated case data fluctuates with inspection frequency. Prompt settings must adapt to dynamically input case information. There are diverse document types that must comply with law enforcement regulations. Multi-turn dialogue must guide the generation of corresponding materials step by step according to the case process. Multimedia data such as on-site photos must be associated with dialogue context. Prompt settings must clearly define rules for material calling and presentation to avoid generated content being disconnected from actual evidence information.
Configuration Settings
For FastGPT v4.17.0, refer to the following multi-turn dialogue and prompt engineering configuration for this category:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding | Bind to the unique number of the urban management law enforcement case | Urban management law enforcement cases have exclusive numbers, enabling precise association between single-case dialogue and data |
messages history call | Load the first 10 rounds of dialogue context | Covers dialogue information from core law enforcement processes including inspection, filing, rectification, and case closure |
variables parameter configuration | Inject case number, illegal point coordinates, party subject information | Ensures generated documents accurately match actual case content |
| Maximum dialogue context length | 10000 token | Accommodates multi-turn dialogue and case-related data to avoid context truncation |
| Timeout threshold | 600 seconds | Adapts to computational requirements for complex case document generation, covering most scenarios |
| Prompt trigger rule | Trigger according to law enforcement nodes | Matches document generation needs for different stages such as inspection, filing, and penalty |
Three Common Misconfigurations
- Phenomenon: Failed to obtain the dialogue record ID of the previous AI reply, and cannot associate the corresponding urban management law enforcement case data. Cause:
chatIdis not bound to the unique case number, and dialogue context is not isolated per single case. - Phenomenon: In the shared application, different users can view other users' law enforcement case dialogue records. Cause: Session isolation rules are not configured, and dialogue context is not distinguished by user or case ownership.
- Phenomenon: An abnormal error occurs when the dialogue generates documents, but no traceable log information is generated. Cause: The system log recording function is not enabled, so exceptions during dialogue and content generation cannot be captured.
How to Verify Correct Configuration
- Initiate a simulated urban management law enforcement case dialogue, check whether
chatIdmatches the preset case number, and confirm that the session association is correct. - Input variable information for different cases, check whether the generated documents accurately match the case number, point coordinates and other content of the corresponding case.
- Trigger an abnormal scenario such as inputting incomplete case information, check whether corresponding error logs are generated, and confirm that the log recording function works properly.
- Share the application and test with different accounts, check whether sessions are only visible to the current user, and confirm that the isolation configuration takes effect.
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.