Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Business data for urban sanitation and urban management law enforcement cases typically originates from urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, and public complaint work orders. The system updates this data daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. The overall structure comprises two categories: structured law enforcement forms and unstructured attachments. These documents usually include common fields such as case ID, on-site location, and disposal process records. Some documents also include multimedia evidence materials.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
Scattered data sources and multiple format types require multi-turn dialogue to support association and parsing of mixed contexts. Daily updated business data requires dialogue contexts to tie to time-sensitive case files from the current day or past three days, to avoid using expired data. The rigor of law enforcement documents requires prompt engineering to align with compliance verification logic. This logic lets the dialogue process automatically verify material completeness. The process-oriented nature of case disposal requires dialogue history to link to a unique case ID, preventing confusion across cases.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum single-turn dialogue context | 800–1200 characters | Matches the typical length of a single urban management law enforcement document, to avoid exceeding model processing limits |
messages context sorting | Arranged by case disposal process | Ensures dialogue history is organized in the order of filing, evidence collection, and disposal, improving model understanding efficiency |
| Dialogue history retention period | 7 days | Corresponds to the typical disposal cycle of urban management cases from filing to closure, covering core processes |
| Timeout threshold | 600 seconds | Prevents dialogue interruptions caused by network or model response delays, adapting to the real-time requirements of law enforcement scenarios |
chatId binding rule | Associated with unique case ID | Ensures dialogue history for the same case is correctly linked, avoiding cross-case context confusion |
variables data scope | Associated case files from the past three days and current day work orders | Follows the daily update feature of data, ensuring variables use the latest business information |
Three Common Misconfigurations
- Issue: Generated file download links are inaccessible, with a prompt stating the link has expired. Cause: The default validity period configuration for dialogue files was not adjusted. The default validity period is too short to cover the full case process.
- Issue: Case dialogue initiated several months ago cannot continue to associate with currently updated case files. Cause: The dialogue history retention period configuration is too short, failing to retain cross-cycle case context.
- Issue: Single-turn dialogue response time exceeds 10 seconds, exceeding the acceptable range for business operations. Cause: The time range for associated data was not limited, and full historical case files were bound, leading to excessive model retrieval load.
How to Verify Correct Configuration
- Initiate a new urban management law enforcement case dialogue, and check if the context automatically associates with that day's inspection records and complaint work order data.
- Review the save configuration in dialogue logs, and confirm the retention period meets the disposal requirements for this category of cases.
- Switch dialogues for different cases, and verify that the
chatIdbinding correctly matches the case ID, with no cross-case context interference. - Upload on-site photos and law enforcement documents to initiate a dialogue, and check if the model can correctly identify key information in the documents.
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.