What data for this category looks like
Data typically comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders. It is updated daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification order notices, penalty decisions, and case closure reports. Core fields include case number, violation type, responsible entity, rectification progress, penalty result, and more. Most data is mixed data combining structured forms and unstructured images.
Constraints imposed by these characteristics on multi-turn dialogue and prompt settings
The mixed data structure and multi-source nature of this category require multi-turn dialogue to first clarify document type and data source. This avoids confusing fields between structured forms and unstructured images. The daily update rhythm of the data requires the dialogue link to limit the time range. Only case data from the current day or a specified cycle may be retrieved. This prevents cross-cycle redundant information from interfering with summary work. Field differences across multi-source data require prompt settings to preset cross-platform mapping rules. This unifies the expression of core fields such as case number and responsible entity. For unstructured images from on-site evidence collection, multi-turn follow-up questions must be used to extract key information such as location and violation details. This ensures complete ledger content.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window length | 8000–12000 characters | Covers the total content volume of multiple types of documents such as ledgers and penalty decisions for a single batch of daily updated cases, and aligns with the basic parameter range of FastGPT v4.17.0 |
messages historical conversation retention rule | Retain the last 7 conversations | Filters non-core prior interactions, focusing on the current case's ledger summary and report generation requirements |
variables bound parameters | Bind case date range variables | Limits dialogue to only processing law enforcement data within a specified cycle, preventing cross-day data from interfering with summary results |
| Single output maximum number of entries | Top 6 cases to be summarized | Matches the standard pagination display logic for urban management ledgers, preventing output content from being too long and truncated |
| Prompt trigger rule | Load dedicated templates by case type | Configure field extraction rules separately for two types of cases: urban environmental sanitation inspection and law enforcement penalty |
Three Common Misconfigurations
- Phenomenon: Knowledge base responses are truncated, only returning partial case ledger content. Cause: No reasonable context window length is configured. Document content exceeding the window limit is automatically discarded.
- Phenomenon: A 500 error is returned when importing urban management law enforcement case documents. Cause: No adaptation rules are configured for the mixed format (form + image) of urban management law enforcement documents. Unstructured image parsing fails.
- Phenomenon: Multi-module workflow outputs include results from all AI dialogue modules. It is not possible to retain only the content of the last one. Cause: No result filtering rules are configured for the workflow. No specification is made to retain only the output of the target dialogue node.
How to Confirm Configurations Are Properly Set
- Upload a single batch of daily updated urban management law enforcement documents. Check whether the ledger content returned by the dialogue covers all core fields.
- Trigger multi-turn dialogue. Verify that the output results do not include case data from non-specified cycles.
- Test multi-round interaction. Confirm that content returned after each follow-up question is only related to the current case's ledger summary.
- Check the parameter settings in the configuration interface. Ensure that the
variablesparameter is associated with the custom variable for case range. - Test the workflow scenario. Confirm that only the output of the last AI dialogue module is returned, with no redundant prior content.
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.