What this category of data looks like
This category of data typically originates from urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, 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. Fields cover case number, violation location, responsible entity, rectification requirements, and disposal progress. There are no fixed, unified standardized unit fields, and some documents include multimedia attachments.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Data sources for this category are scattered, and the data includes both text and multimedia attachments. Multi-turn dialogue must support context association across different document types. Daily updated case data requires conversation contexts to be limited to a valid time range, to avoid referencing expired information. Document fields have no unified standards, so prompt engineering must guide the system to extract core fields such as case number and violation location from unstructured content. Multimedia content from on-site evidence collection must be associatively referenceable in dialogue, while prompt engineering must be constrained to generate basis only from official documents, preventing the production of unsupported conclusions.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Setting | Basis for This Setting |
|---|---|---|
| Maximum context length | 800–1200 characters | Adapt to the single-item content length of daily case documents, avoiding exceeding the model's processing limit |
| Number of retained historical messages | First 15 messages entries | This category of case data is updated daily, limiting recent conversations reduces interference from expired information |
| Preset variables | Case type, responsible entity, violation location | Guide prompt engineering to focus on core fields of urban management law enforcement, adapting to multi-turn follow-up scenarios |
| Session binding rule | Bind single case by chatId | Avoid incorrect association of cross-case data, aligning with the single-case processing logic of law enforcement scenarios |
| Output filtering configuration | Only retain conclusion and basis content | Filter redundant intermediate processing information, adapting to the quick reading needs of law enforcement personnel |
Three common configuration mistakes
- Phenomenon: Conversation contexts from different cases are mixed, and returned results include irrelevant case information. Cause: The rule to bind single-case sessions by
chatIdis not configured, leading to incorrect loading of cross-case data. - Phenomenon: On-site photos imported via Markdown format cannot be displayed normally, and image links lack complete domain names. Cause: The rule for automatically completing image domain names for variables is not configured, which only takes effect during Word import and does not adapt to the Markdown import scenario.
- Phenomenon: Tool execution logs are still output after tool calling, interfering with law enforcement personnel's reading. Cause: No output filtering rules are set, and the prompt engineering does not explicitly require returning only policy basis and conclusion content.
How to confirm the configuration is correct
- Initiate continuous follow-up questions for a single case, check whether the conversation context only includes information related to that case.
- Upload case documents in Markdown format, verify whether image links are automatically completed with complete domain names.
- Call tools to obtain policy basis, confirm that the output content only includes conclusions and basis, with no tool execution logs.
- Switch between Chinese and English input, check whether the conversation opening is correctly switched according to the preset configuration.
The values provided 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.