What the data for this category looks like
Business data for urban sanitation and urban management law enforcement cases typically comes 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. Each document has corresponding business fields: inspection records record inspection locations and problem descriptions, filing approval forms associate case numbers and responsible entities, and on-site photos include shooting time and location information.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources and multi-format content including text and images require multi-turn dialogue to associate document content from different business systems, avoiding information silos. The daily update feature limits dialogue context to the recent business cycle, reducing interference from outdated data. Document fields cover the entire business process, so prompts must clearly guide retrieval of information from corresponding fields to assist in judging violation types and responsible entities. The presence of unstructured data such as on-site photos requires the dialogue flow to adapt to retrieval and parsing logic for image-based knowledge bases.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the content density of text documents such as inspection records and penalty decisions |
| Overlap Length | 100–200 characters | Ensures cross-chunk case numbers and location information are not lost |
| Single Retrieval Limit | Top 6 entries | Matches the number of document types associated with a single case |
| Context Window Size | Top 20 dialogue turns + documents from the past 3 days | Controls dialogue context scope via the messages parameter, adapting to daily updated business data |
| Variable Binding | Bind case number, inspection location | Passes business-specific fields via the variables parameter to support precise retrieval |
| Chat ID Association | Bind unique law enforcement case identifier | Associates dialogue with a specific case via the chatId parameter to avoid context confusion |
Three Common Misconfigurations
- Uploading documents via the dialogue interface triggers a 503 error, though backend logs confirm the file upload succeeded. This happens because the dialogue flow does not properly configure the context caching logic for the
messagesparameter, incorrectly triggering the file upload interface’s timeout threshold. - Using a strict question-and-answer template results in a "no answer found" prompt after retrieving knowledge base content with image addresses. This happens because the prompt does not adapt to parsing rules for image data, and only configures retrieval matching logic for text content.
- Multi-turn dialogue experiences context confusion, mixing information from different cases. This happens because the dialogue flow does not bind the unique case identifier via the
chatIdparameter, failing to isolate session contexts.
How to Verify Proper Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a single inspection record document, confirm the dialogue retrieves the inspection location and problem description fields from the document.
- Enter a query containing a case number, confirm retrieval results only return documents such as the filing approval form and penalty decision associated with that case number.
- Upload an on-site photo file, test across different dialogue scenarios, confirm the system correctly parses and includes image information in the dialogue context.
- Check the session configuration, confirm the
variablesparameter binds case-specific business fields, and thechatIdparameter updates automatically with each dialogue.
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.