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, and is updated daily. Common documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Core fields cover case number, violation type, responsible party, on-site location information, rectification execution status, and more. Each document includes both structured ledgers and unstructured image and text description materials.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
This category of data includes both structured ledgers and unstructured image and text materials, and is updated daily. This requires multi-turn dialogue to automatically distinguish document types from different sources to avoid confusing field logic. Core business steps are concentrated on on-site evidence collection and location, violation type determination, and rectification tracking. Prompts must guide dialogue to focus on these three key actions to avoid deviating from the business process. Data sources are scattered and involve law enforcement compliance, so multi-turn dialogue must automatically associate historical records of the corresponding case to ensure the context matches the current law enforcement scenario, preventing missing evidence or incorrect responsible party determination.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chatId | Bind to the unique number of the current case | Ensure multi-turn dialogue context for the same case is associated, avoiding confusion across cases |
messages | Retain the first 20 rounds of dialogue and associated document fragments | Cover core process nodes from case filing to closure, preventing context loss |
variables | Inject case number, responsible party field, latest rectification status | Enable prompts to accurately call exclusive data for the current case, improving determination accuracy |
| Context retention duration | 7 days | Match the FastGPT v4.17.0 default CHAT_FILE_EXPIRE_TIME=7 rule, covering the typical case handling cycle for this category |
| Per-round token limit | 8000–12000 tokens | Balance loading of structured documents and image-to-text content, avoiding exceeding the model's carrying capacity in a single call |
Three Common Misconfigurations
- Phenomenon: Token limit exceeded error is returned during dialogue calls, with status code 413. Cause: The per-round dialogue token limit is not restricted, causing loaded case documents and historical dialogue to exceed the model's carrying capacity.
- Phenomenon: Past case dialogue context cannot be retrieved after 7 days. Cause: The FastGPT v4.17.0 default
CHAT_FILE_EXPIRE_TIME=7setting is used without matching the actual handling cycle of cases in this category. - Phenomenon: Context confusion occurs in the execution results of multi-turn dialogue in batch execution nodes. Cause: The exclusive case number is not bound via
chatId, causing dialogue context from different cases to overwrite each other.
How to Verify Successful Configuration
- Enter the unique number of a test case, check if the dialogue automatically associates historical documents and exclusive fields for the corresponding case.
- Adjust the context retention duration, verify that test cases older than 7 days can still retrieve dialogue records normally.
- Run a batch test script, check that dialogue for different cases maintains independent context with no field confusion.
- View the per-round dialogue token consumption statistics, confirm that the preset per-round limit is not exceeded.
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.