What the data for this category looks like
The core data for this category includes basic government service item catalogs, implementation lists, service guides, standard material catalogs, flowcharts, and frequently asked questions. Data is primarily sourced from the national basic government service item catalog, administrative licensing item list, power and responsibility list, and local government service item databases. The standard update cycle is once per month. Document structures use item codes as the unique identifier, with fixed fields including acceptance conditions, application materials, and processing procedures. Some regional documents add exclusive handling rules.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
The monthly update cycle requires multi-turn dialogue processes to support regular pulling of the latest official catalog data, to avoid using expired item information. The fixed field structure requires prompts to strictly limit output formats, only allowing matching of preset item fields to prevent generating content that does not comply with document specifications. Cross-level catalog comparison requirements require distinguishing national, provincial, and local item rules during multi-turn dialogue, to ensure guided processes meet the requirements of the corresponding level. The high-frequency need for field verification can be addressed by presetting verification logic in prompts, which automatically prompts for missing required fields during dialogue.
Configuration Settings
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique code of a single government service item | Ensure dialogue context only associates with the currently processed item, avoiding interference across items |
variables | Preset item code and update time variables | Allow prompts to dynamically call exclusive data for the corresponding item, matching document structures |
messages | Retain the last 8 user and assistant interaction records | Control context scale to prevent redundant data from interfering with field verification logic |
| Single dialogue output limit | Top 3 matching documents | Government service documents are clearly structured; excessive results increase processing burden, calibrated based on actual testing |
| Context refresh cycle | Synchronize once every 24 hours | Match the overall monthly update rhythm to ensure timeliness of the underlying data used |
Three Easy-to-Make Mistakes
- Symptom: The dialogue process guides to operational links unrelated to the current item, such as triggering material verification as a case approval process. Cause: The prompt does not bind the item code variable in
variables, causing the classification logic to fail to match the specific item being processed. - Symptom: No response after dialogue is triggered, with a session timeout displayed on the interface. Cause: No reasonable context refresh cycle is set, exceeding processing time limits when batch pulling monthly updated government catalog data.
- Symptom: An extra duplicate interaction record appears in the session history after calling the API for dialogue. Cause: The submission logic for the
messagesarray is not properly controlled, leading to duplicate writing of session messages.
How to Confirm Proper Configuration
- Manually enter a known item code, check if the dialogue only returns document fields corresponding to that item, with no unrelated content appearing.
- Call the API with the
chatIdandvariablesparameters, confirm that the session context only associates with the current item, with no historical data from other items mixed in. - Trigger the simulation process for monthly data updates, check if the dialogue automatically pulls the latest catalog data, with no expired field information used.
- View the session log, confirm that the
messagesarray only retains the last 8 interaction records, with no redundant data present.
The values provided are common starting points and should be measured against relevant deployment 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.