What the data for this category looks like
Core data for this category comes from elderly care service information platforms, civil affairs statistics systems, fire safety inspection records, and facility operation ledgers. Update frequency is typically monthly.
Daily processed documents include elderly care facility filing materials, operation subsidy application materials, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. Data fields include facility qualification numbers, approved bed counts, inspection and rectification items, elderly health status, admission cycles, and more. There is no unified mandatory format, but documents must meet filing and verification requirements from oversight authorities.
Constraints imposed on multi-turn dialogue and prompt engineering
Monthly updated data sources require that multi-turn dialogue context be limited to the latest available data from the current month. This avoids referencing expired filing or rectification information.
The mixed structure of multiple document types requires prompts to clearly distinguish call priorities for different documents. For example, first retrieve filing materials to verify facility qualifications, then match rectification ledgers to check safety items.
Non-standardized fields require adding field verification steps in the dialogue flow. This ensures extracted information such as facility numbers and bed counts meets regulatory formatting requirements. Multi-turn interactions must retain context to link verification data across stages, avoiding repeated questions to users.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Split variables into three categories: filing, rectification, and subsidy, and bind corresponding data source fields | Matches the multi-type business document structure of elderly care service oversight, accurately extracts fields such as qualifications, bed counts, and subsidy amounts, and adheres to the variable binding rules of FastGPT v4.17.0 |
messages | Retain the first 3 rounds of core verification dialogue content, filter redundant inquiries | Monthly updated data sources do not require loading full historical records; limiting the number of rounds reduces computational overhead and improves dialogue response speed |
| Maximum context length | 800–1200 characters | Adapts to the average length of a single regulatory document, avoids exceeding the model’s processing limit, and ensures complete content parsing |
chatId | Bind the facility qualification number as the unique session identifier | Ensures accurate context association across multi-turn dialogues for the same facility, prevents cross-facility data confusion, and complies with regulatory data isolation requirements |
| Variable verification trigger rules | Calibrated based on actual testing | Adapts to non-standardized regulatory document field formats, flexibly adjusts verification logic to match regulatory requirements of different regions |
Three Common Misconfigurations
- Issue: After calling the API with the
variablesparameter, returned results do not replace preset variables. Cause: Variables are not correctly bound to corresponding fields of elderly care regulatory data sources, or parameter format does not comply with FastGPT v4.17.0 specifications. - Issue: No valid information is extracted via text content during multi-turn dialogue, and the session terminates directly after triggering a specified reply. Cause: The wait-for-user-input node of the dialogue flow is not configured, and session context is not retained for subsequent interactions.
- Issue: After deploying a local model, the model only returns Chinese content even when the prompt requires English output. Cause: The prompt does not explicitly bind knowledge base retrieval rules for the target language, or the default output language of the model is not adjusted.
How to Confirm Proper Configuration
- Call the test API with the
variablesparameter, verify that returned results correctly replace regulatory fields such as filing numbers and bed counts. - Simulate a text extraction failure scenario, check that the system triggers a specified reply and waits for user input of new content.
- Configure an English prompt and upload English regulatory documents, verify that the model output language meets preset requirements.
- Bind the facility qualification number as
chatId, initiate a multi-turn dialogue, and confirm that context correctly associates verification data for the same facility.
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.