Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from internal financial reimbursement management documents of Chinese public sector and state-owned enterprise entities. This includes official red-headed documents released by headquarters and branch offices, annually updated reimbursement guidelines, and temporarily released policy adjustment notices. Update frequency is at least one regular update per year. Additional updates are triggered immediately when national fiscal and tax policies are adjusted or internal enterprise processes change. Document structure follows a primarily chapter-based format, covering modules such as reimbursement scope, quota standards, ticket requirements, and approval workflows. Fields include reimbursement category, corresponding limit, compliant ticket type, approval node, and other relevant items. Common units are yuan, sheets, and workdays.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Since data is stored in a chapter-based structured format, multi-turn dialogue must track the current reimbursement module the user is asking about. This prevents mixing answer boundaries across chapters. Data update frequency is not fixed. Prompts must be configured with a mechanism to regularly sync the latest policies. This ensures responses match currently effective rules. Fields include clear amount thresholds and units. Multi-turn dialogue must accurately extract reimbursement amounts, ticket quantities, and other information provided by users. It must also calibrate unit consistency to avoid response errors caused by unit confusion. Some rules have branch conditions based on job level, department, and other factors. Multi-turn dialogue must gradually guide users to provide necessary information. This avoids missing key judgment basis.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier per employee session | Distinguish reimbursement consultation sessions across different employees, avoid confusion of context content |
messages | Retain the last 10 dialogue records | Cover multi-turn information transfer for reimbursement rule inquiries, track the current rule module the user is asking about |
variables | Pass the currently effective reimbursement policy version number | Ensure responses match the latest updated rules, adapt to data update rhythm |
| Maximum context length | 8000–12000 characters | Accommodate complete reimbursement rule document fragments and multi-turn dialogue history |
| Question optimization | Enabled | Convert colloquial reimbursement inquiries into structured queries, match the chapter structure of rule documents |
Three Common Misconfigurations
- Symptom: Calling the dialogue interface returns a 422 status code. No errors occur when testing the model independently. Cause: The
messagesorvariablesparameters are not passed correctly, or the parameter format does not meet FastGPT interface requirements. - Symptom: Multiple employee sessions share dialogue history and context content. Cause: A unique
chatIdis not generated for each employee. A globally unified session identifier is used for calls. - Symptom: Uploaded reimbursement rule documents cannot be parsed correctly. Cause: The uploaded file format does not match supported types, or the parsing switch for the corresponding format is not enabled in the configuration.
How to Verify Proper Configuration
- Initiate one round of reimbursement rule consultation. Check if the returned result matches the currently effective policy version. Confirm the
variablesparameter is passed correctly. - Initiate multiple consecutive consultation rounds. Check that dialogue history is not shared across sessions of different employees. Confirm the
chatIdconfiguration is correct. - After uploading the reimbursement rule document, initiate an inquiry about relevant content. Check if the system can accurately recall content from the corresponding chapters.
- Call the interface to initiate a dialogue. Check that the returned response format meets expectations, with no missing parameters or format errors.
The values provided here are common starting points. They should be evaluated against the samples used in the actual deployment.
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.