Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for reimbursement rule Q&A originates from officially released internal corporate financial reimbursement management documents and intermittently updated reimbursement policy notices. The data update rhythm follows adjustments to corporate financial policies, with no fixed cycle. Content is updated synchronously after new policies are released.
The document structure is categorized by reimbursement scenarios, including major categories such as travel expenses, office expenses, and entertainment expenses. Each major category has detailed sub-items. Fields cover project name, per-person per-time limit, approval level requirements, and compliant voucher types. Units are mostly yuan, person-times, and calendar days.
What constraints these characteristics impose on the "model integration and configuration" link
Reimbursement rule Q&A data is categorized by scenario and includes clear amounts and level requirements. Model outputs must strictly match currently effective policies, and must not exceed given limits.
Data updates have no fixed cycle. Configurations must support quick synchronization of updated knowledge base content.
Rules vary significantly across different reimbursement scenarios. The model must accurately distinguish applicable conditions for different major categories, and avoid applying rules across scenarios.
Amount units in fields are uniformly yuan. The model must correctly identify and verify the correspondence between numerical values and units, to prevent incorrect responses caused by unit confusion.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Reimbursement rule documents are mostly structured content categorized by scenario. Sufficient context ensures the model obtains complete rule details |
maxTokens | 1500–2000 token | Responses to individual reimbursement Q&A need to clearly list limits and processes. This length covers conventional response needs and avoids redundancy |
quoteMaxToken | 600 token | Reimbursement rule entries that need to be cited are mostly short paragraphs. Limiting this parameter ensures cited content is precise and does not exceed the context range |
temperature | 0.1–0.3 | Reimbursement rules require strict compliance. A lower temperature value ensures response certainty and reduces random deviations |
toolChoice | auto | Auto mode allows automatic determination of whether to invoke knowledge base retrieval based on user questions, adapting to Q&A needs across different scenarios |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Used for identity verification, ensuring the security and legality of model calls |
AIPROXY_API_ENDPOINT | Fill in the actual address of the deployment node | Used to specify the interface routing address for model calls, ensuring requests are correctly delivered to the target model |
Three common configuration mistakes
- A
422 "Messages token length must"error is returned during invocation. The configuredmaxContextparameter value is too small to accommodate the user's question and cited rule document content, exceeding the context length limit supported by the model. - Requests have no response after configuring
AIPROXY_API_ENDPOINT. The interface address corresponding to the node was not filled in correctly, or network policies restrict access to this address. - The AI conversation node outputs redundant content. The
maxTokensparameter was not restricted, resulting in the model's generated response exceeding the required scope, and no result filtering rules were configured.
How to confirm the configuration is complete
- Submit a test question such as "What is the per-person per-day limit for travel expenses?" and check whether the response matches the currently configured knowledge base content.
- View the model call log to confirm that the configurations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave been correctly read, with no identity verification or routing errors. - Adjust the
temperatureparameter and observe the consistency of responses, confirming that the impact of parameter adjustments on responses meets expectations. - Trigger a knowledge base update operation, verifying that the configured update mechanism works correctly, and new policy content can be correctly retrieved by the model.
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.