What Data for This Category Looks Like
Reference only; verify against the target FastGPT release and deployment documentation.
What Constraints These Characteristics Impose on Model Access and Configuration
Multi-source heterogeneous data sources require model access to adapt to different document parsing formats. The monthly update rhythm means the knowledge base synchronization cycle does not need to be overly short, and must match the monthly audit rhythm of supervision work. Professional content such as violation determination and expense accounting requires associating the built-in fund supervision rule base as a knowledge base source during configuration, to avoid general knowledge bias. Audit documents involve sensitive supervision data, so access configuration must prioritize data permission isolation, and support accurate recall of core documents including doubt lists and handling decision letters.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text context requirements of audit documents, covering associated content from multiple doubt lists and inspection work drafts |
maxTokens | 4000 token | Controls the output length of single-round responses, matching the structured output requirements of audit responses and avoiding redundant content |
quoteMaxToken | 2000 token | Limits the total length of recalled knowledge base content, ensuring that core supervision rules and settlement data fragments can be fully embedded into the context |
toolChoice | auto | Allows the model to independently choose whether to call tools, adapting to scenarios in audits that require checking settlement data and traceability code information |
temperature | 0.2–0.4 | Reduces the randomness of responses, ensuring the rigor of audit conclusions and complying with compliance requirements of supervision business |
AIPROXY_API_ENDPOINT | Calibrated based on actual testing | Adapts to access requirements of local or third-party compliant APIs, meeting the local deployment requirements of medical insurance data |
Three Common Configuration Mistakes
- Phenomenon: After a manual transfer request is triggered, the response is still output by the large model, and the switch to the manual customer service channel does not occur. Cause: The rule conditions for triggering manual transfer are not configured, or the transfer logic is not bound to the specific trigger words for audit consultation.
- Phenomenon: The response content of the locally deployed model does not synchronize with the latest medical insurance supervision rules on the server. Cause: The automatic synchronization trigger timing of the knowledge base is not configured, or the synchronization cycle does not match the monthly update rhythm of supervision rules.
- Phenomenon: An unauthorized error is returned when calling the API. Cause: The API key is configured incorrectly, or the key permission does not cover the access scope of audit data.
How to Verify Successful Configuration
- Manually upload an audit notice document, test whether the model can accurately recall the associated fund supervision rule content, and check whether the recalled knowledge base fragments match the document fields.
- Configure a test conversation, input a consultation question related to violation determination, check whether the response triggers the tool call logic, and verify that the automatic tool selection function works.
- Adjust the
temperatureparameter value, compare response consistency across different values, and confirm that the impact of the parameter on output rigor meets business expectations. - Trigger a knowledge base synchronization task, check whether the synchronization log shows that the supervision rule base has been updated successfully, and verify that the configured synchronization cycle matches the monthly update rhythm.
The values provided here 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.