What This Category of Data Looks Like
The data sources for this category include the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and report clues. The update frequency is monthly. Documents handled daily include audit notices, inspection work drafts, suspicious point lists, interview records, handling decision documents, and rectification reports. Data fields cover settlement details, violation judgment basis, rectification feedback, and other content. No unified fixed unit is used, and adjustments are made flexibly based on document type and data source.
What Constraints These Data Characteristics Impose on Model Access and Configuration
Full monthly batch data requires model configuration to support large context capacity, to avoid exceeding processing limits during single loading. Multiple types of heterogeneous documents need unified text processing rules to ensure consistent standards for identifying audit suspicious points across all document types. Scattered data from multiple systems requires multi-source access verification configuration to block non-compliant data from entering the model workflow. Post-audit traceability needs require the model to support long-context historical data calls, while strictly following access permission constraints for supervision data.
How to Set Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-document context length of monthly batch audits, covers the total character count of common audit reports and settlement data, and aligns with the default expansion limit of FastGPT v4.17.0 |
maxTokens | 2000 token | Controls the output length of a single audit conclusion, ensures compliance with the standard format requirements of handling decision documents, and avoids exceeding typesetting limits of supervision documents |
quoteMaxToken | 1500 token | Limits the fragment length of raw data referenced by the model, prevents truncation of key violation judgment basis, and ensures the rigor of audit conclusions |
temperature | 0.2 | Reduces output randomness, ensures consistency of audit judgments, and meets the rigor requirements of supervision processes |
AIPROXY_API_TOKEN | Fill in the exclusive token allocated according to permissions | Binds access permissions for supervision data, prevents unauthorized users from calling the model to process sensitive audit data |
Three Common Misconfigurations
- An error message "This token is not authorized to use the model" pops up during invocation. The cause is that an exclusive access token has not been allocated for the audit scenario, or the token configuration is not bound to the access rules of the internal data link.
- The interface displays no available channels. The cause is that the rhythm of monthly data updates is not adapted, and the regular refresh logic of the data source has not been configured, causing cached settlement data to fail to be read normally by the model.
- The audit conclusion generated by the model does not match the raw data. The cause is that
maxContextis set too small, truncating key content from the fund supervision rule base, leading to missing judgment basis.
How to Confirm Successful Configuration
- Import a single audit notice document, trigger a model test, and check whether the output result includes the key suspicious point information in the document.
- View the model call log, confirm that the request header of
AIPROXY_API_TOKENis correctly carried, and there are no permission error records. - Adjust the
temperatureparameter and test again, compare the consistency of the output results, and verify that the parameter takes effect. - Import a monthly batch data sample, check whether the total processing time of the model meets the process requirements of internal office work.
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.