What the data for this category looks like
Core data for this category comes from the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Updates occur once per month. Daily processed documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decisions, and rectification reports. Most documents combine structured and unstructured content aligned with audit workflows. Fields include audit number, basis for violation type determination, rectification feedback content, and additional relevant fields. Overall data scale changes dynamically based on monthly audit tasks.
What constraints these characteristics impose on model access and configuration
Multiple heterogeneous data sources require configuring parsing rules adapted to different formats, to ensure settlement data, rule base text, and other content can be correctly identified. The monthly update cycle requires the knowledge base synchronization frequency to match the data update rhythm, preventing the model from using expired supervision rules. Diverse document types and partially unstructured content require the model’s context processing capability to support long text splicing requirements. Errors in audit work directly impact fund safety, so model invocation parameters must strictly control generation accuracy to avoid incorrect policy interpretation.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length requirement after splicing monthly multi-source audit documents, ensuring full coverage of supervision rules and business data |
maxTokens | 2000–3000 token | Matches the standard answer length for policy Q&A, avoiding truncation of violation determination basis and rectification requirements |
quoteMaxToken | 1500–2000 characters | Ensures core content of a single supervision rule or audit document can be fully quoted, supporting answer accuracy and compliance |
toolChoice | auto | Supports automatic triggering of document recall and rule matching tools, adapting to multi-step Q&A requirements in audit scenarios |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring rigor of policy interpretation and audit conclusions, and meeting compliance requirements for supervision scenarios |
AIPROXY_API_ENDPOINT | Official access address of the corresponding model | Ensures stability and compatibility of the model invocation link, adhering to FastGPT proxy invocation specifications |
AIPROXY_API_TOKEN | Valid token assigned by the corresponding platform | Completes identity verification for model invocation, ensuring interface call security |
Three common mistakes
- Symptom: When uploading audit-related PDF or Word documents, an error "unsupported file format" or "upload failed" is returned. Cause: Parsing adaptation rules for the corresponding document type are not configured, or the uploaded file size exceeds the system's preset limit.
- Symptom: After configuring a model deployed via ollama and enabling the tool call switch, the document recall or rule matching process cannot be triggered, and empty tool call results are returned. Cause: The model version has compatibility issues with the
toolChoiceparameter, and the request format for tool calls is not correctly mapped. - Symptom: A 400 error is returned when accessing the model via a third-party proxy. Cause: The
AIPROXY_API_TOKENin the request parameters is not correctly configured, or the request header does not carry required identity verification information.
How to confirm the configuration is complete
Reference only; verify against the target FastGPT release and deployment documentation.
- Submit a test question with a known policy basis, check whether the model's returned content accurately links to the configured knowledge base content.
- Upload standard documents from this category, verify that the upload progress and parsing results match the expected format.
- Trigger a tool call test, check whether the model can correctly trigger the document recall or rule matching process.
- View the model invocation log, confirm that the invocation link for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhas no abnormalities.
The values given 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.