Data Profile for This Category
Data for this category is primarily sourced from the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and complaint leads. The update frequency is monthly. Daily processed documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decision letters, and rectification reports. Most fields cover violation types, expense amounts, and information of involved institutions and personnel. Some documents are structured forms, while others are unstructured text records, with no unified fixed format.
Constraints for Model Access and Configuration
The monthly data update cycle requires that the knowledge base linked to the model must sync on a monthly basis to prevent calling expired supervision rules. Mixed structured and unstructured document types require model configuration to support both text semantic understanding and structured field extraction. The need to correlate multi-source data requires a sufficiently large context window to accommodate cross-source audit materials, preventing key audit information from being truncated. Additionally, audit results directly relate to fund loss risks, so model configuration must balance output accuracy and compliance, and avoid generating unfounded judgments.
Setting the Configuration
In the FastGPT v4.17.0 model configuration interface, reference the following configuration items and recommended values:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Carry multi-source cross-page audit materials such as audit notices and settlement data to avoid context truncation |
maxTokens | 2000–3000 token | Meet the output length requirements for generating audit opinions or suspicious item analysis, and avoid forced truncation of outputs |
quoteMaxToken | 1500 token | Ensure that sufficient rule base and settlement data fragments are referenced as audit basis, and retain key reference content |
toolChoice | auto | Support the model to independently choose whether to call knowledge base retrieval or rule matching tools, and adapt to the business needs of multi-source data association |
temperature | 0.1–0.3 | Audit work must strictly follow supervision rules; a low temperature value can improve output stability and compliance |
AIPROXY_API_ENDPOINT | http://localhost:8000/v1 | Adapt to locally deployed inference services, and ensure that request routing correctly matches the service address |
Three Common Misconfigurations
- Symptom: The model call returns a 404 status code or a connection timeout prompt. Cause:
AIPROXY_API_ENDPOINTis not configured correctly, and the filled address does not match the actual listening port or request path of the locally deployed model. - Symptom: Knowledge base retrieval takes too long. Cause: The
maxContextparameter is not set reasonably, resulting in loading too much redundant data for each retrieval and increasing processing overhead. - Symptom: The generated audit opinions include unfounded violation determinations. Cause: The
temperaturevalue is set too high, causing excessive randomness in model output and deviating from the strict requirements of supervision rules.
How to Verify a Successful Configuration
- Call the model test interface, input a single audit work paper, and confirm the returned result properly generates a suspicious item analysis with no connection errors or content truncation prompts.
- Manually trigger knowledge base synchronization, and check that the synchronization log includes the latest fund supervision rule base content to meet the monthly update frequency requirement.
- Adjust the
temperatureparameter across different ranges, compare the consistency of the generated audit opinions, and confirm the value aligns with compliance requirements. - Validate the validity of
AIPROXY_API_TOKENby sending a request with an incorrect key, confirming an unauthorized error prompt is returned to ensure access security.
The values provided here are common starting points and should be evaluated against samples specific to the target 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.