What Data Belongs to This Category
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. The update frequency is monthly. Daily processed documents include audit notices, inspection work working papers, suspicious point lists, interview records, handling decision letters, and rectification reports. Each document includes fields such as suspicious point number, involved subject information, expense details, and basis for violation determination. Single case file materials vary widely in length. The core processing flow involves screening suspicious points from multi-source data and retrieving associated materials.
Constraints Imposed on Model Access and Configuration
Monthly updated data sources require the knowledge base sync cycle to match the update rhythm. This prevents the use of expired supervision rules and settlement data. Case file materials have multiple types and wide length ranges. They require the model's context capacity to adapt to long text processing needs. They also require support for multi-source data association verification. Fields for involved subjects and expense details require the model to accurately extract structured information. This avoids recognition deviations caused by fuzzy matching. It also requires strict control of the compliance of generated content.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single case file materials vary widely in length, need to accommodate multi-page text and associated data, adapt to long context processing requirements |
maxTokens | 2000–3000 token | Need to generate clear violation determination opinions and review conclusions, control single-round output length to avoid truncating key information |
temperature | 0.1–0.3 | Need to ensure the accuracy and consistency of violation determination, reduce deviations caused by random generation |
quoteMaxToken | 1500 token | Need to accurately reference specific clauses and amounts in the rule base and settlement data, limit the length of referenced content to ensure context rationality |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the channel | Adapt to access rules of different model channels, ensure correct request routing |
AIPROXY_API_TOKEN | Fill in the valid token allocated by the channel | Complete identity verification, ensure legal access permissions |
Three Common Configuration Errors
- Phenomenon: An authentication failure prompt appears when using AIPROXY to access the Volcengine Doubao model. The interface returns a 401 status code. Cause:
AIPROXY_API_TOKENis not filled correctly, a token that does not match the channel is used, or exclusive identity verification is not completed. - Phenomenon: A context overflow error occurs during tool calling. The task terminates automatically. Cause: The
maxContextparameter is not set reasonably. Insufficient space is reserved for tool calling intermediate output and original case file text. - Phenomenon: Configuration verification fails when trying to access the MiniMax model. The interface displays that the channel is not supported. Cause:
AIPROXY_API_ENDPOINTis not filled according to the requirements of the corresponding channel, or functional parameters supported by the model are not matched.
How to Confirm Successful Configuration
- Run a model access test. Check the response status of the interface. Confirm that
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTare configured correctly. - Import a single typical audit case file. Run the review task. Check whether the output content includes compliant violation determinations and supporting basis. Confirm that the context and output length parameters adapt to the document scale.
- Enable the tool calling function. Test the multi-source data association verification process. Confirm that the model can automatically retrieve relevant rules and settlement data according to the configuration.
- Adjust the
temperatureparameter to different ranges. Compare the consistency of multiple rounds of generation results. Confirm that the parameter value meets the accuracy requirements of review.
The values provided are common starting points. They should be evaluated against the samples used in the respective 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.