What data for this category looks like
Data for this category primarily comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. The update frequency is monthly. Commonly processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Core document fields cover institutional main body information, licensing qualifications, on-site inspection items, rectification requirements, and penalty-related content. The length of single case file documents varies widely, ranging from hundreds of characters of electronic records to multi-page paper scans.
What constraints do these characteristics impose on model access and configuration
Data sources are scattered and updated monthly. Configure stable incremental synchronization rules to match the update rhythm. Document types cover structured certificates and unstructured inspection records. Adapt multi-format text extraction logic. Single case file document length varies widely. Some penalty case file content exceeds the context limit of a single model. Configure context splitting and precise recall mechanisms. Core verification links involve institutional qualification and diagnostic behavior compliance judgment. These links have high requirements for model output accuracy. Adjust generation parameters to balance rigor and processing efficiency.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the context window limit of most public large models, adapts to penalty case files up to thousands of characters and multi-document batch review scenarios for this category |
maxTokens | 1500–2000 token | Controls the length of single-round generated output, avoids generated content exceeding the reasonable range of subsequent processing, fits the output requirements of compliance judgment tasks |
quoteMaxToken | 3000–4000 token | Limits the total length of recalled reference documents, ensures the matching degree between reference content and the context of the current review task, avoids redundant information interfering with model judgment |
temperature | 0.2–0.4 | Reduces generation randomness, improves output consistency for tasks such as qualification verification and compliance judgment, adapts to the review scenarios with high rigor requirements for this category |
AIPROXY_API_ENDPOINT | Fill in the corresponding interface address according to the deployment environment | Adapts to the model call path for local deployment or private deployment, ensures normal model access link |
AIPROXY_API_TOKEN | Fill in the authorization key of the privately deployed model | Ensures identity verification for model calls is passed, avoids unauthorized interface access |
Three common misconfigurations
- Model calls return context overflow errors, with status codes showing 400 or parameter limit exceeded. The root cause is failure to configure the
maxContextorquoteMaxTokenparameters, and failure to properly slice ultra-long case file documents. - Custom model icon configuration displays failed loading on the interface. The cause is directly using image links with non-HTTPS protocols, failing to use files in the required format, and not following the platform's specifications for icon resources.
- Generated review results deviate significantly from actual document content. The cause is that the
temperatureparameter is set too high, leading to excessive randomness in model generated content, which deviates from the rigor requirements of qualification verification and compliance judgment.
How to confirm the configuration is complete
- Import a typical penalty case file document for this category. Check if the system automatically completes text extraction and slicing. Confirm that the context splitting logic takes effect.
- Initiate a qualification verification test. Check if the model return result accurately matches the institutional license information in the imported document. Verify that the rigor settings of the generation parameters meet requirements.
- View the model call logs. Confirm that the interface address and key configuration are correct. Check for no failed call error records.
- Adjust the
temperatureparameter and initiate a test. Compare the differences in review results under different values. Confirm that the impact of parameter adjustment on output consistency meets expectations.
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.