What the data for this category looks like
Data sources for this category include the national medical security information platform, hospital HIS systems and medical insurance interfaces, medical insurance catalog and coding library, and cross-regional medical treatment settlement platform. Data is updated on a daily basis. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. All documents use structured format. Core fields include standardized diagnosis and treatment item/drug consumable codes, expense amounts, insured person information, and settlement time. No custom non-standard fields are included.
What constraints these characteristics impose on model access and configuration
Multiple data sources involving public service sensitive information require model access to comply with compliant interface nodes and permission controls. Daily updated data sources require the configured knowledge base sync frequency to match the daily update rhythm, to avoid data lag. Structured documents contain a large number of standardized coding fields, requiring the model context length to cover core information of multiple documents, to avoid truncating critical coding and expense data. For single or bulk settlement audit business scenarios, the model output needs to meet the length requirements of both single-item judgment and bulk reconciliation, while reducing random deviation to ensure audit accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers core context of multiple medical insurance settlement-related documents, avoids truncating standardized coding and expense information |
maxTokens | 4000–6000 token | Adapts to output length of single audit results, including code matching explanations, rejection judgment basis, and reconciliation content |
temperature | 0.1–0.3 | Reduces random generation probability, ensures consistency of code matching and expense audit results, meets business rigor requirements |
AIPROXY_API_ENDPOINT | Fill in the dedicated node provided by the platform | Complies with compliance requirements for medical insurance data transmission, avoids cross-region interface restrictions |
AIPROXY_API_TOKEN | Secret key bound to dedicated permissions | Ensures access security of medical insurance sensitive data, complies with public service data security specifications |
Three common configuration errors
- An error reading "Language model not configured" appears when accessing the workspace edit page. This is caused by incorrect filling of
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters, leading to the platform failing to recognize available models. - The AI model option is empty in the workflow problem classification menu. This is caused by incomplete model access setup in the global configuration, or the configured model not matching the permission scope of the current workflow.
- A prompt reading "Model stream response is empty, please check if model stream output is normal" appears. This is caused by
maxTokensset too small, leading to model output being truncated to empty, or an interface request timing out with no valid content returned.
How to confirm successful configuration
- The global model configuration page of FastGPT v4.17.0 is accessed, and
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are confirmed to be filled correctly, with no format errors or missing spaces. - A single medical insurance settlement list is uploaded to initiate a test, and the model output is checked for diagnosis and treatment item code matching results, expense and prescription consistency judgment content.
- Bulk settlement lists are imported for testing, and the model is confirmed to normally process multiple documents with no context truncation errors.
- Interface call logs are reviewed, and the request return status code is confirmed to be 200, with no timeout or permission error prompts.
The values provided are common starting points and should be validated against local deployment 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.