What this category of data looks like
This category of data mainly comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records, with a monthly update frequency. Commonly processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data fields cover institutional main body information, licensed qualification scope, on-site inspection items, rectification requirements, penalty records, and other content. Some documents are scanned copies or semi-structured text with no unified fixed format.
What constraints do these characteristics impose on model access and configuration
The data sources for this category are scattered and the formats are inconsistent. This requires model access configuration to support multi-source document parsing and unstructured content adaptation. The monthly update frequency requires the knowledge base synchronization cycle to match the monthly update rhythm to avoid data lag. Documents include scanned non-text content, so visual recognition capabilities must be enabled to process related materials. Long-text case files and rectification documents account for a large proportion. This requires limiting the maximum context length per round of processing to avoid timeouts. Accurate reference to original policies and law enforcement documents as a basis requires retaining sufficient reference token quotas to ensure the compliance and credibility of responses.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category has a large number of long-text case files and rectification documents, which need to cover complete law enforcement records and original policy text while avoiding context overflow |
quoteMaxToken | 2000–3000 token | Sufficient original document fragments must be retained for reference to meet the compliance requirements of law enforcement basis Q&A |
vision | Enabled | This category includes non-text documents such as scanned practice licenses and rectification notices, so visual recognition capabilities must be enabled to extract content |
temperature | 0.1–0.3 | Policy and regulatory basis Q&A requires strictness to avoid generating content that deviates from original documents |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address provided by the FastGPT v4.17.0 platform | Ensures model call stability and permission control |
AIPROXY_API_TOKEN | Fill in the dedicated key assigned by the FastGPT v4.17.0 platform | Implements identity verification and access control for model calls |
Three common mistakes
- Symptom: The target model can be selected in the model list, but it cannot be called after creating a new workflow. Cause: The access permission configuration for the corresponding model has not been completed, or the configuration items are not correctly associated with the workflow node.
- Symptom: Q&A response takes too long. Cause: The
maxContextvalue exceeds the range supported by the model, resulting in excessive long-text processing load and slowing down response speed. - Symptom: Third-party testing passes but an error occurs during FastGPT internal testing. Cause: The
AIPROXY_API_TOKENis filled incorrectly, or theAIPROXY_API_ENDPOINTdoes not match the access address of the target model.
How to confirm that the configuration is complete
- Verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the information assigned by the FastGPT v4.17.0 platform to complete basic access verification. - Upload typical documents of this category, such as scanned practice licenses and rectification notices, to test whether the
visionfunction can normally extract text content. - Initiate a policy basis Q&A test to check whether the document fragments referenced in the response are complete, and confirm that the
quoteMaxTokenvalue meets requirements. - Initiate a long-text case file Q&A test to check whether the response duration meets business expectations, and confirm that the
maxContextvalue does not exceed the model's processing limit.
The values provided here 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.