What this category's data looks like
Data for this category comes from design history files, device and material batch records, MES systems, clean environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Daily documents include production process specifications, batch production records, clean zone operation records, sterilization batch records, inspection reports, software version records, adverse event and traceability materials. Data updates are performed per batch or per product. Clean environment monitoring data updates on a set schedule. Documents include fields such as batch number, unique device identification, clean environment parameters, sterilization duration, and inspection results. Some content exists in structured table format.
Constraints for Model Access and Configuration
Multi-source, scattered data sources require access configuration to support cross-system data pulling and integration. This avoids missing information from single data sources. Frequently updated batch records and environment data require configuration to support near-real-time knowledge base synchronization. This ensures audit results are based on the latest data. Diverse document formats and structured fields require model context processing configuration to support text and table content. It must accurately extract key parameters. Audit errors in this category can lead to serious compliance risks. Configuration must strengthen result traceability and the accuracy of compliance judgments, avoiding vague outputs.
Configuration Settings
| Configuration Item | Suggested Value | Basis for This Value |
|---|---|---|
maxContext | 8000–12000 token | This category of audits requires associating multiple batch records, sterilization and inspection reports, and needs to accommodate complete associated context |
maxTokens | 2000–3000 token | Inspection and process audits require detailed compliance judgments and traceability descriptions, requiring sufficient output length |
quoteMaxToken | 1500–2000 token | Need to accurately reference core traceable document fragments such as sterilization batch records and clean zone records, ensuring audit results are traceable |
temperature | 0.1–0.3 | Audit scenarios require stable and compliant judgment results, reducing output randomness and avoiding non-compliant judgments |
AIPROXY_API_ENDPOINT | Dedicated access address for deployment nodes | This category of data involves compliance traceability, requiring the use of controlled access addresses to prevent unauthorized access |
AIPROXY_API_TOKEN | Token bound with exclusive permissions | Strictly control model call permissions, meeting the security and compliance requirements of medical device data |
Common Configuration Mistakes
- Phenomenon: Calling a 70B-level large model results in a response timeout. The interface shows the request duration exceeds 60 seconds. Cause: The
maxContextparameter was not adjusted for large model load. Loading too many cross-batch associated data causes excessive inference resource consumption. - Phenomenon: The audit page embedded via iframe fails to load in a mini-program. The console displays a domain name verification failure. Cause: Security domain rules adapted for the mini-program were not configured, or an access method that meets mini-program embedding requirements was not used.
- Phenomenon: The model’s audit output does not reference specific document fragments. Cause: The
quoteMaxTokenparameter was not configured correctly, making it impossible to extract enough traceable document content for referencing.
How to Verify Successful Configuration
- Initiate an inspection audit request for a single batch of products. Check that the output result includes accurate reference fragments of the corresponding batch record and sterilization parameters.
- View the model call logs in FastGPT v4.17.0. Confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have no format errors, and no 401 or 403 errors occur. - Adjust the
temperatureparameter to 0.2. Initiate 3 duplicate audit requests. Check that the consistency of the results meets expectations. - Import a single clean zone monitoring record. Verify that the model can correctly identify whether the parameters meet the established process requirements.
The values provided are common starting points and should be measured against the reader's own samples.