What Data for This Category Looks Like
Data for this category covers full production process documents from manufacturers of single-use consumables, implanted and interventional devices, diagnostic equipment, and similar products. Included documents are production process specifications, batch production records, clean zone monitoring records, sterilization batch records, inspection reports, software version records, and adverse event traceability materials. Data sources include design history files, device and material batch ledgers, MES systems, clean environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Data updates follow cycles per product batch or equipment unit, and are also synchronized regularly per environmental monitoring plans.
Constraints Imposed by These Characteristics on Model Access and Configuration
Documents in this category require association with precise fields such as batch numbers and unique identifiers. Data updates occur frequently, and the work involves high-risk links like aseptic processing and traceability. This places strict requirements on the accuracy of model input context and the timeliness of data. Multi-source and heterogeneous document formats and field requirements mean the configuration process must adapt to the structural characteristics of different documents. This prevents review failure caused by field mismatches. High-risk attributes mean model outputs must have traceable citation sources. They also require limiting randomness in generated content to avoid compliance risks from incorrect configuration. Additionally, the need to batch process multiple product batches means configurations must balance efficiency and stability for single-round processing.
How to Configure Settings
The following configurations are based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text cross-review needs for multiple batch records and process specifications, covers context association for full production process documents of a single batch, and ensures reviews cover complete production links |
maxTokens | 2000–3000 token | Limits the length of single-round review outputs, prevents generated content from exceeding the review boundary of compliant documents, ensures single-round processing efficiency, and adapts to resource usage requirements for batch reviews |
quoteMaxToken | 1500 token | Restricts the character length of referenced source documents, ensures referenced content can be accurately traced to corresponding batches or record entries, and meets traceability compliance requirements for medical device production |
temperature | 0.1–0.3 | Reduces randomness in generated content, ensures consistency of review conclusions, and meets compliance requirements for medical device production |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed model service address | Adapts to local or cloud-based model access requirements, supports configuration of Ollama service addresses for non-docker deployments |
AIPROXY_API_TOKEN | Fill in according to the authentication key of the model service | Completes identity verification for model access, ensures request legitimacy, and prevents unauthorized access |
Three Common Configuration Errors
- Phenomenon: When using a non-docker deployed Ollama model, call failures still occur after configuring
AIPROXY_API_ENDPOINT. Cause: The local service port of Ollama is not filled in correctly, or network access permissions for the corresponding port are not enabled. - Phenomenon: After adding a system prompt, the model output does not adjust the review logic as expected. Cause: The prompt is not configured in the dedicated system prompt input box, or is not associated with the current technical document review scenario.
- Phenomenon: Model configuration fails when accessing FastGPT via a domain name, and configuration data differs from that accessed via IP. Cause: The API proxy address bound to the domain name is not updated synchronously in the configuration, or the domain name is not correctly mapped to the deployment port of FastGPT.
How to Confirm Successful Configuration
- Check the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto confirm they match the actual deployed model service address and authentication key. - Initiate a single test request, input a cross-review question for one batch production record and process specification, and verify that the model output references content from the corresponding documents.
- Adjust the value of the
temperatureparameter, observe changes in the randomness of the model output, and confirm the parameter takes effect. - Verify batch processing requests for multiple documents, confirm that the system does not experience timeouts or content truncation.
The values provided here are common starting points, and should be evaluated against the operator’s own sample data.