What data looks like for this category
Relevant data for pharmaceutical dosage form manufacturing originates from approved formulation processes, material batches, MES/electronic batch records, equipment and environmental monitoring data, LIMS, and quality management systems. Daily documents include master production files, batch production records, batch packaging records, cleaning records, inspection records, deviations, CAPA, and changes, as well as batch release approval forms. Data is updated per production batch, each critical process step, and per established monitoring plans. Data fields include material batch numbers, weighing parameters, process execution parameters, test results, deviation details, and more. This data must be stored synchronously with production operations.
Constraints for model integration and configuration
Audit scenarios for this category require strict association with batch information. Model integration must support linking multi-source heterogeneous data by batch dimension. Data updates happen frequently and must sync with production operations, so configured data source synchronization mechanisms must support near-real-time performance. Document structures are complex and fields are highly specialized, so the model must accurately identify key compliance fields such as material batch numbers and weighing data. Audit results directly impact batch release, so model output must have high reliability, and the randomness of generated content must be strictly limited. Additionally, compliance requirements mandate synchronous document storage and operation tracking, so documents processed by the model must fully cover all production process nodes without missing critical links.
How to set configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate complete batch records and associated deviation, test data for a single batch, to avoid truncation of critical audit information |
maxTokens | 4000–6000 token | Must cover the complete conclusions and supporting evidence for batch audits, to avoid exceeding the model’s output limit |
quoteMaxToken | 2000 token | Must retain complete source document fields such as material batch numbers and weighing parameters, to ensure accurate and compliant citations |
temperature | 0.1–0.3 | Audit scenarios require precise output, reducing the randomness of generated content to ensure consistent judgment |
| Workflow/API tool selection | auto | Automatically call document parsing tools to process multiple types of documents such as batch records and test reports |
AIPROXY_API_ENDPOINT | Fill in according to actual deployment, such as http://localhost:8080/v1 | Connect to local or cloud-based model proxy services, ensuring correct interface access paths |
AIPROXY_API_TOKEN | Fill in the security token according to deployment configuration | Ensure access permissions for model interfaces are controllable, preventing unauthorized calls |
Three common configuration errors
- Model icons do not display in workflows, with no normal loading status. This occurs when
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, preventing the model channel from completing initialization. - File upload prompts a network error. This occurs when correct document parsing tool access parameters are not configured, or the port of the locally deployed MinerU service is not properly connected to the FastGPT container network.
- Index model channel construction fails. This occurs when
maxContextis not set to a value adapted to batch record length, triggering a context truncation error during index construction.
How to confirm successful configuration
- Check the model channel list in FastGPT v4.17.0. Confirm the target model displays normally with no error prompts.
- Upload a single batch production record document. Confirm that key information such as material batch numbers and process parameters can be extracted normally after parsing.
- Initiate a batch audit test. Confirm that the output result cites specific fields from the source document, with no irrelevant redundant content generated.
- View system operation logs. Confirm there are no records of model call timeouts, interface errors, or document parsing failures.
The values provided are common starting points and should be measured against the reader's own samples.