Data for This Category: What It Looks Like
Operational data for pharmaceutical preparations production originates from approved formulation processes, MES electronic batch records, equipment and environmental monitoring systems, LIMS laboratory information management systems, and quality management systems. Daily generated documents include master production files, batch production records, batch packaging records, cleaning records, deviation and CAPA change documents, and similar records. Core fields include material batch numbers, process execution parameters, test results, and equipment operation data. Updates occur per production batch, each key process step, and per established monitoring plans. Data must be generated synchronously with production operations.
Constraints on Model Access and Configuration
Data for this category is multi-source, heterogeneous, and must strictly comply with pharmaceutical production compliance requirements. Model access and configuration must support unified reading formats for data from multiple systems. Generated documents must also be fully traceable to their source data. Single-batch production data volumes are large, so sufficient context windows are required to hold complete batch record content. Document generation must maintain high rigor to avoid statements that conflict with compliance rules. Data updates happen frequently, so model call response speeds must match production rhythms to avoid delays in batch record review and release workflows.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | http://your-deployed-domain/v1 | Follows the path specification for general large model APIs. Adding /v1 to the end of the address ensures successful model calls |
AIPROXY_API_TOKEN | Enter the access key bound to the target model | Used to verify the legal identity of API calls, ensuring only authorized requests can trigger model generation |
maxContext | 8000–12000 token | Single-batch production records include multiple associated documents. Sufficient space is needed to hold complete source data fragments to support compliant traceability |
maxTokens | 2000–3000 token | Process documents and exception reports must comply with the length specifications for pharmaceutical industry documents, preventing generated content from exceeding reasonable lengths |
temperature | 0.1–0.3 | Pharmaceutical production documents must remain precise and accurate. Reducing the probability of randomized output ensures generated content meets compliance requirements |
quoteMaxToken | 4000 token | Sufficient source data reference fragments must be retained to meet compliance requirements for data traceability, avoiding truncation of critical reference information |
Three Common Configuration Mistakes
- API calls return a 404 error, with logs showing the path does not exist. Cause: The
/v1path was not added to the end of the API address, which does not follow model call interface specifications. - Generated reports omit source data reference fragments, making compliant traceability impossible. Cause: The
quoteMaxTokenconfiguration value is too low, truncating required reference data and violating data traceability rules. - Workflows trigger retrieval operations even when the knowledge base is empty, returning a no-results prompt. Cause: No branch logic was configured for empty knowledge base scenarios, leading to wasted retrieval resources.
How to Verify Correct Configuration
- Use the FastGPT built-in API testing tool, input test production data, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid, with normal model responses returned. - Upload complete single-batch batch record data, check that generated process documents include all core fields, and that referenced source data fragments are complete and traceable.
- Simulate an empty knowledge base scenario, trigger the exception report drafting process, and confirm that the workflow skips the retrieval step and directly runs the generation logic.
- Adjust the
temperatureparameter to 0.5, compare the rigor of the generated content, and confirm that the original configuration’s output contains no redundant or randomized statements.
The values provided are common starting points and should be measured against the reader's own samples.