What data for this category looks like
Data sources for this category include cell banks and seed lots, process analytics, cleanroom monitoring, MES, LIMS, and equipment integrity testing systems. Update frequencies cover each production batch cycle, real-time monitoring data, and environmental monitoring plans executed on fixed schedules. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring reports, media fill simulation data, sterility test reports, and batch release documents. Fields include batch number, culture temperature, cleanroom colony count, equipment integrity verification results, and other related fields, with dedicated units such as temperature, duration, and colony-forming units.
What constraints these characteristics impose on tool calling and plugin workflows
The batch-based and real-time nature of category data requires tool calls to be bound to the context of the corresponding production batch, to avoid mixing data across batches. The multi-source and heterogeneous document structure requires plugins to support parsing and calling both structured tables (such as LIMS test data) and unstructured text (such as batch records). Cleanroom data updated per monitoring plans requires periodic verification triggers, so tool calls must support scheduled pulling or event-triggered configuration. Dedicated fields and units require quotation documents output by plugins to automatically match corresponding formats, to prevent errors in parameter units.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to automatic calling requirements for multiple document types and parameters |
| Single search limit | Top 8 entries | Cover key information from core documents for a single batch |
| Maximum context size | 8000–12000 token | Accommodate complete batch records and test data for a single batch |
| Workflow/API function-call configuration | Enabled | Force calling compliant tools to parse structured data |
| Timeout threshold | 120 seconds | Adapt to time requirements for large PDF parsing and multi-source data pulling |
Three common mistakes
- Garbled characters appear when uploading bulk CSV-format production records. This occurs because the correct encoding format is not specified during tool calling, causing a mismatch between file encoding exported from cross-system tools and the parsing plugin.
- No log records or return results appear after calling a tool. This occurs because the
functionCallparameter is not configured correctly, causing tool calls to not be triggered by the system, or the timeout threshold is set too short, leading to early termination of the request. - A "Cannot read properties of undefined" error occurs when calling a locally deployed pdf-marker plugin. This occurs because the FastGPT version does not match the plugin version, and version adaptation configuration has not been completed.
How to confirm configuration is complete
- Upload a single batch CSV file, check if parsed data fields include dedicated fields such as batch number and test results, to confirm the encoding format is correct.
- Enable tool calling logs, execute a bulk document parsing request, and confirm that corresponding tool calling records and return results are generated in the logs.
- Call a very large PDF document for parsing, wait for the parsing result to return within 120 seconds, to confirm the timeout threshold configuration is effective.
- Check if the system automatically selects the corresponding plugin to process structured and unstructured documents when the
toolChoiceparameter is set toauto.
The values provided are common starting points and should be validated against relevant operational samples.