What Data for This Category Looks Like
Data for this category comes from registered process documents, raw material batch ledgers, DCS control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Documents include batch production records, material balance ledgers, reaction and purification step records, cleaning validation records, impurity profile reports, and deviation and change documents. Data is updated per production batch and process step. Fields include batch number, reaction temperature, feeding amount, impurity content, solvent residue amount, and other relevant items. There is no fixed uniform length for individual data entries.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Equipment inspection and maintenance Q&A for this category must link to multi-source heterogeneous data. Tool calling must support cross-system data pulling. Data updated per batch and process step requires tool calling to bind precise batch filtering conditions. This prevents mixing data across batches. Structured field differences across multiple document types require plugin configurations to adapt to field mapping rules for different data sources. Real-time data requirements in equipment inspection scenarios require limiting tool calling response durations. This ensures timeliness of maintenance decisions. Calls for compliance-related deviation and change documents must strictly limit tool access permission scopes. This prevents sensitive data leaks.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-scenario calling needs for equipment inspection and maintenance, automatically triggers corresponding tools to pull data |
| Workflow/API function-call configuration | Enabled | Supports structured field mapping, adapts to field format requirements of multi-source heterogeneous documents for this category |
| Tool call timeout duration | 600 seconds | Cross-system pulling of registered process, LIMS and other data requires sufficient response time to avoid mid-process failures |
| Plugin data source filtering rules | Bind by batch number | Data is updated per production batch, accurately matches equipment and process data for the corresponding batch |
| Tool return result limit | Top 3 entries | Filters redundant data, focuses on core parameters required for equipment inspection and maintenance |
| FastGPT Version | FastGPT v4.17.0 | Adapts to the plugin calling logic of this version, ensures stable functionality operation |
Three Common Misconfigurations
- Symptom: When calling tools via API, the conversation log displays the tool title but does not trigger actual data pulling. Cause: The
toolChoiceparameter is not set toauto. Only front-end manual tool calling is supported, and no correct trigger configuration is passed during API calls. - Symptom: Frequent timeout errors occur when calling cross-system data tools. Cause: An insufficiently long timeout duration is not configured, making it impossible to complete pulling of multi-source data such as registered process documents and LIMS data.
- Symptom: Tool return result fields do not match business documents. Cause: No plugin data source field mapping rules are configured, and the field format differences across multiple document types for this category are not adapted.
How to Verify Proper Configuration
- Initiate an API call. Check whether the conversation log correctly displays tool calling trigger records. Adjust the configurations of
toolChoiceandfunctionCallbased on the calling scenario. - Simulate an equipment inspection scenario to initiate a query. Verify whether the tool return results are bound to specified batch number data.
- Test the tool calling response duration. Confirm that it does not exceed the preset timeout threshold.
- Review the plugin configuration's field mapping rules. Confirm that they match the field formats of business documents.
The values provided are common starting points and should be measured against the reader's own samples.