What the data for this category looks like
This category’s data primarily comes from customer color swatches, formula libraries, dye chemical batch records, dye vat control systems, colorimeters, setting machines, and environmental monitoring equipment. Daily generated documents include dyeing formulas, chemical weighing records, dye vat operation curves, proofing confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data updates follow per-vat, per-batch, or per-shift cycles. Individual data entries include fields such as batch numbers, process parameters, and test results. Some raw data collected by equipment includes timestamps and unit identifiers.
What constraints these characteristics impose on tool calling and plugins
The batch-based high-frequency data, multi-type document structure, and cross-device data source characteristics of this category create multiple constraints for tool calling and plugin configuration. Multi-format process and test documents require plugins to support parsing different file types such as CSV-format dye vat curves and PDF-format color difference reports. The per-vat and per-batch update rhythm requires tool calls to pass batch numbers as precise retrieval parameters, to avoid mixing data across batches. Raw data collected by equipment and manually recorded ledgers require linked calls. Plugins must pull associated information across data sources to ensure the integrity of traceability logic.
How to set the configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API function-call configuration | Set to enabled | Supports structured transfer of precise retrieval parameters such as batch numbers and dye vat IDs, adapting to this category’s batch-based data traceability needs |
| Workflow/API tool selection | Set to required | Forces tool calls to pull actual production data, avoiding unsupported quality analysis conclusions |
| Single tool call timeout | Set to 600 seconds | Adapts to the parsing and pulling duration of large-volume data such as dye vat operation curves and sewage ledgers |
| Document formats supported by plugins | Include CSV, PDF, TXT | Covers common document types for this category such as dye vat curves, color difference reports, and chemical weighing records |
| Tool call result recall limit | Set to top 10 entries | Balances retrieval efficiency and data integrity, adapting to multi-dimensional quality test data for a single batch |
Three common misconfigurations
- Symptom: When invoking a content extraction node, the self-built model interface returns an unsupported prompt, and data parsing cannot be completed. Cause: Support for
functionCallandtoolChoicewas not enabled in the tool plugin configuration. Modifying only the config.json file does not take effect. - Symptom: Tool calls only return single dye vat operation data, and do not associate chemical weighing records and color difference reports for the corresponding batch. Cause: Cross-data source plugin linkage logic was not configured. Only data from a single device was called.
- Symptom: An OpenAPI call returns a parameter error, and the tool call process cannot be triggered. Cause: Configuration parameters for
toolChoiceandfunctionCallwere not passed as required. The parameter format does not meet interface specifications.
How to confirm the configuration is complete
- Initiate a test call, pass a known batch number, and check if the tool automatically pulls the corresponding batch’s dye vat operation curves, chemical weighing records, and color difference reports.
- Review the plugin configuration list to confirm parsing support for common category documents including CSV, PDF, and TXT.
- When calling the OpenAPI interface, check if the returned tool call parameters include the correct configuration for
functionCallandtoolChoice. - Simulate a large-volume data upload scenario, and confirm the tool call completes data pulling and parsing within 600 seconds without interruption errors.
The values provided are common starting points and should be measured against the reader's own samples.