What data for this category looks like
Core data for this category comes from formula systems, raw material batch management systems, internal mixers, Mooney viscometers, rheometers, vulcanizers, and laboratory testing equipment. Daily produced documents include formula sheets, weighing records, mixing curves, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance test reports. Data is updated per truckload of raw material batches, per batch of rubber compound mixing, and per vulcanization cycle. Each document includes fields such as raw material batch number, mixing temperature range, vulcanization time parameters, Mooney viscosity value, and finished product tensile strength. Field structures vary across different document types, requiring adaptation to multi-source heterogeneous data structures.
What constraints these characteristics impose on tool calling and plugins
The multi-source heterogeneous document structure requires tool calling plugins to adapt to different data reading and parsing formats, to avoid field extraction failures caused by format mismatches. The high-frequency update characteristic requires tool calling to support trigger frequencies within minutes, and cannot rely on overly long context caching. The multi-link data association requirement requires connecting multiple data sources such as formula systems, production equipment databases, and laboratory test libraries, while supporting batch query and write operations. The rigor of exception reports requires that tool calling results undergo format verification, to ensure that core fields such as feeding sequence and temperature parameters are free of errors, and avoid production accidents caused by data deviations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-type tool calling needs for this category, covers scenarios including formula verification, data extraction, and exception recording, and is compatible with FastGPT v4.17.0 plugin logic |
| Workflow/API function-call configuration | enabled | Supports structured output of core fields for process documents and exception reports, enabling subsequent automatic organization and archiving |
| Database connection timeout | 300 seconds | Mixing data collection cycles typically range from 2 to 5 minutes, so the timeout setting must cover the requirement for batch reading data from multiple devices |
| Allow multi-row queries | Enabled | Requires simultaneous execution of operations such as inserting exception report records and selecting matching historical vulcanization parameters, which aligns with standard business processes |
| Tool calling retry count | 2 times | Occasional network fluctuations in high-frequency production scenarios can be resolved through retries, preventing task interruptions that disrupt production rhythm |
Three common configuration mistakes
- Phenomenon: Tool calling returns empty results or missing fields. Cause: Failed to adapt to the multi-source heterogeneous document format of this category, and failed to correctly parse CSV column names from mixing curves or PDF text structures from vulcanization process cards.
- Phenomenon: MySQL database plugin reports an error when executing multi-row queries. Cause: The allow multi-row queries configuration is not enabled, or the query statement contains unspecified special characters that do not comply with database execution rules.
- Phenomenon: Tool calling results are unstable, with duplicate or incorrect exception report entries. Cause: No reasonable deduplication rules set for the high-frequency update characteristic of each vulcanization cycle, leading to repeated reading of the same batch of vulcanization production data.
How to verify a correct configuration
- Execute a single mixing data extraction test, and verify that returned result fields match the original data collected by the internal mixer.
- Trigger a multi-row query task that includes insert and select operations, and verify that database operations complete as expected.
- Simulate an abnormal vulcanization scenario to trigger tool calling, and verify that the returned result includes all core fields required for an exception report.
- Trigger multiple high-frequency tool calling tasks consecutively, and verify that result stability meets the needs of the target business scenario.
The values provided here are common starting points and should be measured against the reader's own samples.