What the Data for This Category Looks Like
Typical entities engaged in continuous basic chemical production include chlor-alkali, synthetic ammonia, methanol, and soda ash enterprises. Daily documents cover operating procedures, process indicator cards, DCS trends, laboratory reports, interlock bypass records, special work permits, and shift reports. Data sources include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring tools, energy metering devices, and raw material tank sensors. Data update frequencies are divided into three categories: real-time, per-shift, and daily. Fields include equipment numbers, process parameters (pressure, temperature, flow, etc., with units of MPa, ℃, m³/h), laboratory results, and work approval information. Ledgers are stored in hierarchical layers based on real-time collection, shift summarization, and daily reports.
Constraints Imposed by These Characteristics on the Tool Calling and Plugins Link
Multi-source heterogeneous data sources require plugins to adapt to different interface formats, to avoid data pull failures caused by protocol incompatibility. Differentiated update frequencies (real-time, per-shift, daily) require configuring matching trigger rules for each data type, to prevent repeated data pulls or missed updates. Document types include structured parameters and unstructured approval files. Tool calling logic must be differentiated to ensure complete ledger fields. Sensitive data calls related to strong interlocks require additional permission verification, to avoid production risks caused by unauthorized access.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Auto | Adapts to multi-type tool calling scenarios, no manual specification of calling method required |
| Timeout Threshold | 600 seconds | Matches real-time data pull and report generation cycles for continuous basic chemical production |
| Parallel Data Pull Count for Multiple Data Sources | 3 | Balances data acquisition efficiency and interface load, adapts to multi-source data while avoiding timeouts |
| Unstructured Document Parsing Mode | Match by document type | Adapts to parsing requirements for documents of different formats, including operating procedures and work permits |
| Workflow/API function-call configuration | Trigger only explicitly | Controls calls for sensitive documents such as interlock bypass records, prevents accidental triggers |
| Retry Count | 2 | Addresses common scenarios of temporary fluctuations in DCS interfaces |
Three Common Misconfigurations
- Symptom: Some nodes fail to execute when associated workflows are called, and logs indicate the code running link is missing. Cause: Correct tool calling parameter transfer is not configured in the main workflow, causing sub-workflows to fail to obtain necessary data source permissions.
- Symptom: A write EPROT error is returned when a third-party system calls the FastGPT knowledge base interface. Cause: HTTPS interface certificate verification or request header parameters are not configured correctly, failing to meet interface security requirements for basic chemical scenarios.
- Symptom: Fields in the tool calling background ledger are empty, preventing generation of complete reports. Cause: Unit conversion for basic chemical data is not adapted, such as directly writing pressure values in MPa into reports without format verification.
How to Confirm Configuration Completion
- A manual tool call is initiated, and pulled data sources are reviewed for inclusion of documents matching this category, including DCS trends and shift reports.
- Tool calling logs are checked to confirm the retry mechanism triggers normally when simulating interface fluctuations.
- Parsed fields from unstructured documents are verified to include necessary basic chemical ledger contents, such as process indicators and work time.
- A scheduled trigger task is configured, and daily/per-shift ledger summary reports are checked for generation as expected.
The values provided here are common starting points, and should be evaluated against the reader's own samples.