What the data for this category looks like
Daily documents for this category include material batching lists, smelting logs, electrolyzer condition logs, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis results, DCS system data, cell voltage and current monitoring data, laboratory test data, and energy and environmental protection monitoring data. Business nodes dictate data update frequency. Each batch of raw materials, each furnace, or each cell triggers an update upon completion of production. Some cell condition, energy consumption, and environmental protection data update in real time. Document fields include material component proportions, furnace temperature parameters, cell voltage values, laboratory indicators, environmental protection emission values, and more. Some fields use dedicated units: current is measured in kiloamps, and temperature in degrees Celsius.
What constraints these characteristics impose on the tool calling and plugins link
This category uses multi-source data with highly varied update rhythms. Real-time monitoring data requires immediate tool calls. Batch-generated documents trigger calls after output completion. Tool calling must adapt to different data source access formats and trigger timing. Document fields include a large number of professional parameters and dedicated units. Tool calling must accurately match field names and unit rules to avoid data parsing errors. Cross-review compares associated data across multiple document types. Examples include matching component values between batching lists and laboratory reports, and linking statuses between electrolyzer condition logs and anode quality records. Tool calling must support parallel calls across multiple documents and result integration. It must also handle cross-document field association logic.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto or a specified list of text extraction, data comparison tools | Adapts to scenarios with parallel tool calls, meets tool requirements for multi-document cross-review. FastGPT v4.17.0 supports this parameter configuration |
| Workflow/API function-call configuration | Enable and configure field validation rules | Ensures the JSON format passed during tool calling complies with preset rules, avoids parsing errors |
| Tool call timeout period | 300–600 seconds | Some real-time monitoring data calls require waiting for DCS system response, reserve sufficient response duration |
| Maximum parallel tool calls | First 3–5 tools | The number of document types to compare in a single cross-review is limited, avoids overloading system resources |
| Context binding rule | Bind corresponding document data by business batch | Ensures review for each batch of raw materials is linked to all document data for that batch, maintains context consistency |
Three Common Errors
- Symptom: The text extraction tool returns the
400 Invalid JSON payload received. Unknown nameerror. Cause: The JSON fields passed during tool calling include non-preset dedicated fields for smelting documents, leading to format validation failure. - Symptom: Calling the workflow API does not return expected context data. Cause: The workflow context is not bound to document data for the corresponding business batch. Only a single API call is made without linking historical business data.
- Symptom: The
modelparameter passed when calling the API cannot clearly correspond to the task type. Cause: The task binding rule for tool calling is not clearly configured, confusing model calling logic between problem classification and AI question answering.
How to Confirm Proper Configuration
- Trigger a cross-review for a single batch of documents, check the tool call logs to confirm all preset tools are called normally.
- Export the result data returned by tool calls, verify that field names and units match the preset rules for the target documents.
- Simulate an abnormal request passing non-preset fields, check if the system triggers the corresponding format validation error.
- Bind the business batch context, then call the API twice consecutively. Confirm each call links to historical data for the corresponding batch.
The values provided here are common starting points. They should be evaluated against in-house operational samples.