What data for this category looks like
Data for this category comes from ERP material master data, melting furnace thermometers, spectrometers, MES furnace heat records, mold management systems, and customer technical drawings. Daily documents include foundry process cards, alloy batching lists, furnace heat ledgers, mold histories, first article inspection records, material test reports, and nonconforming product review forms. Data updates are performed per furnace heat, batch, or daily. Each single record includes fields such as alloy composition percentage, pouring temperature, mold cycle parameters, and defect type. Units include degrees Celsius, kilograms, percentages, and others.
What constraints these characteristics impose on tool calling and plugins
Foundry scenario data has high update frequency and scattered sources, requiring tool calling to support real-time pulling of business data from multiple systems. There are many specialized fields and industry-specific parameters, so plugins must parse specialized terms like alloy composition and pouring temperature. A single furnace heat audit involves cross-checking multiple documents, so tool calling must be able to associate identification fields like furnace heat ID and mold cycle to achieve precise data matching. Additionally, data abnormalities can directly lead to entire furnace heat scrap, so tool calling needs basic abnormal retry mechanisms to avoid interrupting the audit process due to a single interface failure.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to the need for parallel multi-tool calls in foundry audit scenarios, automatically matching three types of tools: alloy batching verification, pouring parameter interpretation, and defect traceability |
| Single tool call limit | 3–5 times | A single furnace heat audit only needs to cover three core links: alloy batching, pouring parameters, and defect analysis, avoiding redundant calls |
| Tool call timeout | 600 seconds | There is communication delay when pulling data from equipment such as melting furnaces and spectrometers, reserve sufficient response time |
| Custom plugin trigger condition | Match by furnace heat ID | Foundry business data is organized by furnace heat, pull the batching list and furnace heat records of the corresponding batch accurately through the furnace heat ID |
| Return field filter | Retain alloy composition percentage, pouring temperature, defect type | Focus on the core business fields concerned by the audit, reduce unnecessary data transmission |
This configuration applies to FastGPT v4.17.0.
Three Common Mistakes
- Phenomenon: After calling the
pushdata APIto upload foundry furnace heat records, the interface always displays "Indexing" status. Cause: The index rule for associating documents by furnace heat ID is not configured, causing the system to fail to match valid business data. - Phenomenon: When calling the knowledge base query via API, the returned results do not include the referenced document file names. Cause: The metadata extraction switch is not enabled in the tool call configuration, and the identification fields such as document source and batch are not specified for extraction.
- Phenomenon: An error is returned when calling the knowledge base query interface using an application key. Cause: The tool call permission rules are not configured, and the application key is not granted permission to access the corresponding knowledge base and tools.
How to Confirm Correct Configuration
- Upload a test furnace heat record, check whether the index status is updated within a reasonable time.
- Call the tool to query the alloy batching data of a certain furnace heat, confirm that the returned results include the preset core business fields.
- Trigger the custom plugin to call the melting furnace data interface, check whether the returned data includes foundry-specific parameters such as temperature and composition.
- View the tool call log, confirm that the number of single calls does not exceed the preset limit, and there are no timeout error records.
The values provided are common starting points and should be measured against the reader's own samples.