What Data Looks Like for This Category
Data sources for this category include ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, customer drawings, and technical specifications. Data is updated per heat, per batch, or daily. Daily documents include foundry process cards, batching lists, heat records, mold histories, first-piece inspection records, material test reports, and nonconforming product review forms. Data fields include alloy composition percentage, melting temperature, pouring duration, heat number, mold number, defect type, and other items. Corresponding standard units include degrees Celsius, seconds, percentage, and others.
Constraints Imposed on Tool Calling and Plugins by These Characteristics
Data for this category is updated per heat or batch, and is distributed across multiple systems. Tool calling must use heat number as the sole associated identifier to prevent mixing of cross-batch data. Fields from multi-source data have clear units and fixed formats. Tool calling must precisely match field names and units to avoid parameter errors. Exception reports require tracing defects to their corresponding heat and mold numbers. Tool calling must connect to the mold management system, MES system, and quality record system simultaneously to integrate associated data. Data anomalies can result in full heat scrap or subsequent claims. Tool calling must add a pre-parameter verification step.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to dynamic multi-tool selection, automatically matches required data sources and processing tools based on current tasks |
| Workflow/API function-call configuration | enabled | Enables explicit function call mode, ensures tool calling parameter formats meet requirements of each system’s interface |
| Tool call retry count | 2–3 times | Addresses temporary network fluctuations or system response delays, prevents process interruptions from single failed calls |
| Multimodal tool call switch | Enabled | Supports recognition of foundry defect images, meets multimodal call requirements |
| Context window upper limit | 8000–12000 token | Accommodates all records, drawings, and inspection documents for a single heat, avoids content truncation |
| Timeout threshold | 60 seconds | Adapts to real-time requirements for melting parameter retrieval, prevents process blocking from excessive waiting |
Three Common Misconfigurations
- Segmented streaming generation content cannot be retrieved. The cause is failure to configure the streaming response callback link, and failure to enable related streaming output settings.
- The generated process document does not include the melting time parameter for the current heat. The cause is failure to bind the tool plugin for retrieving real-time parameters, and failure to correctly configure the calling logic for time retrieval.
- Defect types in generated exception reports do not match their corresponding heat numbers. The cause is failure to use heat number as the association key when calling data from multiple systems, resulting in mixing of inspection records from other batches.
How to Verify Successful Configuration
- Initiate a tool call request simulating single-heat data, check if returned parameters such as alloy batching ratio and melting temperature match preset ERP and melting furnace data.
- Upload an example image of a foundry defect, trigger multimodal tool calling, check if typical defects such as shrinkage cavities and gas pores can be identified and associated with corresponding heat information.
- Test the process document generation workflow, check if segmented streaming output content can be returned, and verify that the front-end callback link functions normally.
- Trigger exception report generation, check if multi-system data such as mold history and first-piece inspection records can be automatically associated, and ensure data association is accurate.
The values provided here are common starting points, and should be evaluated against the user’s own samples.