What the data for this category looks like
Non-ferrous metal smelting ledger data comes from three sources: on-site industrial control systems, quality control laboratory management systems, and production scheduling ERP systems. Update frequencies fall into three categories: process parameter data is updated every 15 minutes, shift production ledgers are updated daily, and monthly comprehensive summary data is updated monthly. Most documents are in structured table format, with fields including heat number, production duration, input raw material weight, output metal quantity, unit energy consumption, shift staff ID, abnormal alarm count, qualified inspection batch count, and others. Units include kilograms, hours, tons of standard coal per ton of metal, and others. A single batch of ledger records can reach thousands of entries.
What constraints these characteristics impose on the tool calling and plugins workflow
Multiple data sources require that tool calling be configured with rules for pulling multiple data sources, to adapt to the interface formats and permission requirements of different industrial control, quality control, and ERP systems. Differences in update frequencies require setting differentiated calling intervals for different types of ledger data, to avoid repeatedly pulling real-time data or delaying the pulling of monthly summary data. Structured data with multiple associated fields requires clearly defining association keys during tool calling, to prevent misaligned aggregation of ledger data from different heats. The structured table format requires plugins to adapt to column name matching and data type verification logic, to ensure that parsed ledger data can be used for subsequent summary calculations and report generation.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | Auto | Adapts to multi-tool calling scenarios, automatically matches the data source pulling and data aggregation tools required for ledger summary |
functionCall | Enabled | Supports structured data parsing, adapts to the multi-field association requirements of non-ferrous metal smelting ledgers |
| Data source pulling interval | 15 minutes – 24 hours | Covers the update frequencies of real-time process data and monthly summary data |
| Associated field matching rule | Match by heat number | Matches the unique identifier field in ledger data, to avoid misalignment of cross-batch data |
| Single return record count | Top 1000 entries | Limits the data volume returned by a single tool call to avoid system overload |
| Timeout threshold | 600 seconds | Adapts to the time consumption requirements of multi-source data pulling and structured parsing |
Three Common Misconfigurations
- A
You need to use the app key rather than the account keyerror is returned when calling a tool. This occurs because the application-level calling key is not configured, and an account key is mistakenly used to initiate the tool calling request. - The ledger data fields returned by the tool are empty. This occurs because the correct associated field is not specified, causing multi-source data to fail to complete matching and association.
- A single tool call triggers a timeout. This occurs because the number of single return data entries is not limited, and a large number of ledger records exceeding the system processing limit are pulled.
How to Verify Successful Configuration
- Initiate a tool calling request, and check that the response result contains the correct smelting ledger field data.
- View the tool calling logs to confirm that the data pulling interval falls within the preset 15 minutes – 24 hour range.
- Test the key verification scenario, and confirm that a specified verification failure prompt text is returned when a key error occurs.
- Verify the structured data parsing result, and confirm that the data aggregation logic after associated field matching works correctly.
The values provided here are common starting points, and should be evaluated against the samples corresponding to the specific use case.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.