What the Data for This Category Looks Like
Data sources include grain reserve inventory detailed ledgers, rotation business approval documents, warehouse operation records, and policy-based grain purchase and subsidy allocation vouchers. Document structures mostly combine structured tables and paragraph descriptions. Fields include grain type, storage bin number, quantity (unit: ton), storage date, quality inspection grade, subsidy calculation base, and other relevant items. Update frequency varies with business nodes: daily operational data is updated daily, quarterly rotation plans are finalized 15 working days in advance, and annual reserve reports are completed by the end of January of the following year.
Constraints for Tool Calling and Plugins
The mixed document structure of structured ledgers and approval documents requires precise matching of exclusive fields such as grain type and storage bin number during tool calling, to avoid data misalignment caused by generalized extraction. The requirement for field validation for specific units such as ton adds constraints to parameter extraction. In document drafting scenarios involving multi-source data linkage, plugins must support simultaneous calls to inventory databases and subsidy system interfaces, which increases configuration complexity. The requirement for fixed-format reserve documents requires limiting the structure of tool output to align with official templates, to avoid format deviations.
Configuration Setup
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | Set to auto, and specify that only grain reserve-specific tools may be called | Adapt to scenarios where multiple business tools are required for document drafting, and avoid interference from unrelated tools |
| Database connection timeout setting | 300 seconds | Match the batch query time of grain reserve ledger data, and avoid timeouts caused by large data volumes |
| Custom field mapping rules | Map by grain type, storage bin number, quantity (ton) | Match the exclusive field structure of reserve data, and ensure accurate data extraction |
functionCall | Enable mandatory parameter validation | Avoid unit errors or missing fields in generated documents |
| Number of tool returns per single call | Top 15 entries | Control the amount of returned data, and avoid exceeding context window limits |
| Data time range limit | Last 30 days | Adapt to the latest data requirements for daily document drafting, and avoid calling expired business data |
Three Common Misconfigurations
- Configuring a MySQL database connection plugin results in no return when executing inventory data queries, with logs showing field matching failures. The mapping rules are not configured for grain reserve-exclusive fields such as storage bin number and grain type, causing the plugin to fail to recognize the target data table.
- Configuring a custom URL parsing plugin results in no data return after uploading a reserve warehouse public report link. The text extraction switch for URL parsing is not enabled, or the access permission whitelist for the corresponding website is not configured.
- Calling
functionCallto generate a rotation application report results in output that does not include the quantity unit. Mandatory parameter validation forfunctionCallis not enabled, causing the generated content to omit the required unit field.
How to Verify Successful Configuration
- Manually trigger an inventory data query tool, and check whether the returned result fields include exclusive fields such as grain type, storage bin number, and quantity.
- Submit a simulated rotation application request, and check whether the document format generated by the tool call aligns with the official reserve document template.
- Initiate two separate document drafting requests simultaneously, and verify that the plugin can process requests normally in parallel.
- Configure the public report URL of the target reserve warehouse and trigger parsing, and check whether the returned text includes the core data of the report.
The values provided here are common starting points, and should be measured against the reader's own samples.
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.