What Data for This Category Looks Like
Refined oil sales document data primarily originates from gas station retail ledgers, oil product inventory systems, value-added tax invoicing systems, and oil quality inspection reports. Retail ledgers are updated daily, recording the oil product number, specification, sales quantity (unit: liters or tons), unit price, and transaction amount for each individual transaction. The inventory system synchronously updates inventory remaining quantities and purchase batches. The invoicing system stores buyer information, invoiced amounts, and oil product details for all issued invoices. All data uses a structured format, with fixed fields and no free text redundancy.
What Constraints These Characteristics Impose on Tool Calling and Plugins
Refined oil sales data has strict real-time requirements, and retail ledgers are updated daily. As such, tool calling must support high-frequency queries, and the timeout period must be kept within a reasonable range. Data fields include specific business units (liters or tons), so parameter validation for tool calls must align with this specification. Failure to do so will cause the data source interface to reject responses. Data comes from multiple independent systems, so plugins must be configured with multi-data-source docking logic and unify the field formats across different systems. Document drafting often requires batch calls of multiple transaction data sets, so the tool calling context window must be large enough to accommodate batch content and prevent information truncation.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to dynamic tool calling needs for multi-step document drafting, supporting on-demand calls to multiple data sources such as retail ledgers and inventory systems |
functionCall | enabled | Supports generating structured calling requests that conform to refined oil sales data formats, ensuring all required fields are fully transmitted |
| Plugin timeout period | 30 seconds | Most refined oil sales data queries target real-time ledgers; overly long timeouts will disrupt the smoothness of document generation |
| Parameter validation rules | Match oil product numbers and sales units (liters/ tons) | Prevents passing fields and units that do not comply with business specifications, reducing interface call failures |
| Context window upper limit | 8000–12000 token | Must accommodate batch data including multiple retail transactions and invoicing records to avoid content truncation |
| Tool call retry count | 2 times | Addresses call failures caused by temporary interface fluctuations of individual systems, improving overall call success rates |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Tool calls return empty results, and logs show a 400 error from the interface. Cause: Parameter validation rules are not configured, and units that do not meet business specifications are passed (such as using "L" instead of "liters"), causing the data source interface to reject responses.
- The first tool call works normally, but subsequent calls involving multiple transactions trigger errors. Cause:
toolChoiceis not set toauto, and only a single tool is called fixedly, which cannot adapt to the multi-step query requirements of batch data. - The generated document omits the oil product specification field. Cause:
functionCallis not enabled, so the AI cannot generate structured tool call parameters and misses required business fields.
How to Verify the Configuration Is Correct
- Upload one refined oil retail ledger document containing multiple transactions, trigger tool calling, and check whether the generated call request includes required fields such as oil product number, sales quantity, and unit price.
- Simulate three consecutive different document drafting requests, and confirm that tool calls complete responses within
30 secondswithout timeout errors. - Intentionally pass parameters that do not comply with unit specifications (such as "500 kilograms"), and check whether the system triggers parameter validation errors.
- Review the tool configuration logs in the FastGPT backend to confirm that
functionCallis enabled andtoolChoiceis set to automatic mode.
The configuration values provided here are common starting points, and should be evaluated 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.