Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data sources for this category include the food safety sampling and monitoring system, the national market supervision administration sampling results query system, the laboratory information management system (LIMS) of testing institutions, and the ledgers of food producers and operators. The data update frequency is monthly. Daily documents include sampling plans, sampling forms, inspection reports, unqualified food verification and disposal reports, and risk control notices. Data fields cover sampling identification, inspection items, unqualified judgment results, disposal process nodes, and other items. Units are adjusted according to the detection item and document type.
What constraints these characteristics impose on tool calling and plugins
Data sources for this category are scattered, involving multiple platforms such as supervision systems and testing institution LIMS. Tool calling must adapt to the authentication and format requirements of different interfaces, and multi-source data aggregation plugins need to be configured. The monthly batch-updated sampling data volume fluctuates greatly, so reasonable batch processing thresholds must be set for tool calling to avoid overloading single requests. The structure of daily documents varies significantly; the field rules for sampling forms, inspection reports, and disposal reports are different, so dedicated tool calling parameters need to be configured for different document types. The unqualified disposal link requires linkage with field verification across multiple documents, so tool calling must support cross-document data association.
How to set the configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
toolChoice | auto | Adapt to tool calling requirements for different business scenarios such as sampling plans and inspection reports, automatically match corresponding rules |
| Question Optimization | Enabled, matched to food safety sampling business scenarios | Optimize queries for sampling professional terminology to improve the accuracy of tool calling |
| Maximum Context | 8000–12000 token | Cover the context length of monthly batch sampling data, avoid truncation of key business information |
| Single Tool Call Limit | First 8 entries | Match the sample quantity range of a single batch of sampling, avoid calling excessive redundant data |
functionCall | Triggered by document type | Ensure that tool calling for different documents follows corresponding business rules, avoid confusion of cross-type data |
Three common mistakes
- Symptom: Returns
messages is emptyerror when calling OpenAPI. Cause: Did not populate document fields related to sampling business in the tool calling request, resulting in missing valid context information in the request body. - Symptom: Model stream response is empty after tool calling. Cause: Did not configure the
toolChoiceparameter adapted to the food safety sampling scenario, so the model cannot automatically trigger tool calling logic matching the business. - Symptom: Inspection report data obtained via tool calling does not match enterprise ledger data. Cause: Did not configure verification rules for multi-source data aggregation, resulting in unaligned data pulled from the LIMS system and enterprise ledger data.
How to confirm the configuration is complete
- Initiate a simulated call, pass in the sample fields of a sampling form, check whether the tool can correctly pull the corresponding inspection report data.
- View system logs to confirm that the tool calling request contains valid context fields related to sampling business, with no null value errors.
- Adjust the
toolChoiceparameter toauto, verify whether the model can automatically trigger corresponding tool calling rules based on different document types. - After configuring the multi-source data aggregation plugin, check whether the field matching logic of the pulled sampling system data and LIMS system data meets business requirements.
The values provided 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.