Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Grain reserve data sources include structured ledgers exported from grain reserve inventory management systems, regularly updated rotation execution sheets, structured documents and scanned copies of inbound and outbound shipments, and daily inspection records at storage sites. Update cadence: inventory ledgers update daily, rotation plan sheets update quarterly, and inspection records are generated daily.
Documents fall into two categories: structured field tables containing grain variety, storage site number, current inventory volume, inbound batch number, qualified inspection value, and storage period; unstructured documents include inspection reports and inspection logs, in PDF or structured table formats. Unit specifications: inventory volume is measured in tons, storage period in days, and inspection indicator values in milligrams per kilogram.
What constraints these characteristics impose on tool calling and plugins
The large number of structured fields in grain reserve data, and the characteristic of linking inventory and batches, requires precise matching of preset field names during tool calling to avoid cross-field data confusion. Unstructured inspection reports and inspection logs require text extraction plugins, which increases dependencies in the tool chain.
Daily updated inventory ledgers require tool calling trigger frequencies to align with the update cadence, to avoid pulling outdated data. Quarterly updated rotation plans require regular sync plugin configuration to avoid redundant data from continuous real-time pulling. Uniform numbering rules across different storage sites require preset format verification during tool calling, to reduce input error probability.
How to Set Up the Configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
toolChoice | Specify custom tools | Tool calling for grain reserve management must bind dedicated tools such as inventory query and batch verification, to avoid calling irrelevant tools |
| Batch execution tool | Trigger by batch, maximum 10 batches per single run | Grain reserve inbound and outbound operations and rotations are executed in batches, so batch tools adapt to batch processing logic |
| Variable update tool | Accumulate by classification tags, step size 1 | Need to count call times for different grain varieties and storage sites, classification tags match data fields |
functionCall | Force call specified tools | Core business processes must strictly call inventory verification tools, to avoid the model from selecting tools independently |
| Tool call timeout setting | 300–600 seconds | OCR processing of unstructured documents and cross-site data pulling require longer time |
| Format verification configuration | Match storage site numbering rules (format: K + 4 digits) | Storage site numbers follow uniform formats, so input validity must be verified before tool calling |
Three Common Configuration Mistakes
- The symptom is that the classification statistics field shows no value changes after the variable update tool runs. The cause is that the trigger condition for variable updates is not bound to the tool execution completion node, causing variables to not update synchronously with the business process.
- The symptom is that the number of results returned by the batch execution tool does not match the preset batch quantity. The cause is that the correct batch filter field is not specified in the batch configuration, causing cross-batch data to be mixed in.
- The symptom is that custom RAG queries return unrelated grain reserve data. The cause is that the entity relationships of the GraphRAG tool are not bound to the dedicated fields of grain variety and storage site, causing entity matching to fail.
How to Verify Correct Configuration
- Trigger one inventory query tool call, verify that the returned fields fully match the inventory ledger.
- Execute one batch execution tool, verify that the number of returned results matches the preset batch quantity.
- Trigger a variable update operation, check that the classification statistics values in the variable panel increase synchronously with the number of calls.
- Call the GraphRAG tool, verify that the returned results include the association between grain variety and storage site.
The 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.