What this category of data looks like
The data for this category comes from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS, and warehouse systems of fine chemical enterprises including coatings, resins, additives, and agrochemical intermediates. Daily documents include batch production instructions, feeding review slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Fields cover raw material batch numbers, feeding amount (kilograms), reaction temperature (℃), reaction duration (hours), and other relevant items. Data is updated for each batch production task and each key reaction step. Single-batch data is associated with complete information for all nodes in the full production process.
What constraints this category's characteristics impose on tool calling and plugins
The multi-source heterogeneous data feature, updated per batch and per step, imposes three constraints on tool calling and plugins. First, tool calling must be triggered based on the production batch dimension to avoid cross-batch data confusion. Second, it must adapt to parsing requirements for multiple document types. Plugins must separately support format extraction for structured feeding slips, time-series reaction records, and cleaning records. Third, due to formula confidentiality and production compliance requirements, tool calling must bind permission verification logic. Only authorized roles are allowed to call sensitive data interfaces. In addition, the multi-step production process requires plugins to call data from corresponding systems in sequence according to reaction order. Binding batch number parameters via functionCall ensures logical consistency.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Force trigger via functionCall | Ensure tool calling follows the preset quality traceability process, prevent the large language model from selecting irrelevant tools automatically |
| Single tool call timeout | 600 seconds | Single-batch production data requires pulling information from multiple systems, which takes a long time. This setting prevents mid-process interruptions |
| Number of tool return results | Top 3 entries | Key node data associated with a single batch production is limited. Excessive results will increase analysis redundancy |
| Workflow/API function-call configuration | Bind batch number parameter | Ensure each tool call only pulls data related to the current traceability batch, preventing cross-batch confusion |
| Tool call context window | 8000–12000 token | Calibrated through actual testing, adapts to the content length after multiple documents are spliced |
This configuration applies to FastGPT v4.17.0.
Three common errors
- Phenomenon: The output contains the phrase "Reference Marker: [1]". Cause: No result cleaning step is configured for tool calling, and index markers from original documents are not filtered out.
- Phenomenon: After a user inputs a specified keyword, the system does not trigger a custom interface reply and directly returns a default response. Cause: No keyword trigger rule is added in the plugin configuration, or the rule is not bound to the corresponding tool call node.
- Phenomenon: A 400 status code is returned when calling a custom interface, prompting missing parameters. Cause: Required parameters such as batch number and raw material batch are not passed during tool calling, which does not meet the input parameter requirements of the custom interface.
How to confirm the configuration is correct
- Initiate a single-batch quality traceability test, check whether the output contains system data from unrelated batches.
- Trigger tool calling, confirm that the number of returned results matches the preset configuration.
- Input test content containing sensitive formula keywords, check whether permission interception or custom interface reply is triggered.
- View tool call logs, confirm that each call carries the current batch number parameter, and no cross-batch data is pulled.
The values provided are common starting points and should be measured against the reader's own samples.