What data for this category looks like
Data sources include wood pulp and waste paper batch ledgers, paper machine DCS/QCS systems, online measurement sensors, laboratory test reports, and energy and water treatment system logs. Data update rhythms cover real-time (online quality parameters), per shift (stock preparation and beating records), and per roll (sheet break and grade change records). Daily documents include stock preparation sheets, beating operation records, paper machine operation logs, online quality curves, and laboratory test reports. Fields include pulp component values, grammage, moisture, thickness, sheet break duration, and other metrics. There is no fixed, uniform length for individual documents.
What constraints do these characteristics impose on the "tool calling and plugins" link
Data sources are scattered and update rhythms vary significantly. Tool calling must adapt to multi-source data pull timing. For example, poll online quality parameters at short intervals, and trigger batch production records by shift or roll. Pulp and paper documents include time-series operation records and unstructured quality curves. Tool calling must support structured field extraction and curve data parsing, and cannot rely solely on general text processing logic. Single-roll production record data volume fluctuates greatly. The context carrying capacity of tool calling must adapt to dynamic input scale to avoid exceptions triggered by data overload. Supplier batch audit requires cross-data source association and traceability, so tools must support multi-parameter associated queries.
How to set up the configuration
The configuration on this page is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to parsing requirements for multiple types of production data, no need to manually specify function calls |
| Workflow/API function-call configuration | Enable only during specified structured parsing | Adapt to accurate extraction of fields such as pulp components and grammage |
| Polling interval | 10–60 seconds | Adapt to the real-time update rhythm of online quality parameters, avoid frequent calls triggering rate limits |
| Context length | 8000–16000 characters | Adapt to scale fluctuations of single-roll production records and multi-source associated data |
| Associated query scope | Previous 3 batches | Match the traceability requirements of supplier audits, avoid excessive query resource consumption |
| Number of returned results | Previous 10 entries | Adapt to core parameter display for quality audits, avoid redundant data interfering with judgment |
| Trigger timing | Trigger by roll or shift | Match the update rhythm of production records, reduce invalid calls |
Three common errors
- A
400 InternalError.Algo.InvalidParametererror is returned when calling MCP services. The cause is failure to adapt to the field range of pulp and paper data, for example, the passed pulp component parameters exceed the reasonable interval preset by the system. - Tool call concurrency is not limited, leading to failed pulls of paper machine online data during peak hours. The cause is failure to adjust concurrency configuration based on traffic fluctuations during production hours.
- Array format results returned by external search are directly passed to tools without structured conversion. The cause is failure to configure dedicated format processing tools to adapt to the requirement of converting arrays to text.
How to confirm the configuration is properly set
- Trigger a tool call for supplier batch audit, check if the returned results include specified core fields such as pulp components and grammage, and that the number of fields matches the preset associated query scope.
- Observe tool call logs to confirm that the polling interval falls within the configured range, with no frequent calls or timeout records.
- Simulate input of the maximum single-roll production data scale, verify that the context carrying capacity of tool calls does not trigger exceptions.
- Test cross-data source associated queries, confirm that the returned results include association information between supplier batches and corresponding production records.
The values provided are common starting points and should be measured against the reader's own samples.