This page is compatible with the workflow orchestration features of FastGPT v4.17.0.
What Data for This Category Looks Like
Data for this category typically comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratories, and energy and environmental monitoring equipment. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data updates are completed in real time per batch of raw materials, each furnace, or each cell operation. Fields include raw material composition, equipment operating parameters, test results, energy consumption and emission indicators. Units cover mass values, voltage and current values, volume values, and more.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Data sources for this category are scattered and update rhythms vary. Workflow orchestration must integrate multiple types of data source nodes, and adapt to real-time and batch update trigger rules. Daily document fields include professional process parameters, so workflows must configure field filtering and standardization nodes to retain only content related to process specifications and technical standards. Since abnormal process parameters directly affect metal recovery and product quality, workflows must embed abnormal verification nodes to intercept or alert out-of-range parameters. For core needs of impurity control and metal recovery, workflows must add targeted calculation and association nodes to fit the exclusive business logic of this category.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Question Classification Model | Fine-tuned large language model adapted to smelting terminology | Improve recognition accuracy of process-related questions |
variables | Bind raw material batch, cell number, furnace number parameters | Associate corresponding batch process documents and real-time data |
| Single Search Limit | Top 8 results | Cover the retrieval scope of core process documents for this category |
| Result Reranking | Sort by professional relevance | Prioritize frequently referenced content such as electrolytic cell condition records and smelting records |
| Timeout Threshold | 600 seconds | Adapt to time requirements for retrieving multi-source industrial data |
nodeId | Name by function (e.g., "Impurity Verification") | Facilitate workflow debugging and abnormal node location |
| Maximum Context Length | 8000 token | Adapt to context carrying requirements of professional process documents |
Three Common Errors
- Phenomenon: Workflow calls an external MCP plugin and returns an interface error, with status code 404. Cause: Incorrect configuration of MCP plugin node connection parameters, and failure to match the plugin's interface call format.
- Phenomenon: Process question classification is incorrect, identifying electrolytic cell maintenance issues as batching adjustment issues. Cause: Failure to use a model adapted to smelting professional terminology; general models cannot recognize exclusive industry terms such as cell condition and anode plate.
- Phenomenon: Response content includes input and output logs from knowledge base searches, instead of only displaying the final answer. Cause: Failure to configure a node to hide tool call logs in the workflow, or failure to turn off the tool call output display switch.
How to Verify Proper Configuration
- Trigger the workflow to run a standard process Q&A, and check that the returned result only includes the answer content, with no additional tool call logs.
- Input different types of process questions, and check that the question classification results match the actual business scenarios.
- Call the external MCP plugin, and check that the interface returned data is consistent with the preset smelting business logic.
- Adjust the parameters bound by
variables, and check that the Q&A result associates the corresponding batch process data.
The values provided are common starting points and should be measured against the reader's own samples.