What the Data for This Category Looks Like
Business data for this category comes from channels including concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratory testing, energy and environmental monitoring, and others. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, environmental protection ledgers, and more. Fields cover raw material composition, equipment operating parameters, test results, ledger information, and similar categories. Units include mass, voltage, current, time, and other types. Data update frequencies vary by raw material batch, each furnace or cell, or in real time, with no fixed unified cycle.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Multi-source and real-time updated data requires multi-turn dialogue to retain context for the corresponding batch, to avoid mixing data across batches. Complex document structures require prompt engineering to clearly specify the range of fields to extract, ensuring generated process documents or exception reports contain complete business information. The batch-based update feature requires the dialogue flow to bind exclusive parameters for the current batch, preventing cross-interference of data from different batches. Time-consuming component balance and cell condition control links require multi-turn dialogue to gradually guide users to supplement necessary parameters, avoiding overloading the context limit by requiring too much input at once.
How to Configure Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind the current batch's concentrate composition and cell condition data | Match the batch-based business characteristics of this category, ensuring dialogue calls real-time parameters for the corresponding batch |
messages | Retain the last 30 dialogue history entries | Adapt to gradual adjustment needs when drafting process documents across multiple turns, avoiding context overload |
| Maximum context length | 800–1200 characters | Match the core field length of single-batch process documents, preventing timeouts caused by loading excessive historical data |
| Single-turn dialogue return limit | Top 5 entries | Limit the number of records extracted for exception reports, avoiding interference from redundant data during drafting |
chatId | Bind the current application session ID | Ensure multi-turn dialogue context only takes effect within the current session, avoiding mixing data across batches |
| Question optimization switch | Enabled | For complex component balance and cell condition control issues, optimize user input wording to align with business logic |
Three Common Mistakes
- Issue: After uploading a JSON file containing data from multiple batches, the number of dialogue return results does not meet expectations. Cause: No reasonable single-turn dialogue return limit is set, causing excessive data loading to exceed the context limit.
- Issue: The locally deployed model can be called normally in the debug preview interface, but the model is automatically replaced with another type in the formal application. Cause: No exclusive configuration for model calls is bound via
variables, causing the session context to be overwritten by default parameters. - Issue: After connecting to an external dialogue interface, only the first dialogue node can be interacted with, and multi-turn context cannot be continued. Cause:
chatIdis not correctly bound to the session identifier, causing the context to reset with each interaction.
How to Confirm the Configuration Is Complete
- Trigger a multi-turn dialogue flow, check whether the number of retained dialogue history entries matches the preset range.
- Upload business data for the corresponding batch, verify whether the bound
variablescorrectly associate with the parameters of the current batch. - After calling the local model, check the session log to confirm that the model type has not been automatically replaced.
- Input test data containing specified fields, confirm that the generated document includes all required business fields.
The values provided here are common starting points, and should be evaluated against the user's own samples.