What this category's data looks like
Data sources for this category include concentrate and auxiliary material analysis results, DCS systems, cell voltage and current monitoring equipment, laboratory test data, and energy and environmental monitoring devices. Update cycles cover per-batch raw materials, single operations per furnace or cell, and real-time operating parameters. Daily documents include batching lists, smelting records, electrolytic cell status records, test reports, anode and cathode quality records, and environmental protection ledgers. Each document contains dedicated business fields and physical units, such as batching lists with parameters like feeding amount, furnace temperature range, and operating duration.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Data sources for this category are scattered, with large differences in update cycles. Multi-turn dialogue must divide context modules by data type to avoid confusion between real-time data and historical records from different sources. Multiple document types have obvious structural differences, so prompt engineering must clearly specify priority document categories for retrieval, ensuring core inspection data is matched first during audits. Frequently updated single furnace or cell data requires dialogue contexts to be associated with unique identifiers of corresponding batches or furnace numbers, preventing cross-batch data misuse. The exclusive attributes of fields and units require prompt engineering to clearly require output results to match the units of corresponding business fields, avoiding mismatches between numerical values and scenarios.
How to set up the configuration
All configurations below are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Context Window | First 12 dialogue turns + latest 3 sets of real-time cell data | Matches the requirement of this category's multi-turn audit to associate historical operations with real-time operating conditions |
messages parameter | Group and concatenate by furnace or cell number, no more than 800–1200 characters per single request | Avoids overly long contexts causing the model to confuse inspection data from different furnace runs |
| Variable Binding | Bind dedicated business fields from batching lists and electrolytic cell status records | Ensures structured parameters of corresponding documents can be directly called during dialogue |
| Streaming Output Toggle | Enabled, return in segments after a 600-millisecond delay | Adapts to the gradual audit requirements of real-time monitoring data |
| Context Refresh Frequency | Sync latest cell voltage and current data every 300 seconds | Balances real-time performance and interface load, matching the business rhythm of real-time updates for this category |
| Dialogue Log Tagging | Classify and tag by inspection link, such as batching, smelting, electrolysis | Facilitates subsequent review and audit retrospectives |
Three common errors
- Phenomenon: After enabling streaming output via API, returned content is segmented chaotically and includes inspection data from unrelated furnace runs. Cause: Did not group and concatenate the
messagesparameter by furnace or cell number, leading to mixed contexts from different furnace runs. - Phenomenon: AI dialogue output in the workflow is directly displayed on the page, and cannot be processed by subsequent components before output. Cause: Did not disable the default streaming output toggle, and did not configure output interception rules.
- Phenomenon: The dialogue log tagging function can only perform global tagging, and cannot classify by inspection link. Cause: Did not bind classification fields of corresponding documents, and did not specify tagging rules in prompt engineering.
How to confirm the configuration is correct
- Launch a multi-turn dialogue for single furnace inspection, verify returned results only associate batching, smelting, and electrolysis records for the corresponding furnace or cell number.
- Call the API to test streaming output, confirm returned content is segmented by inspection link, with no cross-furnace run data mixed in.
- Enter the workflow configuration page, confirm the output toggle of the AI dialogue module has been disabled, and trigger rules for the text splicing component have been configured.
- View the dialogue logs, confirm each record can be classified and tagged by inspection link, and that the unique identifier of the corresponding business document is associated.
The values provided here are common starting points and should be measured against the reader's own samples.