What the data for this category looks like
Internal office data sources for non-ferrous metal smelting include real-time collected data from production distributed control systems (DCS), equipment maintenance logs, material quality inspection reports, inventory and sales ledgers, employee work orders and training records. Real-time collected data updates every second. Ledger-type data updates daily. Quality inspection reports update with each production batch. Most document structures use structured fields, including equipment number, temperature (℃), current (A), operating duration (hours), work order ID, associated equipment number and other fields. Some unstructured data includes equipment fault descriptions and employee feedback text.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Real-time second-level updated collected data requires that multi-turn dialogue context does not include overly distant historical records, otherwise outdated parameters will be introduced to affect judgment. Multiple fields with professional units require that prompt engineering clearly specifies the format and unit of each field, to avoid returning results that do not comply with business specifications. The business logic of associated equipment and work orders requires that multi-turn dialogue tracks the current session's associated identifier, to ensure each round of questions matches the correct business context. Unstructured fault descriptions and feedback text require prompt engineering to understand professional terms, to accurately convert them into executable query instructions.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the combination of current session employee ID and equipment number | Distinguish different sessions associated with different employees and equipment, ensure historical dialogue is only visible to the current user and equipment |
messages | Retain the most recent 20 turns of dialogue context | Match the update rhythm of real-time data in smelting scenarios, avoid introducing outdated information from overly long context |
variables | Preset three variables: equipment ID, batch number, and job role | Match the multi-dimensional business fields in smelting scenarios, allowing prompt engineering to accurately call corresponding data |
| Maximum context length | 8000–12000 characters | Cover operation dialogue and parameter records for a single shift, avoid truncating key business information |
| Question optimization | Enabled | Adapt to professional terms in smelting scenarios, automatically optimize the accuracy of questions |
Three common mistakes
- Phenomenon: Multi-turn dialogue cannot continue the previous smelting equipment query, and each question requires re-stating the equipment number. Cause: The user-device association information of the current session is not bound via
chatId, and the context is not stored correctly. - Phenomenon: After the workflow runs, all embedded <AI Dialogue> node replies appear in the chat window. Cause: Output filtering rules are not configured, and intermediate node outputs that need to be hidden are not marked via
variables. - Phenomenon: Dialogue requests return errors about missing parameters. Cause: The prompt engineering does not explicitly require calling dedicated fields for smelting scenarios, resulting in returned results that do not comply with business format requirements.
How to confirm the configuration is correct
- Initiate a test dialogue, input "View yesterday's temperature data for converter 1", then follow up with "View oxygen content during the same period", confirm that the system automatically associates the context of converter 1.
- Call a test session bound to
chatId, switch to another test account, confirm that the dialogue records of the previous account cannot be viewed. - Run a test workflow containing multiple <AI Dialogue> nodes, check that only the final required reply is displayed in the chat window, and the outputs of intermediate nodes are not shown.
- View the session log in the FastGPT v4.17.0 backend, confirm that the
messagesparameter only retains dialogue content within the most recent 20 turns, with no overly long context records.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.