What the data for this category looks like
This category’s data is sourced from DCS/APC control systems, LIMS laboratory information management systems, tank farm metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Update frequencies include real-time, shift-based, and daily. Daily documents include operating procedures, production plans, material balance sheets, distillate quality reports, interlock ledgers, shift handover records, and change management documents. Fields cover crude oil property parameters, unit operating load, product distillation range indicators, tank farm inventory, and energy consumption data. Units include tons, degrees Celsius, cubic meters, and other standard industrial units.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering segment
Data sources are scattered and update frequencies vary significantly. Multi-turn dialogue must filter context content based on data timeliness to avoid calling expired historical data. There are multiple document types and numerous specialized fields. Prompts must explicitly specify allowed document categories and field ranges to reduce irrelevant information interference. Multiple continuous production units are coupled through logistics and energy networks. Multi-turn dialogue must retain cross-unit associated context to support load coordination and material balance calculations. Real-time data updates frequently. Context window token usage must be limited to avoid call timeouts that negatively impact audit efficiency.
How to Configure the Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to a fixed value per conversation session | Ensures multi-turn dialogue context association, retains cross-stage supplier audit progress and data references |
messages | Retain the most recent 10 turns of dialogue and 3 pieces of historical key context | Balances context completeness and token consumption, adapts to the multi-step audit logic of refining operations |
variables | Inject current crude oil batch ID, unit number, and audit node type | Enables prompts to accurately call exclusive data from corresponding data sources, avoids confusion between material balance data of different batches |
| Context window size | 8000–12000 tokens | Adapts to the context requirements of multiple documents and fields in refining operations, adjust based on actual measurements |
| Conversation file expiration time | 7 days | Matches the update cycle of shift handover records and change management documents, aligns with the default configuration logic of FastGPT v4.17.0 |
Three Common Configuration Errors
- Phenomenon: Dialogue return results include expired tank farm metering data, and logs show context expiration prompts. Cause:
chatIdis not correctly bound, causing the session context to not associate with the 7-day expiration time parameter, and expired historical audit data is called. - Phenomenon: When inserting multi-task AI dialogue in batch execution nodes, context crossover occurs, and single-turn dialogue returns material balance data across batches. Cause: Exclusive batch ID is not injected via
variables, causing different dialogue sessions to share context variables. - Phenomenon: Multi-turn dialogue calls exceed token limits, triggering task timeouts. Cause: The number of retained
messagesturns is not restricted, and all historical dialogue and documents are passed in, exceeding the window limit.
How to Verify Configurations Are Correct
- Initiate a test dialogue that includes crude oil batch ID and unit number, check whether the returned results only associate material balance and distillate quality report data for the current batch.
- View dialogue logs, confirm that
chatIdremains fixed within the same session, and the number of retainedmessagesturns matches the configured value. - Re-access historical conversations 7 days after testing, confirm that the system prompts that files have expired and cannot be called, verifying that the expiration time configuration is effective.
- Initiate multiple consecutive dialogue rounds for unit load coordination, check whether the context retains the previous round's unit parameters, and no cross-session crossover occurs.
The values provided are common starting points and should be measured against the reader's own samples.