What the data for this category looks like
Petroleum refining technical documents cover operating procedures, production plans, material balance reports, distillation outlet quality reports, interlock ledgers, shift handover records, and change management documents. Data sources include DCS/APC systems, LIMS laboratory management systems, tank area metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Data update frequencies cover real-time, per-shift, and daily. Document fields include unit load rate, distillation range parameters, sulfur content, tank capacity, interlock trigger time, and more. Units and formats vary significantly across different documents. Some real-time data requires second-level refresh, while batch reports are aggregated per shift.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-source heterogeneous data and high-frequency update characteristics of petroleum refining require multi-turn dialogue to filter redundant real-time data, avoiding exceeding the model context limit. Diverse document structures and field units require prompts to clearly specify document classification rules and unit unification requirements, ensuring parameter consistency during cross-review. The business logic where multiple continuous units are coupled via logistics and energy networks requires multi-turn dialogue to track interaction contexts across different units, preventing parameter confusion between units. The need for real-time handling of process fluctuations requires prompts to prioritize returning conclusions that can be directly used for scheduling.
How to configure the settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind a unique session identifier | Ensure correct context association for multi-turn dialogue, avoid parameter confusion across sessions |
messages | Retain the last 10 interaction records | Cover core business interactions, control total context length to stay within model limits |
variables | Bind business variables such as unit number, crude oil type, tank area number | Automatically fill exclusive parameters in the context, improve cross-review accuracy |
| Chunk overlap length | 150-200 characters | Avoid loss of key process information such as unit numbers and distillation range parameters across chunks, adapt to cross-review needs for long petroleum refining documents |
| Maximum context length | 8000-12000 token | Adapt to total character volume of long petroleum refining documents and multi-turn interactions, match the model context limit of FastGPT v4.17.0 |
Three common configuration mistakes
- Phenomenon: Second and subsequent questions in a dialogue off-topic. Cause: The
chatIdparameter is not correctly bound, and historical interaction records for themessagesparameter are not tracked, leading to broken context association. - Phenomenon: An error triggers after uploading a document exceeding the model's supported context limit. Cause: No document chunking parameters are set, and long documents are not automatically sliced, resulting in data loading exceeding the context limit.
- Phenomenon: Redundant tool call logs appear in outputs after tool invocation. Cause: The
variablesparameter is not configured to filter unnecessary call information, resulting in returned content containing debug logs unnecessary for engineers.
How to verify the configuration is complete
- Import a petroleum refining material balance report, initiate 3 consecutive related questions, and confirm the answer correctly references unit load data from historical interactions.
- Upload an operating procedure with over 10,000 characters, confirm the system automatically splits the document and completes retrieval without context overflow errors.
- Initiate a request including tool invocation, confirm the returned result only contains AI dialogue content with no redundant tool call logs.
- View conversation log storage settings, confirm retention duration and log count meet business requirements, with no invalid log accumulation.
The values provided are common starting points and should be measured against the reader's own samples.