What the data for this use case looks like
Refinery and chemical production business data primarily comes from real-time operation logs of DCS control systems, paper and electronic patrol records, production work order systems, and safety operation procedure documents. Data is updated at three frequencies: real-time for equipment operating condition parameters, daily for shift production records, and monthly for compliance document updates. Most individual documents combine structured tables (with fields such as equipment ID, operating temperature, pressure values, patrol time, etc.) and long-text operation procedures. Common units include degrees Celsius, megapascals, and cubic meters per hour.
What constraints these characteristics impose on multi-turn dialogue and prompting
The high-frequency updates of real-time operating data require that multi-turn dialogue context stays relatively short, to avoid loading timeouts. Structured equipment parameter fields require prompts to clearly specify extraction formats and units, to prevent output confusion. Embedding long-text operation procedures requires limiting the context length of single-round calls, to avoid exceeding the model's context processing limit. Multi-turn dialogue must retain key identifiers such as equipment ID and shift number from previous interactions, to ensure subsequent questions can be linked to the corresponding production scenario.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Single-turn dialogue context limit | First 8 rounds of messages + current input | Refinery and chemical production shift interactions are mostly continuous operations; 8 rounds are sufficient to cover shift logic and avoid context overload |
| Session binding identifier | chatId | Bind interactive sessions corresponding to production teams or equipment, to ensure correct context association and avoid cross-session confusion |
| Dynamic parameter passing | variables | Used to pass on-site dynamic information such as equipment ID, shift time, and work order number |
| Knowledge base recall count | Top 6 entries | Match the association needs of structured parameters and long documents for refinery and chemical production, covering core business information of equipment operating conditions, work orders, and safety specifications |
| Dialogue timeout threshold | 600 seconds | Avoid timeout of real-time data requests, and adapt to the response rhythm of on-site operations |
| Context truncation rule | Calibrated based on actual measurements | For high-frequency updates of real-time data, adjust truncation timing to balance information integrity and response speed |
Three Common Misconfigurations
- Phenomenon: The dialogue interface does not return knowledge base matching results, and the log shows that the
messagesfield does not carry recalled content. Cause: The context recalled by the knowledge base is not correctly spliced into the dialogue message queue. - Phenomenon: Multiple LLM calls triggered by a single input do not produce merged output. Cause: The parallel calling and result merging logic of multi-task prompts is not configured via
variables. - Phenomenon: The output of the program execution module cannot be connected to subsequent AI dialogue. Cause: The execution result is not passed to the dialogue module as a
variablesparameter, and is not bound to the currentchatIdsession.
How to Verify Correct Configuration
- Use the built-in session log panel of FastGPT v4.17.0, initiate a question containing an equipment ID, and check whether the
messagesfield in the dialogue history includes document fragments of the corresponding equipment's operating parameters. - After configuring multi-task prompts, enter test content, and check whether the output merges the generation results of multiple prompts.
- After running the program execution module, pass the output result to the dialogue module, and check whether the dialogue references the execution result as background knowledge.
- Switch to different
chatIdto initiate questions, confirm that the context will not be confused across sessions, and only retain the interaction records of the current session.
The configuration values listed above are common starting points, and should be evaluated 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.