What the Data for This Category Looks Like
The policy and regulatory basis data for refinery and chemical production originates from official regulations, industry standards, and implementation guidelines published by the National Energy Administration, Ministry of Emergency Management, and China Petroleum and Chemical Industry Federation. The knowledge base updates in real time on the day a new policy is released. Document structure follows chapter-based articles, with fields including document number, issuing authority, effective date, applicable production links, and more. Units used include clause numbers, page numbers, and date formats. Individual documents are typically over 5,000 characters in length. Some industry standards include multi-page attachments.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Policy documents have significant length. Context windows for multi-turn dialogue must be limited to a reasonable range to prevent redundant information from interfering with current reasoning. Policies vary in effective dates and hierarchical levels. Multi-turn dialogue must include timestamps and policy hierarchy information from historical queries to ensure answer timeliness and authority. Some policy terms have industry-specific definitions. Prompt engineering must clarify the scope of these terms to avoid deviations from general interpretations. Policy update frequency is high. Dialogue processes must perform real-time verification of currently used policy versions to prevent expired content from being called.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages context management | Retain the most recent 8 conversation turns | Refinery policy Q&A mostly uses progressive queries. Excessive historical content distracts reasoning. 8 turns covers complete business logic |
variables variable injection | Inject the policy library ID associated with the current session | Accurately match the dedicated policy dataset for refinery and chemical production, avoiding interference from cross-category data |
chatId session identifier | Bind to the unique session ID | Ensure persistent context for multi-turn dialogue, preventing data mixing between sessions |
| Maximum context length | 800–1200 characters | Refinery policy documents are lengthy. This range covers core policy clauses and avoids context overflow |
| Single recall upper limit | Top 5 results | Policy documents have clear entry structures. Excessive results make prompts overly lengthy and reduce reasoning efficiency |
| Timeout duration | 60 seconds | Policy queries require retrieval of multiple related documents. This duration reserves sufficient time for retrieval and reasoning |
Three Common Misconfigurations
- Issue: Only 2 context entries are displayed in conversation details, which does not match the configured 8-turn context limit. Cause: The
chatIdparameter is not correctly bound, leading to failure to persist session context. Only temporary data from the current turn is loaded. - Issue: Previous round policy conclusions cannot be used as input content for current prompt engineering in multi-turn dialogue. Cause: Previous round AI output results are not injected into current session variables via
variables, preventing prompts from reusing historical conclusions. - Issue: Circular logic cannot correctly judge result compliance after triggering. Cause: The input of the question classification node is not bound to the output of the AI dialogue node, leading to failure of the circular branch to trigger correctly.
How to Verify Successful Configuration
- Open the session details page, and confirm that the number of context entries displayed in the
messagesparameter matches the configured retention turns. - Initiate a progressive policy query. For example, first query "safety specifications for refinery and chemical production", then query "effective date of these specifications". Verify that the next round of prompts includes conclusions from the previous round.
- Trigger custom circular logic, and observe whether the question classification node correctly identifies AI output results and triggers corresponding branches or ends the workflow.
- Check session logs to confirm that the
chatIdparameter remains consistent across each session interaction, ensuring correct context association.
The values provided above are common starting points. They should be evaluated against the reader’s own specific 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.