What this category of data looks like
Refinery and chemical production service inquiry data originates primarily from production shift logs, process procedure manuals, equipment maintenance records, and safety inspection archives. Data update frequency follows two patterns: real-time production parameters are updated with each shift (approximately 8 hours of operations), while static process specifications are only updated after process iteration and revision. Document structure is organized per individual work instruction or equipment file, with fields including equipment number, process temperature, pressure thresholds, operation steps, and additional relevant details. Units uniformly use internationally recognized industrial standard units such as ℃, MPa, m³/h.
Constraints imposed on multi-turn dialogue and prompt engineering
The high-frequency updates of real-time production parameters require multi-turn dialogue to retain parameter context from the most recent 3 shifts, to avoid calling expired historical data. The fixed structure of static process specifications requires prompts to clearly distinguish between real-time operating condition queries and standard procedure invocation scenarios. The specialized units for equipment and process fields require prompts to mandate output matching industrial standard units, to prevent confusion of parameter units. The compact, single-document structure requires retrieval to prioritize matching equipment numbers or process links mentioned in queries, to avoid mixing invalid context across documents. Multi-turn dialogue must track equipment numbers mentioned in queries, to prevent retrieval deviations caused by interrupted context.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Max Context Length | 8000–12000 characters | Matches the length of process parameters and historical dialogue required for single-turn refinery and chemical production conversations |
| Question Optimization Switch | Enabled | Assists in organizing equipment numbers and process links mentioned in queries, improving retrieval accuracy |
variables | Bind global production shift and workshop ID variables | Automatically adapts to process specifications and real-time parameters for the current operating scenario |
| Single Retrieval Limit | Top 6 entries | Matches the typical size of individual refinery and chemical archives, preventing redundant context from being included |
messages | Retain the most recent 4 turns of dialogue content | Covers the information transfer cycle of shift handovers, preventing context breaks |
chatId | Automatically generate session ID | Used to bind multi-turn dialogue context, preventing content mixing across different sessions |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: After invoking knowledge base dialogue, returned content does not match the current query, and instead relates to process parameters of other equipment. Cause: The equipment number mentioned in the query is not forcibly bound in the prompt, leading to retrieval matching irrelevant documents.
- Phenomenon: When configuring prompts in a workflow, global variables do not take effect, and variable placeholders in the prompt are not replaced. Cause: Global production variables are not correctly bound in the
variablesconfiguration item, or the variable pending verification format does not meet system requirements. - Phenomenon: The AI dialogue node occasionally returns the
chat:LLM_model_response_emptyerror, or outputs empty content. Cause: Large model call timeout or insufficient retrieved context, preventing the model from generating a valid reply.
How to Verify Proper Configuration
- Input a query containing specific equipment numbers and process parameters, and confirm returned content matches specifications for the relevant equipment.
- View dialogue history to confirm that the
messagesconfiguration item retains the specified number of context turns. - Trigger workflow execution, and check whether the global variables bound by
variablesare correctly replaced into the prompt. - Simulate multiple consecutive queries, and confirm that no
chat:LLM_model_response_empty-type errors occur.
The values provided here 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.