What the data for this category looks like
Data for basic chemical industry internal office and employee services originates from production ERP systems, equipment maintenance work order systems, OA office platforms, and employee attendance systems. Production ERP systems update production ledger data per shift. Equipment maintenance work order systems sync equipment maintenance logs in real time when work orders are completed. OA office platforms refresh approval and attendance data daily.
Document structure includes fields such as equipment number, shift, output, energy consumption (units: tons, kilowatt-hours), employee ID, department, position, approval node, and spare part model. Some long documents include multiple sections of process descriptions and parameter records.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Configuration for basic chemical industry multi-turn dialogue and prompt engineering must adapt to constraints imposed by data characteristics. Real-time requirements for production and maintenance data require dialogue to quickly pull the latest ledger and work order information. Prompts must explicitly specify prioritizing the latest data within the current session. Multiple fields have dedicated units, so prompts must mandate retaining corresponding units in output results to avoid parameter confusion. Long documents include multiple process sections and parameters, so multi-turn context length must be limited to prevent loss of key process nodes due to context overflow. Employee-related data involves employee IDs and departments, so dialogue must verify consistency of context fields to prevent mixing of data across sessions.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 characters | Basic chemical industry internal documents often include multiple process sections and parameters; this range can cover complete work order or ledger fragments and prevent context overflow |
| Number of recalled documents per single call | Top 3–5 | Internal office data has many fields and is closely related; a small number of highly relevant documents can avoid information overload |
| Prompt prefix configuration | Mandatorily require retaining dedicated units for equipment and energy consumption | Basic chemical industry data includes dedicated units such as tons and kilowatt-hours; output format must be strictly aligned |
| Session timeout duration | 300 seconds | Internal office conversations mostly involve approval queries and work order follow-ups; this duration covers typical consultation cycles |
| Variable binding rules | Bind employee ID and department variables to session context | Employee service scenarios require associating personal position information; reuse session variables via the variables parameter |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: When initiating an employee identity query, the chat log does not return the Feishu username, and the corresponding field is empty. Cause: The corresponding identity variable was not bound in the session, and the user identity identifier was not passed via the
variablesparameter. - Phenomenon: Uploaded production ledger or maintenance work order data is not included in statistical summaries, and the output results do not contain the uploaded content. Cause: The uploaded knowledge base file was not associated with the current session context, and the prompt did not explicitly require calling the uploaded data.
- Phenomenon: Stuttering occurs during conversations between a local model and the knowledge base, and response timeouts happen. Cause: The number of recalled documents per single call was set too high, causing the context length processed by the model to exceed the carrying capacity of local computing power.
How to Confirm the Configuration Is Correct
- Initiate a conversation involving production data queries, check whether the output results include the specified units (such as tons, kilowatt-hours) to confirm that the prompt configuration is effective.
- Upload a test maintenance work order document, initiate a statistical summary request, confirm that the output results include the content of the uploaded document to verify that data association is working properly.
- View the session log, confirm that the
chatIdmatches the current session identifier, and that themessagesparameter includes complete conversation turns, with no context truncation. - Initiate a query related to employee identity, confirm that the returned results are associated with the employee ID and department information bound to the current session to verify that variable binding is effective.
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.