What this category’s data looks like
This category’s data originates from formulation and process libraries, raw material batch ledgers, reactor DCS systems, weighing record systems, LIMS laboratory management systems, and warehouse management systems. Data updates trigger for each production batch and each critical reaction step. Daily documents include batch production instructions, material feeding verification slips, reaction process records, cleaning confirmation records, finished product inspection reports, deviation and change orders. Fields cover batch numbers, feeding weights, reaction temperatures, residence times, cleaning validation results, and additional relevant metrics.
Constraints imposed on multi-turn dialogue and prompt engineering by these characteristics
The multi-product, batch-switching production model of this category requires multi-turn dialogue to bind exclusive context for the current production batch, to prevent mixing data across batches. The pattern of data updates per batch and critical step means prompts must explicitly limit calls to only the current batch’s feeding, reaction, cleaning, and related documents, to avoid retrieving expired or irrelevant data. Formulation confidentiality requirements demand that prompts include information filtering rules, to prevent leakage of formulation details. Multi-step traceability needs require multi-turn dialogue to retain conversation history of prior feeding and reaction parameters, to support gradual troubleshooting of defect causes.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context Retention Rounds | First 10 conversation turns | Supports gradual multi-step defect troubleshooting, avoids excessive context consuming system resources |
| Knowledge Base Filtering Rules | Match by current batch number | Aligns with this category’s batch-updated production data, only retrieves documents for the current batch |
variables | Bind batch number, reactor number | Accurately calls exclusive data such as feeding and reaction records for the corresponding batch |
chatId | Bind current production batch ID | Ensures multi-turn dialogue context for the same batch is not mixed, isolates data across batches |
messages | Maximum 8000 tokens per single conversation | Adapts to FastGPT v4.17.0’s context processing limit, matches the length of fine chemical production documents, avoids context overflow |
| Prompt Filtering Rules | Block core formulation parameter fields | Meets the formulation confidentiality requirements of this category, prevents sensitive information leakage |
Three common misconfigurations
- The symptom is that outputs from HTTP request orchestration steps do not appear in conversation results. The cause is failing to configure
variablesto synchronously return the output fields of these steps, resulting in these step data not being included in the conversation context. - The symptom is that documents from different production batches are incorrectly retrieved in the current troubleshooting conversation. The cause is failing to bind
chatIdto the current batch ID, failing to achieve context isolation between batches. - The symptom is that sensitive information such as core formulation parameters appears in conversation outputs. The cause is failing to configure prompt filtering rules, failing to block the output of confidential formulation fields.
How to confirm the configuration is correct
- Manually enter a known batch number, trigger a conversation, verify that retrieved documents only include feeding and reaction records for that batch.
- Initiate two or more troubleshooting conversations, verify that
chatIdis bound to the current batch ID, and that the context retains previous conversation content. - Test a query that includes core formulation parameters, verify that sensitive field information is not leaked in the conversation output.
- Initiate an HTTP request orchestration test, verify that outputs from orchestration steps appear in the conversation results.
Note: The values listed above are common starting points, and should be validated against the reader’s own samples.