FastGPT v4.17.0
What This Category of Data Entails
The data for this category comes from formula systems, raw material batch ledgers, internal mixer operation logs, Mooney viscosity and rheometer test records, vulcanizer process parameters, and laboratory finished product performance reports. Core business documents include formula sheets, weighing and feeding records, mixing temperature curves, rubber compound quick test results, vulcanization process cards, mold usage records, and finished product performance test reports. Data updates occur for every raw material batch, every batch of mixed rubber, and every vulcanized product per mold run. Document fields include feeding ratio, internal mixer temperature range, vulcanization duration, Mooney viscosity value, and additional relevant metrics. Units include kilograms, degrees Celsius, minutes, MPa, and other standard industrial units.
Constraints on Multi-turn Dialogue and Prompt Engineering
High data update frequency tied to production batches necessitates limiting context turns in multi-turn dialogue. This prevents interference from old batch data on current sessions. Multiple document types with clearly subdivided fields require prompts to precisely specify extraction of specific fields from corresponding documents. This avoids confusion between mixing and vulcanization process parameters. Multi-source device data requires differentiated validation. Prompts must clearly define parameter rules for each data source, such as differences between internal mixer curves and vulcanizer process cards. Severe consequences of single-production errors require adding parameter validation steps in multi-turn dialogue. This ensures accuracy of key information such as feeding sequence, temperature, and duration.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Turns | First 8–12 turns | Matches operation intervals per mold run, avoids interference from redundant historical data on current sessions |
| Prompt Bound Variables | Use variables to bind batch numbers, formula IDs | Quickly associates exclusive document data for the corresponding production batch |
| Single-turn Message Limit | 2 messages per turn | Adapts to continuous interaction requirements for feeding confirmation and exception feedback |
| Knowledge Base Retrieval Count | First 6 entries | Covers three core business document types: mixing, vulcanization, and finished product testing |
| Global Token Tracking | Enabled | Facilitates monitoring of single-turn session costs and compliance |
| Knowledge Base Sync Trigger | Triggered when session completes | Ensures generated exception reports can be synced to the target knowledge base |
Three Common Misconfigurations
- Exception reports generated via dialogue cannot be automatically synced to the knowledge base: Knowledge base update rules triggered after session completion are not configured. Generated documents only remain in the dialogue context and cannot be called by subsequent sessions.
- Token consumption data for single-turn dialogue is not displayed in the interface: The interface display switch for global token tracking is not enabled. Session consumption details corresponding to
chatIdcannot be viewed. - A parameter error is triggered when sending two consecutive messages: The single-turn message limit is not set to 2 messages, and the number of elements in the
messagesarray is not verified. This leads to parameter exceptions returned by the API.
How to Confirm Configuration is Complete
- Initiate a dialogue that includes a specified batch number. Verify that the prompt automatically associates weighing records and mixing curve data for the corresponding batch.
- Send two consecutive messages. Confirm that the dialogue process can normally receive and process the two input contents without parameter errors.
- View the dialogue history panel. Confirm that token consumption data for single-turn dialogue is normally displayed.
- After generating an exception report, manually trigger the sync operation. Verify that the document is successfully written to the target knowledge base.
The values provided are common starting points and should be validated against applicable on-site production samples.