What Data for This Category Looks Like
Data sources for this category include raw material quality certificates, heating furnace systems, press collection data, workshop MES, non-destructive testing instruments, and customer forging drawings. Daily associated documents cover forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data is updated in units of single-batch forgings and single-heat heating cycles. Each data entry includes fields such as furnace batch number, process parameters, testing results, and mold number.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
This category’s data is updated in units of single heat and single batch. Multi-turn dialogue must limit the context scope to avoid mixing historical data from non-current batches. The data includes multi-dimensional process and testing fields, so prompts must clearly specify the document types and fields to call, preventing the generation of irrelevant content. Data sources are scattered and updated frequently, so multi-turn dialogue should gradually guide users to provide key identifiers such as furnace batch number and forging part number to narrow the search scope. Additionally, defect analysis results are directly linked to batch scrap risk, so each dialogue output must be associated with the corresponding data source to ensure traceability.
Configuration Settings
This configuration is based on FastGPT v4.17.0, adapted to the quality traceability requirements of the forging and die forging category.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current forging batch number | Ensure that multi-turn conversations for the same batch are linked to the same data context, avoiding cross-batch confusion |
messages | Retain the first 3–5 rounds of the current session | Filter irrelevant historical content and focus on problem analysis for the current batch |
variables | Preset furnace batch number, mold number, and ultrasonic testing result fields | Quickly access forging-specific documents and testing data |
| Maximum context length | 800–1200 characters | Adapt to the total character count of single-heat data, preventing exceeding model input limits |
| Session expiration time | 72 hours | Match the typical processing cycle of forging batches, avoiding interference from cross-batch sessions |
Three Common Misconfigurations
- When calling the model, the log shows the token field is a fixed value, and the interface returns an authentication failure. Cause: The custom token was not correctly passed to the session context during configuration, causing the token passed by FastGPT to not match the preset value.
- The results returned by multi-turn dialogue include forging data from non-current batches. Cause:
chatIdwas not used to bind the current forging batch number, causing sessions of different batches to share context. - The analysis content generated by the prompt does not reference forging ratio or fiber direction. Cause: The prompt did not clearly limit usage to only forging process-related fields and documents, causing the analysis logic to deviate from category-specific characteristics.
How to Verify Successful Configuration
- Enter the forging furnace batch number, initiate the first round of dialogue, and verify that the returned documents include the heating furnace records and ultrasonic testing reports for that furnace batch number.
- Initiate more than two rounds of dialogue, confirm that the context only retains relevant content from the current session and does not mix data from other batches.
- Check the model call log to confirm that the passed token matches the preset value, with no replacements.
- Export the conversation history, verify that it only contains content from the current session and is linked to the correct forging batch identifier.
The values provided here are common starting points and should be measured against the reader's own samples.