What this category’s data looks like
Data for this category comes from material grade standards, customer technical requirements, furnace temperature control instruments, furnace temperature uniformity test reports, hardness tester and metallographic equipment output results, and workshop MES systems. Daily documents include heat treatment process cards, furnace loading layout drawings, single-heat furnace temperature curves, hardness and metallographic test reports, equipment inspection records, and exception handling sheets. Data is updated per single heat or batch. Fields cover heating, holding, and cooling parameters, furnace loading position, furnace temperature fluctuation range, hardness values, and metallographic structure grades. Most units are degrees Celsius, megapascals, millimeters, and similar standard units.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" workflow
This category’s data is updated frequently per single heat or batch. Multi-turn dialogue must accurately retain the audit context of the current batch to avoid repeated retrieval of historical batch documents. Documents include unstructured furnace temperature curves and furnace loading layout drawings. Prompt engineering must explicitly require associating visual data and text parameters for the target batch, to ensure audit logic aligns with actual production scenarios. Most physical parameters have dedicated units. Prompt engineering must enforce matching between parameters and their units to prevent mismatches during audits. High-frequency, short-cycle data requires limiting the dialogue context window to the current batch or recent batches, to avoid redundant data interfering with audit judgments.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Pre-set heat batch numbers, material grades | Audit core requires matching batch and material parameters to reduce invalid inputs |
| Single recall document limit | Top 3-5 entries | Documents related to a single heat fall within this range, avoiding redundant data interference |
messages | Retain the last 10 turns of dialogue | Multi-turn audit steps typically do not exceed 10 turns; excessive turns cause context overflow |
| Maximum context length | 8000-12000 token | Total character count of documents for a single heat batch fits this range, covering multi-turn dialogue records |
| Question optimization toggle | Enabled | Audit scenarios require repeated refinement of questions; optimization improves retrieval accuracy |
Three Common Misconfigurations
- Symptom: An error pops up during formal dialogue after connecting to the heat treatment process knowledge base, but no issues appear in preview mode. Cause: Knowledge base mounting permissions or path configurations differ between formal and preview environments, and parsing for the unstructured format of heat treatment documents is not supported.
- Symptom: Dialogue cannot load complete data after connecting to multiple heat treatment knowledge bases. Cause: Total character count of connected knowledge bases is not limited, exceeding the system’s default bearing range.
- Symptom: Furnace temperature data from non-current batches appears repeatedly during multi-turn dialogue. Cause: Batch parameters are not pre-set via
variables, leading the retrieval scope to cover historical batch data.
How to Verify Configurations Are Correctly Set
- Submit an audit request for a specified heat batch number, and verify that recalled documents only match the process card, furnace temperature curve, and test report for that batch.
- Launch multiple rounds of refined questions consecutively, and confirm that the dialogue context retains historical steps and no irrelevant batch data appears.
- Test connection to multiple heat treatment documents, and check that loading and retrieval processes have no abnormal errors.
- Copy dialogue output content, and confirm that the format meets business audit display requirements.
The values provided are common starting points and should be measured against the reader's own samples.