What Data for This Category Looks Like
This category's data comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, and customer drawings and technical specifications. Update frequency is per heat, per batch, or daily. Daily documents include foundry process cards, batching lists, heat records, mold histories, first article inspection records, material reports, and nonconforming product review forms. Each document contains fields such as heat number, mold number, alloy batching values, temperature readings, composition test values, pouring parameters, and more. There is no unified format, and some fields require cross-system cross-checking.
Constraints for Multi-turn Dialogue and Prompt Engineering
Frequent updates of data per single heat or single batch require limiting the valid historical range of multi-turn dialogue contexts to avoid interference from old batch data with current audits. Field differences across multiple cross-system documents require that prompts clearly specify document types and field rules to be referenced, preventing confusion between data from different sources. Complex audit logic such as batching ratio verification and defect tracing requires multi-turn dialogue to gradually break down audit steps, avoiding overloading a single prompt. At the same time, the requirement for audit result rigor requires embedding verification links in dialogue to ensure each audit step corresponds to accurate document records.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | Previous 20 turns of dialogue | The audit process for a single foundry heat typically includes 10-15 steps. Retaining 20 turns covers the complete audit logic while avoiding overload |
| Single Knowledge Base Recall Limit | Top 8 matching documents | Documents related to a single casting batch do not exceed 10. Recalling 8 covers core reference materials while reducing redundancy |
| Timeout Setting | 600 seconds | Melting data synchronization and cross-system document retrieval take a certain amount of time. 600 seconds covers most scenarios |
| Variable Binding | Bind the heat number parameter in variables | Accurately locate exclusive documents such as batching lists and heat records for the current audit batch |
| Historical Message Management | Automatically truncate messages to the most recent 15 turns | Avoid interference from old batch data with the audit logic of the current dialogue |
| Dialogue ID Binding | Bind chatId | Distinguish dialogue contexts for different heats, preventing data cross-use |
Three Common Misconfigurations
- Returned results from calling the dialogue interface do not associate documents for the specified heat. The cause is failure to bind the
variablesparameter in the request, preventing the prompt from accurately matching records for the corresponding batch. - Response lag occurs after multi-turn dialogue exceeds 10 turns. The cause is failure to limit the historical turns of
messages, leading to overload of context data and preventing the system from processing requests quickly. - A timeout error is returned immediately after initiating a dialogue. The cause is failure to set a reasonable timeout threshold, where melting data synchronization or document retrieval exceeds the default time limit.
How to Verify Proper Configuration
- Initiate a test dialogue, enter the specified heat number, and check whether the heat number field bound to
variablesis automatically included in the request parameters. - Initiate 18 consecutive turns of dialogue, check whether the system automatically retains the
messagescontent of the most recent 15 turns, with old turn contents properly truncated. - Simulate a melting data synchronization delay scenario, verify that the system completes the response and returns audit results within 600 seconds.
- Enter test parameters for a non-current heat, check whether the returned results do not associate document records for the incorrect batch.
The values provided are common starting points and should be measured against the reader's own samples.