Data Profile for This Category
Data for this category comes from raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing equipment, and customer forging drawings. Daily documents include forging process specifications, cutting cards, heating furnace logs, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data updates per individual piece, batch, or furnace heat. Document fields include furnace batch number, forging batch number, heating temperature, holding time, forging ratio, and more. Corresponding units are degrees Celsius and minutes; forging ratio has no unit.
Constraints Imposed on Multi-Turn Dialogue and Prompt Engineering
This category’s data updates per single furnace heat or single forging batch. Multi-turn dialogue must accurately bind the corresponding furnace batch number and forging number to avoid mixing cross-batch data. Daily document fields include dedicated units and process parameters. Prompts must explicitly require matching field formats and units to prevent parameter mismatches. Multi-source data is scattered across different systems. Multi-turn dialogue must gradually guide retrieval of document fragments from corresponding data sources to complete cross-checks step by step. Errors may lead to forging scrap or service fracture. Dialogue must output check basis in each round of verification to facilitate traceability and troubleshooting.
Configuration Settings
All configurations below are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Only retain dialogue history for the current forging batch, filter out context from unrelated batches | Matches single furnace heat, single batch data update rules to avoid interference from cross-batch data |
variables | Preset two global variables: furnace batch number and forging drawing number | Quickly bind data sources for the corresponding batch, improve check efficiency |
chatId | Create independent dialogue sessions per forging batch | Isolate check processes for different batches, facilitate traceability of single-batch review records |
| Single document recall count | Top 5 entries | Adapts to the average number of documents per batch, avoids redundant information occupying model processing resources |
| Dialogue timeout | 600 seconds | Covers time required for document retrieval and verification in multi-turn checks, prevents mid-process interruptions |
| Maximum context length | 800–1200 characters | Adapts to the average length of documents per batch, avoids irrelevant content impacting dialogue logic |
Three Common Misconfigurations
- Phenomenon: A connection error prompt appears when calling the model, and recovery fails even after enabling a network proxy. Cause: Dialogue context binding rules are not configured correctly, causing the model to attempt to retrieve data source documents beyond permission scope, triggering a connection exception.
- Phenomenon: Irrelevant batch review results are displayed in dialogue outputs. Cause:
chatIdis not used to isolate sessions per batch, causing context from different batches to load together, leading to cross-batch data leakage. - Phenomenon: Parameters in dialogue outputs do not match document units, such as labeling holding time in degrees Celsius. Cause: Prompts do not explicitly require matching the unit format of document fields, causing the model to generate incorrect parameter labels.
How to Verify Correct Configuration
- View the dialogue session list, confirm that each forging batch corresponds to an independent session entry.
- When calling dialogue, check if input parameters include preset global variables such as furnace batch number and forging drawing number.
- After triggering multi-turn checks, verify that output results only include document fields and corresponding units for the current batch.
- Test cross-batch calls, confirm that dialogue contexts from different batches do not interfere with each other.
The values provided are common starting points and should be measured against the reader's own samples.