What Data for This Category Looks Like
This category’s data primarily originates from steel coil labels and quality certificates, press and feeder operation data, mold management systems, MES systems, coordinate measuring machine or fixture inspection data. Daily documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, dimensional inspection reports, and mold change records. Fields cover part numbers, mold numbers, inspection dimensions, mold change times, material consumption values, and similar items. Updates are synchronized per coil of material, per batch of workpieces, or each time a mold is changed.
Constraints Imposed by These Characteristics on the Multi-turn Dialogue and Prompt Engineering Link
Frequent data updates and strong batch correlation require that dialogue contexts be isolated by batch to prevent cross-batch data mixing. Multiple data sources and document structures require that multi-turn dialogue gradually retrieve information from different systems; loading all data at once will increase response latency. A large number of professional fields with fixed formats means prompts must accurately match document fields, otherwise data cannot be accurately extracted or audited. Significant parameter differences across mold change batches require dynamic adjustment of verification logic during dialogue to avoid applying fixed rules.
How to Configure
This page is written based on the configuration logic of FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier for each coil of material or each mold change | Stamping batch data is updated frequently, so dialogue contexts for different batches must be isolated to avoid data mixing |
messages | Retain the most recent 5 turns of dialogue messages | Multi-turn dialogue for stamping inspection and audit typically does not exceed 5 turns; overly long messages reduce dialogue processing efficiency |
| Maximum context length | 800–1200 characters | Document data related to a single stamping batch is moderate in volume; overly long contexts reduce dialogue response speed |
variables | Preset fixed fields including stamping part numbers, mold numbers, mold change times, and similar items | Reduces the need to repeatedly enter basic information during multi-turn dialogue, improving audit efficiency |
| Single dialogue return result limit | Top 3 entries | Stamping inspection records have a limited number of entries per batch; too many results will disrupt dialogue logic |
| Timeout threshold | 600 seconds | Retrieving stamping data typically takes a certain amount of time; 600 seconds covers response requirements for most scenarios |
Three Common Configuration Errors
- Phenomenon: When calling the API, after passing in
chatId, the backend log does not associate the corresponding identifier. Cause: The binding logic for thechatIdparameter was not enabled in the dialogue configuration, or the format of the passedchatIddoes not meet platform requirements. - Phenomenon: Form nodes in the dialogue interface fail to render fields normally, displaying abnormally. Cause: Form parameters were not configured according to the field structure of stamping documents, resulting in failed HTML rendering matching.
- Phenomenon: Users accessing via login-free links delete dialogue content, and backend logs are synchronously deleted. Cause: The log synchronous deletion rule for login-free dialogue was not disabled in system configuration, retaining the binding relationship between dialogue and logs.
How to Verify Proper Configuration
- Initiate an inspection dialogue for a specific batch of stamped parts, and check whether the backend log carries the correct
chatIdidentifier. - Submit simulated mold change record data, and verify whether the dialogue can correctly extract preset
variablesfields. - Adjust the maximum context length parameter, and test whether the dialogue response time meets actual business requirements.
- Create a login-free test link, delete the dialogue, and check whether the backend log retains the corresponding record.
The values provided are common starting points and should be measured against sample data specific to the actual deployment.