Data Profile for This Category
Data for this category originates from MES systems, online inspection equipment, laboratory records, process parameter documents, customer quality standards, and historical defect libraries. Daily generated documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product review forms, release orders, and quality monthly reports. Data is updated by production batch and shift team. Some online inspection data is collected continuously. Fields include batch unique identifier, inspection parameter values, defect type codes, release status, inspection time, and more. Batch identifiers use a 6-digit alphanumeric combination. Time format follows YYYY-MM-DD HH:MM:SS. Defect types must match a predefined standardized terminology library.
Constraints on Multi-turn Dialogue and Prompt Engineering
The multi-source heterogeneous data structure of this category requires prompts to clearly define retrieval scopes, to avoid mixing data across batches or workshops. Dynamically updated batch-level data requires the messages context to bind the current session’s batch identifier, ensuring that confirmed basic parameters are not lost during multi-turn dialogue. The extensive set of document fields requires multi-turn dialogue to guide users to input or confirm necessary information step-by-step, preventing parameter extraction errors caused by requesting too many fields at once. Additionally, finished product release involves quality permissions, so prompts must implicitly verify the batch permission scope associated with the operator, to prevent unauthorized queries.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind current batch number, workshop code, inspection date | Data for this category is updated by batch or workshop; dynamic variables enable precise targeting of target data. FastGPT v4.17.0 supports this parameter configuration |
messages | Retain the most recent 10 turns of dialogue context | Multi-turn quality traceability requires linking historical queries, avoiding repeated input of basic information and ensuring context coherence |
chatId | Bind production batch unique identifier | Ensures dialogue context for the same batch is not mixed with other batches, preventing data retrieval errors |
| Multi-turn session timeout | 1200 seconds | Quality traceability involves multi-step verification and document retrieval, requiring sufficient operating time |
| Input field validation rules | Adjust based on actual measurement calibration | This category has many document fields with high standardization requirements; adjustments must be made based on the actual field structure of control plans and SPC reports |
| Retrieval scope restriction | Only retrieve the historical defect library for the current workshop or batch | Avoid interference from cross-batch data, improving retrieval and analysis accuracy |
Common Misconfigurations
- Phenomenon: Extracted parameters in dialogue do not match actual document fields. For example, irrelevant information is incorrectly extracted as inspection parameters | Cause: The prompt does not clearly specify that fields must be extracted from documents such as control plans and SPC reports for this category, leading to generalized extraction errors
- Phenomenon: After enabling voice input, speech-to-text results are empty or not synchronized to the current session | Cause: Corresponding voice model callback parameters are not configured, or the session identifier in
variablesis not bound, leading to context loss - Phenomenon: The same batch information is requested repeatedly during multi-turn dialogue | Cause: Confirmed batch parameters are not retained in the
messagescontext, causing the system to repeatedly ask for basic information
How to Verify Successful Configuration
- Input the inspection parameters of a known batch, verify whether the dialogue automatically associates data from the corresponding SPC report
- Initiate a multi-turn traceability process, check whether the context retains confirmed basic parameters such as workshop and batch
- Test the voice input function, confirm that voice content is correctly converted to text and bound to the current session
- Manually input a non-standardized defect type, check whether the system triggers field validation prompts
The values provided here are common starting points and should be evaluated against the reader's own samples.