What This Category of Data Looks Like
This category of data comes from MES systems, online inspection equipment, laboratory test results, process parameter libraries, customer quality standards, and historical defect libraries. Data is updated per production batch, collected continuously per shift, or synced on a fixed schedule. Daily associated documents include control plans, patrol inspection records, SPC reports, final product inspection reports, nonconforming product review forms, release forms, and quality monthly reports. Core fields include batch identification, inspection item parameters, measured values, qualified judgment results, defect types, and release status. Corresponding units include common production and inspection units such as length, torque, and pressure.
Constraints Imposed by These Characteristics on the Multi-Turn Dialogue and Prompt Engineering Link
Decentralized and frequently updated data sources require multi-turn dialogue to associate context by batch to avoid mixing cross-batch data. Large differences in field formats across multiple document types require prompts to clearly specify extraction rules for different documents, ensuring accurate identification of fields such as inspection items and release status. Continuously collected real-time data must be sorted by timeline during dialogue to assist in identifying abnormal trends. Retrieval of the historical defect library must be bound to the process parameters of the current batch to narrow the retrieval scope and improve review efficiency.
Configuration Settings
These configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current production batch | Ensure multi-turn dialogue retains the inspection and review context for the same batch, avoiding cross-batch data confusion |
messages | Only retain dialogue history for the current batch, no more than 20 turns | Control total context length to avoid processing delays caused by excessive historical data |
variables | Preset three variables: batch number, inspection item list, and customer quality standards | Quickly access category-specific data and reduce repetitive prompt writing work |
| Maximum context length | 800–1200 characters | Adapt to the content length requirement of splicing multiple documents for this category; adjust slightly based on actual measurements |
| Context filtering rules | Only retain real-time inspection data from the last 24 hours | Exclude expired data and focus on the latest inspection results for the current batch |
Three Common Mistakes
- Phenomenon: The batch identifier bound to
chatIddoes not appear in the dialogue log, resulting in mixed cross-batch review context. Reason: ThechatIdwas not associated with the unique identifier of the production batch during configuration, and only a randomly generated session ID was used. - Phenomenon: When a user accessing a login-free link deletes a dialogue, the background log is also cleared, making historical review records untraceable. Reason: Independent archiving configuration for dialogue logs was not enabled, and user sessions and logs were stored together.
- Phenomenon: Inspection data returned by tool calls cannot be automatically brought into the prompt for the next dialogue turn. Reason: No receiving variable for tool output was preset via the
variablesconfiguration, causing context transfer to fail.
How to Verify Successful Configuration
- Launch a simulated batch inspection dialogue, check whether the preset batch identifier is bound in the session log, and confirm that the
chatIdconfiguration is effective. - Call a tool to retrieve inspection data, verify whether the returned results are automatically filled into the preset
variablesvariables, and confirm that context transfer is working normally. - Delete the login-free session, check whether the background log retains the review records for that session, and confirm that the log archiving configuration is correct.
- Adjust the context length parameter, launch a multi-turn dialogue, verify that dialogue processing has no timeouts or truncations, and confirm that the parameters are adapted to current business needs.
The values provided are common starting points and should be measured against the reader's own samples.