What This Category's Data Entails
Business data for SMT placement and electronic assembly includes customer-provided BOM and coordinate files, line change lists, stencil and reflow oven temperature records, first article confirmation reports, SPI/AOI inspection reports, maintenance records, and traceability labels. Data sources include customer-submitted BOMs, MES systems, solder paste and component batch data, and real-time collected data from placement machines, SPI/AOI equipment, and reflow oven temperature monitoring systems. Data is updated per work order and each line change. Fields include part numbers, reference designators, component batches, reflow oven temperature parameters, and more. Units include millimeters, degrees Celsius, batch numbers, and more.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
In FastGPT v4.17.0, this category has high data update frequency. Multi-turn dialogue must link to the latest data of the current work order, and avoid using old data to generate responses. Business documents have complex structures and clear fields, so prompts must limit Q&A scope to exclusive information of the current work order such as part numbers, reference designators, and maintenance records, to prevent the model from generating irrelevant content. Data is tightly bound to production workflows, so multi-turn dialogue must track identifiers such as current equipment model and work order ID, to ensure each round of Q&A matches the current production scenario.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current work order | Ensure multi-turn dialogue is linked to all business data of the same work order, avoiding cross-workorder confusion |
messages | Retain the first 10 dialogue context entries | Match the high-frequency updated data per work order and line change, avoiding context overload that affects model processing efficiency |
variables | Inject current work order BOM, line change list, and reflow oven temperature records | Align with exclusive data of the current production scenario, ensuring Q&A content matches the current work order |
| Maximum context length | 8000 tokens, adjustable range based on actual testing | Limit total context length to avoid exceeding model processing thresholds |
| Question Optimization | Enabled | Organize user inspection and maintenance questions, extract core fields such as part numbers and reference designators, and generate recommended questions that fit the business scenario |
Three Common Misconfigurations
- Empty content or error messages are returned after calling the dialogue node, with the front end displaying blank results. Cause: Current work order exclusive business data such as BOM and line change list are not injected into
variables, so the model cannot obtain valid information to generate a response. - No response or error is returned after sending a dialogue record deletion request. Cause: The
chatIdparameter is not passed correctly, so the system cannot locate the target dialogue record, causing the deletion operation to fail. - Unable to read content when attempting to use Feishu online documents as a data source. Cause: Document access permissions or correct document links are not configured in
variables, so the model cannot obtain business data from the document.
How to Verify Proper Configuration
- Submit a device inspection-related question, check that returned content includes information such as the current work order's part number and reference designator, to confirm
variablesinjection is successful. - Initiate two consecutive dialogue sessions, check that context retains work order information from the previous dialogue, to confirm the
messagesconfiguration is correct. - Send a dialogue record deletion request, check that the system returns an operation success prompt, to confirm the
chatIdparameter configuration is correct. - After enabling the guess-you-want-to-ask function, check if the dialogue interface generates recommended questions that fit the current production scenario, to confirm question optimization is enabled.
The values provided are common starting points and should be measured against the reader's own samples.