What this category’s data looks like
Business data for SMT mounting and electronic assembly comes from BOMs, Gerber files, and component coordinate tables provided by customers. It also includes real-time collected data from MES systems, mounting machines, SPI/AOI testing equipment, and reflow ovens. Daily documents cover work order-level BOMs, line change lists, stencil parameter records, furnace temperature curve reports, first article confirmation sheets, defect repair records, and traceability labels. Data updates in real time per work order and each line change. Fields include part number, reference designator, component batch number, furnace temperature set and measured values, coordinate offset, and more. Units include millimeters, degrees Celsius, batch numbers, and other standard units.
What constraints these characteristics impose on model access and configuration
This category’s data updates in real time per work order and line change. A single process Q&A must link multiple short-cycle documents, so context coverage must be limited during model access to avoid interference from redundant data. The data includes multi-format content such as structured tables, coordinate files, and temperature logs. Model configuration must adapt to parsing logic for multiple document types. Process Q&A requires accurate reference to original document fragments, without unsupported inference. Parameters must be configured to lock extraction rules for original text references. Line changes and work order switches occur frequently. Multi-turn conversations must bind the current work order context to avoid cross-work order data confusion. Model call frequency must also be controlled to match the real-time collection rhythm of equipment data.
How to set configurations
This document is compatible with FastGPT v4.17.0. The following configuration items are formulated based on business characteristics:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple documents such as BOMs, line change lists, and furnace temperature reports within a single work order, to avoid context overflow |
maxTokens | 2000–3000 token | Meets full response requirements for defect analysis and parameter adjustment in process Q&A, to avoid truncation of critical instructions |
quoteMaxToken | 1500–2000 characters | Limits the length of a single referenced original document fragment, ensuring referenced content in responses is complete and does not occupy excessive context |
temperature | 0.1–0.3 | Process procedure Q&A requires accurate matching of standard parameters. A lower temperature value reduces random model output and avoids incorrect inference |
AIPROXY_API_ENDPOINT | Fill in the actual interface address of the deployment node | Adapts to locally deployed FastGPT nodes to ensure correct routing for model calls |
AIPROXY_API_TOKEN | Fill in the exclusive token generated by the platform | Verifies identity legitimacy of model calls to avoid unauthorized access |
Three common configuration errors
- Phenomenon: After adding a model in a locally deployed FastGPT, tests return a 403 status code (no body). Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, leading to identity verification failure or incorrect routing. - Phenomenon: When attempting to access the GPT-5 model, no corresponding option appears in the channel configuration interface, making access impossible. Cause: A third-party compatible interface is not configured via a custom
AIPROXY_API_ENDPOINT, and reliance on built-in platform channel options prevents adaptation. - Phenomenon: After calling MySQL business data, responses do not include database original text fragments. Cause: The
quoteMaxTokenparameter is not configured, or the parameter value does not meet document fragment length requirements, so complete original content cannot be extracted and returned.
How to confirm successful configuration
- Perform a process Q&A test for a single work order, check whether the response includes the specified original document fragments, and confirm that the
quoteMaxTokenvalue meets the length requirements of the current document. - Initiate multiple consecutive rounds of Q&A, check whether the model can link historical data of the current work order, and confirm that the
maxContextrange is suitable for the current business scenario. - Check model call logs, confirm that there are no format errors in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and no identity verification failure errors. - Adjust the
temperatureparameter and initiate a defect analysis Q&A, check the consistency of responses, and confirm that the parameter value meets the accuracy requirements for process Q&A.
The values provided are common starting points and should be measured against the reader's own samples.