What Data Looks Like for This Use Case
Data sources for PCB manufacturing include Gerber files, drill files, BOMs, CAM analysis files provided by customers, drilling and lamination parameters from production equipment, bath solution test records, and AOI, electrical testing, and laboratory slice reports. Data update frequency follows per production batch, per shift, or continuous collection. Daily documents include engineering production instructions, Gerber analysis records, drilling and lamination parameter tables, chemical analysis reports, AOI defect reports, electrical testing qualification reports, and slice inspection reports. Document fields include aperture unit (mil or mm), lamination temperature (℃), defect coordinates, etching compensation values, and other professional parameters.
Constraints on Model Access and Configuration
PCB manufacturing data includes multiple types of structured and semi-structured files. It requires real-time or batch updates. The model access link must adapt to multi-format data parsing. The accuracy of professional terms such as hole metallization and etching compensation directly affects the validity of process documents. The model must maintain professional matching. The data volume per batch is large. Sufficient context capacity must be configured to accommodate complete production parameters and abnormal records. This avoids process errors caused by missing information. The accuracy of abnormal reports is directly linked to product scrap risk. The randomness of generated content must be strictly controlled.
How to Set Configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to batch process documents and abnormal report data for PCB manufacturing. It accommodates complete context information |
maxTokens | 2000–3000 token | Meets the requirements for complete drafting output of a single abnormal report or process document |
temperature | 0.1–0.3 | Maintains accuracy of professional terms and parameters. It reduces randomness of generated content |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed proxy address | Adapts to local or cloud-based model invocation links |
AIPROXY_API_TOKEN | Bind the authorization credential for the corresponding model | Completes identity verification for model access |
| Workflow/API tool selection | auto | Automatically triggers document extraction tools to process structured data such as Gerber analysis records and AOI reports |
Three Common Configuration Mistakes
- Incorrect configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. Connection timeout or 401 status code returns during invocation. The cause is incorrect proxy address or authorization credential filling. It does not match the invocation link of the corresponding model. - Setting
maxTokensto above 4000 token. Prolonged unresponsive lag occurs during invocation. The cause is that the model's generated content exceeds the computing power bearing threshold, leading to processing delay. - Configuring
toolChoiceasnone. The text content extraction node cannot automatically parse Gerber analysis records. This causes missing key parameters in the generated process documents. The cause is that automatic tool call adaptation for structured data is not enabled.
How to Verify Successful Configuration
- Upload a local Gerber analysis record file. Trigger model invocation. Check whether the returned process document contains correct drilling aperture and lamination parameter fields.
- Submit an AOI report. Check whether the generated abnormal report contains the defect location and type in the report. Verify field matching accuracy.
- View the model invocation log. Confirm that the request return status code for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENis 200. - Adjust the
temperatureparameter. Compare the contents of the two generated process documents. Confirm that the change range meets expectations.
The values given are common starting points and should be measured against the reader's own samples.