What data looks like for this use case
PCB manufacturing scenario data sources include customer Gerber files, drill files, BOM files, CAM engineering files, equipment operating parameters, bath solution test records, AOI inspection reports, electrical test reports, and laboratory slice reports. Data update frequency follows each production batch, per shift, or continuous collection. Daily documents include engineering production instructions, Gerber analysis records, drilling and lamination parameters, chemical analysis reports, and more. Fields include aperture, copper thickness, layer count, offset, pass rate, and others. Common units are millimeters, ounces, percentage, and similar.
Constraints Imposed on Model Access and Configuration
Multi-source heterogeneous data formats require model access to adapt to multiple document types: binary files, structured tables, text reports, and more. Corresponding format parsing rules must be configured. The high-frequency data update rhythm requires retrieval and indexing links to match production cycles. This prevents retrieval failures caused by index lag. Complex document structures and long text content require a larger context window. This ensures the model can cover the complete process chain and abnormal records. Review results directly link to production scrap and claim risks. This demands rigor and consistency in model calls. The randomness of generation parameters 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 | PCB manufacturing documents contain multi-layer Gerber parameters and multiple test reports. A long context can cover the complete process chain |
maxTokens | 2000–3000 token | Cross-review requires complete abnormal analysis output, to avoid truncation of key conclusions |
quoteMaxToken | 1500–2000 characters | Complete drilling parameters and AOI abnormal records must be referenced as review basis |
temperature | 0.1–0.3 | Technical review requires rigor, reduce randomness to ensure consistent conclusions |
AIPROXY_API_ENDPOINT | Fill in the corresponding address according to the deployment environment | Adapt to local or cloud model access requirements |
AIPROXY_API_TOKEN | Generate a key according to authorization | Ensure the security of model calls |
Three Common Misconfigurations
- Phenomenon: Knowledge base retrieval responses time out. Logs show retrieval duration exceeds the preset threshold. Cause: No suitable index refresh frequency is configured for high-frequency updated PCB manufacturing data. A large volume of unindexed production data participates in real-time retrieval as a result.
- Phenomenon: Authentication failure occurs when accessing a distilled DeepSeek model. Cause:
AIPROXY_API_ENDPOINTis not set to the dedicated access address for the distilled model. The general large model access endpoint is still used. - Phenomenon: Parsing results are empty after uploading Gerber or AOI reports, with no obvious abnormal logs. Cause: The parsing plugin for the corresponding document format is not enabled, or the document type is not linked to the corresponding parsing rule.
How to Confirm Configurations Are Correct
- Run a retrieval test on a single PCB process document. Verify that the returned reference content covers the core parameters of the document. Adjust
quoteMaxTokento cover complete reference fragments. - Upload multiple PCB documents in different formats: Gerber files, AOI reports, bath solution test records, and others. Confirm that parsing results include corresponding fields, and that format parsing rules are properly configured.
- Trigger a model call test. Check that the returned review conclusion aligns with process logic. Adjust
temperatureto meet review rigor requirements. - Review API call logs. Confirm there are no spelling errors in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and no authentication failure records.
The values provided are common starting points and should be evaluated against samples used in actual deployment.