What the data for this category looks like
Data related to PCB manufacturing process specifications and technical standards comes from customer-provided Gerber files, drill files, BOM lists, CAM-generated engineering documents, production equipment parameters, bath solution test records, AOI inspection reports, electrical testing reports, and slice analysis reports. Data updates are completed with production batch progression, per-shift output, or continuous device collection. Daily documents include engineering production instructions, process parameter records for each production step, and quality inspection reports. These contain specific fields and corresponding units such as drilling speed, lamination temperature, chemical solution concentration, defect coordinates, and on-resistance.
What constraints do these characteristics impose on model access and configuration
The multi-source heterogeneous data characteristics of PCB manufacturing impose multiple constraints on model access and configuration. Data includes binary engineering files, structured process parameter tables, and unstructured quality inspection reports. This requires configurations to support multimodal data parsing and cross-document association. High-frequency batch-level updates require adaptation to real-time or near-real-time context synchronization logic to avoid mixing process data from different production batches. Single-batch data scale expands as PCB layer count increases. This requires limiting the total context accessed per request to prevent model inference timeouts. Strong field correlation between process parameters and quality inspection results requires prioritizing accurate matching of context references.
How to set the configurations
For FastGPT v4.17.0, the following configuration items can be adapted specifically for PCB manufacturing scenarios:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_TOKEN | Application-specific key | PCB manufacturing process data contains core production parameters. Using an application-specific key restricts access scope, which meets data security requirements. |
maxContext | 8000–12000 token | Single-batch PCB process data has a large scale. This setting accommodates context from multiple associated documents and avoids truncation of critical parameters. |
quoteMaxToken | 1500–2000 token | This setting ensures that each referenced process parameter or quality inspection record is fully displayed, avoiding truncation of critical values. |
temperature | 0.1–0.3 | Process specifications and technical standards Q&A require strict matching of predefined parameters. Excessively high randomness is not appropriate. |
vision | Enabled | Documents such as Gerber analysis records and AOI reports contain charts. Supporting visual parsing improves information extraction accuracy. |
maxTokens | 2000–3000 token | Responses need to cover complete process steps or troubleshooting solutions, avoiding output truncation. |
Three common configuration errors
- A 403 permission error occurs when calling the API. Cause: A universal key is used for PCB process data Q&A scenarios, and an application-specific key is not used to restrict access scope. This does not meet data security requirements.
- The knowledge base fails to parse chart content in Gerber or AOI reports. Cause: The
visionconfiguration item is not enabled, so multimodal data processing capability is not activated, and key information in the charts cannot be extracted. - Process parameters returned by the model do not match actual documents, and referenced fragments are incomplete. Cause:
maxContextandquoteMaxTokenare not properly configured, leading to total context exceeding the limit or referenced fragments being truncated.
How to confirm the configuration is correct
- Pass a sample of single-batch PCB process data to call the test API, and check if returned results include correct process parameters and corresponding document reference sources.
- Upload a Gerber analysis report or AOI inspection report containing charts, verify that the model can correctly identify and extract key values from them, and confirm that the multimodal configuration is active.
- Check API call logs to confirm that the configured
AIPROXY_API_TOKENis used, with no permission error or parameter anomaly prompts. - Adjust the
temperatureparameter, check if detail changes of the answer meet expectations, and confirm that configuration parameters are effective.
The values provided are common starting points and should be evaluated against the reader's own samples.