This page is compatible with FastGPT v4.17.0.
What This Category's Data Looks Like
This category's data comes from engineering, production, quality control, and laboratory teams at printed circuit board factories. Daily documents include engineering production instructions, Gerber analysis records, drilling and lamination parameters, chemical solution analysis reports, AOI inspection reports, electrical test reports, and sectioning reports. Data sources include customer-provided Gerber files, drill tapes, BOM lists, CAM files, equipment operating parameters, bath solution test results, AOI and electrical test data, and laboratory test records. Data is updated per production batch, per shift, with some equipment data collected continuously.
What Constraints These Characteristics Impose on Model Access and Configuration
This category has diverse and inconsistently formatted data, including multiple types of unstructured content such as engineering documents, equipment parameters, and inspection reports. This requires model access configurations to support context stitching for multi-source documents. Data is updated frequently, with some streams collected continuously. Configurations must align with data update rhythms to avoid scenarios where the context window cannot accommodate the latest batch data. There are many specialized fields such as drilling and lamination parameters and bath solution test indicators. Configurations must balance the rigor required for professional Q&A while supporting associated queries across multiple documents, ensuring that output results can be traced back to corresponding business documents.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the deployment address plus the /v1 suffix | Most equipment data interfaces for PCB manufacturing use standard API formats; adding /v1 aligns with most model access specifications |
AIPROXY_API_TOKEN | Fill in the exclusive key assigned by the platform | Call permissions must be verified to prevent unauthorized access to business data |
temperature | 0.2–0.4 | Equipment inspection and maintenance Q&A requires precision and accuracy to avoid generating irrelevant content |
maxContext | 8000–12000 token | Must accommodate associated content from multiple production batch documents and inspection reports to cover context requirements for single queries |
quoteMaxToken | 2000–3000 characters | Must fully extract key business segments such as drilling parameters and AOI anomaly records to ensure accurate traceability for Q&A |
| Workflow/API tool selection | auto | Must automatically select whether to call the document retrieval tool based on queries to adapt to multi-source data query needs |
Three Common Configuration Mistakes
- Issue: Test returns status code 404 when configuring the Ollama channel. Cause: The
/v1suffix was not added to the end of the API address, resulting in a mismatched interface path. - Issue: Modifying OneAPI configuration has no effect in FastGPT v4.8.20 and later versions. Cause: Parameters were not updated in the model configuration module of the platform page, and static configuration files in docker compose were still relied upon.
- Issue: After uploading business documents, the large model prompts that null was not uploaded, but the files can be downloaded normally. Cause: File metadata was not correctly associated during document parsing, resulting in the model being unable to read the uploaded file's identification information when called.
How to Confirm the Configuration Is Successful
- Call the model test interface, enter a professional question related to equipment inspection, and check whether the returned results include traceability information for the corresponding business documents.
- View platform logs to confirm that the API request address includes the correct
/v1suffix with no path errors. - Upload a test drilling parameter document, trigger model Q&A, and check whether key parameter content in the document can be correctly extracted.
- Adjust the
temperatureparameter and retest, observe changes in the rigor of returned results to confirm the parameter takes effect.
The values provided are common starting points and should be measured against the reader's own samples.