What Data Looks Like for This Category
PCB manufacturing quality traceability data covers multiple types of professional documents. Sources include customer Gerber files, drill files, BOM lists, CAM engineering files, equipment operating parameters, tank solution test records, AOI inspection reports, electrical testing reports, and section analysis reports. Data update frequencies cover scenarios such as per production batch, real-time collection per shift, and continuous monitoring. Document structures include standardized parameter fields such as drilling depth, lamination temperature, and hole copper thickness, with corresponding units such as millimeters, degrees Celsius, and microns.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
PCB manufacturing quality traceability data has multiple types, updates frequently, and includes professional parameters. This requires retrieval to cover multi-source heterogeneous documents while accurately matching professional fields. High-frequency updates at the production batch level require retrieval to support near-real-time document synchronization. Because the consequences of errors are severe, recall results must avoid mixing irrelevant documents, and must associate multiple types of detection data from the same batch. This ensures that abnormal sources can be quickly located during traceability.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precision requirement for PCB professional parameters, and prevents irrelevant non-production documents from mixing into retrieval results |
limit | Top 8 entries | Covers multiple types of associated documents for a single production batch, while controlling memory usage from context concatenation |
usingReRank | Enabled | Performs secondary sorting on homogeneous lamination and drilling parameter documents, and prioritizes recently updated content |
searchMode | embedding | Adapts to semantic matching for multiple types of professional documents, and accurately matches AOI defect descriptions and engineering production instructions |
datasetSearchUsingExtensionQuery | Enabled | Supplements context related to production batches, and associates cross-document information such as customer BOMs and drilling parameters |
Three Common Configuration Mistakes
- Homogeneous recent documents in retrieval results are not sorted by date. For example, a newly uploaded lamination parameter document appears after older documents. Cause: The secondary sorting logic of
usingReRankis not enabled. FastGPT does not sort documents of the same type by update date by default. - FastGPT deployed via Docker cannot upload knowledge base files. The interface displays "File upload failed". Cause: The file storage volume is not mounted correctly, or the read-write permission of the temporary upload directory for the FastGPT container is not configured.
- Server memory usage spikes after calling the knowledge base retrieval interface, and crashes at fixed times each day. Cause: The value of
limitis not restricted. Retrieving too many documents in a single call causes excessive memory usage from vector database queries and context concatenation, and a reasonable resource recycling strategy is not configured.
How to Confirm Correct Configuration
- Upload three PCB production documents of the same topic. For example, two lamination parameter records and one drilling parameter record. Trigger a retrieval query for "lamination abnormality". Confirm that the most recently uploaded lamination parameter document appears at the top of the retrieval results.
- Call the knowledge base retrieval interface of FastGPT v4.17.0. Pass a query term that includes "hole copper thickness abnormality". Check that the number of returned results matches the value set for
limit. - View container resource monitoring logs. Confirm that the memory usage of a single retrieval does not exceed the reserved memory threshold of the server.
- Upload documents in two formats: Gerber files and CSV-format tank solution test data. Confirm that both can be imported into the knowledge base normally.
The values provided here are common starting points. They should be evaluated against samples specific to the actual deployment.