What this category's data looks like
Data sources for this category include Gerber files, drill files, BOM lists provided by customers, as well as engineering documents generated by CAM, production equipment parameters, tank solution test results, AOI inspection reports, electrical testing reports, and laboratory slicing reports. Data is collected continuously alongside production batches and shifts. Update frequency is dynamically adjusted based on process links during a single production cycle. Daily documents include production instructions, drilling and lamination parameter records, chemical analysis ledgers, and more. Fields cover aperture, copper thickness, etching compensation value, inspection points, and other relevant metrics. Units include millimeters, ounces, seconds, and other standard units closely tied to PCB manufacturing processes.
What constraints these characteristics impose on HTTP interfaces and external systems
PCB manufacturing data sources are scattered, and update rhythms shift dynamically with production batches. This requires HTTP interfaces to support multi-source data pulling and on-demand triggering, and adapt to deployment scenarios where internal networks cannot access public networks. Process document fields contain specialized parameters, so interfaces must support field-specific filtering to reduce redundant data transmission. Continuously collected real-time data requires interfaces to have stable scheduled pulling capabilities, while also supporting non-standard format data exported by different devices to lower adaptation costs for external system docking. In addition, process data accuracy directly impacts production quality, so interfaces must include request verification and failure retry mechanisms to ensure reliable data transmission.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Request timeout | 600 seconds | Adapt to delays from multi-source data pulling in internal network environments, avoid interrupting valid requests early. This configuration can be adjusted directly on the interface settings page in FastGPT v4.17.0 |
| Authentication configuration | Custom secret key for internal deployment | Adapt to deployment scenarios that cannot access public networks. Pass the secret key via the Authorization header to avoid security risks associated with public network authentication |
| Return field filtering | Only retain production instructions, drilling parameters, and AOI inspection results | Reduce invalid data transmission, focus on core content of process specifications and technical standards. Field filtering is implemented via the responseData parameter |
| Number of items returned per request | Top 5 | Single PCB process document has relatively long content, limiting the number of items avoids context overflow, and meets information density requirements for Q&A scenarios |
| Variable parameter configuration | Bind production batch number and equipment type fields | Accurately match process data for the current production batch via the variables parameter, improving retrieval accuracy |
| Database access whitelist | Add the internal network IP of the FastGPT deployment server | Avoid database connection errors, ensure data access permissions in internal network environments |
Three common configuration mistakes
- Interface returns 304 status code and fails to obtain the latest process data: When the front-end deployment instance switches model channels, authentication parameters are not updated. The browser caches old interface responses, resulting in returned content that is not updated.
- Database connection error "Access denied for user": The internal network IP of the FastGPT deployment server is not added to the PostgreSQL or MongoDB access whitelist, or the configured account is not granted read and write permissions for the corresponding database.
- Retrieval results do not match expectations: The production batch number or equipment type in the
variablesparameter is not bound correctly, causing the interface to fail to match process documents of the corresponding batch and return redundant or invalid data.
How to verify successful configuration
- Initiate a test request, check whether the returned
responseDatacontains preset core process fields, and verify whether the field filtering configuration takes effect. - Simulate a request in an internal network environment, check whether the interface can respond normally, and verify whether the authentication configuration adapts to internal network deployment scenarios.
- Adjust the production batch number or equipment type in the
variablesparameter, check whether the returned process data corresponds to the adjusted parameters, and verify whether the variable binding configuration takes effect. - View database connection logs, confirm that account permissions and IP whitelist configurations are correct, and verify whether the database docking configuration is correct.
The values provided here are common starting points and should be evaluated against the reader's own samples.