What data for this category looks like
Source data for this category comes from client-provided Gerber files, drill files, BOMs, as well as CAM system data, production equipment parameters, bath solution test results, AOI inspection reports, electrical testing reports, and laboratory slice reports. Data is updated by production batch, per shift, or generated via continuous collection. Daily documents include engineering production instructions, drilling lamination parameter records, chemical analysis ledgers, and other related materials. Data fields cover graphic layer information, hole position parameters, chemical solution concentration, defect types and inspection results, and more. There is no unified fixed field order or format, and data is dynamically generated based on production stages.
Constraints for model access and configuration
This category of data is multi-source heterogeneous with inconsistent formats. Model access must support parsing multiple types of heterogeneous documents. A sufficiently large context window must be configured to accommodate batch production data. Data is updated by production batch or continuously, and the volume of data processed in a single run is large. Interface call frequency must be controlled to avoid triggering QPS limits. Professional fields such as hole copper thickness and lamination pressure must be accurately identified. Model output must maintain a stable format to adapt to ledger summary logic, and must also be compatible with raw data formats exported by different manufacturer equipment.
How to set configurations
The following model access configuration logic is compatible with FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Accommodates batch context for multi-source PCB production documents, and stores all ledger and inspection data for a single production batch |
maxTokens | 4000–6000 token | Limits the length of single-round output to avoid parsing failures caused by overly long summary reports |
temperature | 0.2–0.4 | Reduces random output probability to ensure stability of professional terminology and ledger format |
| Workflow/API tool selection | auto | Automatically selects tool calls for structured data extraction, adapting to field extraction across multiple types of production documents |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the service provider | Ensures interface calls point to the correct model service node |
AIPROXY_API_TOKEN | Fill in the key generated by the service provider | Completes identity verification for interface calls to ensure access permissions |
Three common configuration errors
- Interface calls frequently trigger QPS limit error messages. The cause is failure to adjust call frequency based on the batch update rhythm of production data, with the volume of data processed in a single run exceeding the interface's carrying capacity.
- The Markdown format of large model output is unstable, with chaotic heading levels or code block format errors. The cause is that the temperature parameter is set too high, leading to excessive output randomness that does not meet the fixed format requirements of the ledger.
- Unable to call the specified third-party model, with a key verification failure prompt. The cause is incorrect configuration of
AIPROXY_API_TOKEN, or failure to complete key binding verification for the third-party model.
How to verify a successful configuration
- Initiate a single test data call to confirm that the interface returns a normal status code.
- Check that the ledger fields in the model output correspond to the fields in the original production documents, with no omissions or errors.
- Initiate multiple consecutive calls to confirm that the output format remains stable with no obvious random deviations.
- Verify that the interface key and endpoint configuration information matches the content provided by the service provider.
The values provided here are common starting points, and should be evaluated against relevant samples.