What the Data for This Category Looks Like
The data for auto parts intelligent due diligence reports primarily comes from supplier qualification files, supply chain BOM ledgers, third-party quality inspection reports, and industry compliance certification documents. The data update rhythm is adjusted according to business scenarios: supplier qualifications are updated annually, BOM ledgers are updated monthly, and batch inspection reports are updated in real time alongside production batches. Most documents are structured tables or formatted PDF files. Core fields include OE part numbers, batch numbers, material specifications, inspection parameters, and compliance certification numbers. Units mostly follow industrial standard units such as pieces, kilograms, hours, and megapascals.
What Constraints These Characteristics Impose on Model Access and Configuration
The multi-source structured nature of auto parts due diligence data requires models to accurately identify specific fields such as OE part numbers and batch numbers, to avoid confusing part data of the same model but different batches. The characteristics of long documents and multi-field combinations require the model context window to adapt to large lengths, while also configuring reasonable recall rules to associate scattered data sources. Real-time or high-frequency updated data requires the model access configuration to support regular refreshing of data sources, to avoid using expired supplier or inspection data. In addition, some compliance certification files have encryption or specific formats, so corresponding file parsing parameters need to be configured to ensure complete content extraction.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Auto parts due diligence reports often include multiple BOM lists and batch inspection reports. A single structured document can reach thousands of characters in length, so long context processing must be supported |
UPLOAD_FILE_MAX_SIZE | 500 MB | Single supplier qualification packages and annual supply chain ledgers often include multiple scanned documents and detailed tables, so bulk upload of large files must be supported |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large BOM lists need to traverse hundreds of part data entries, so sufficient time must be reserved for the parsing process to avoid mid-process interruptions |
recall_count | Top 8–12 entries | Auto parts due diligence requires associating multiple types of data sources such as supplier qualifications, inspection data, and compliance documents. A sufficient number of relevant contents must be recalled to support complete analysis |
similarity_threshold | 0.72–0.78 | It is necessary to distinguish part data of the same model but different batches, to avoid including low-match irrelevant documents in the context |
classify_model | Calibrated based on actual testing | It is necessary to adapt to the classification logic of the auto parts category, to ensure that the classify module can correctly identify the type and source of due diligence data |
The parameter values provided on this page are all common recommended starting points for configuration. The actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Misconfigurations
- When calling an LLM, the session ID is not passed, resulting in multiple due diligence requests for the same part failing to associate historical context. The interface displays an empty session context, and the
session_idfield is absent from the logs. The cause is that the session ID passing parameter is not configured in the model call interface. - When using the deepseek-r1 671b model, forged reference IDs may appear. The generated due diligence report contains non-existent part numbers or inspection report numbers, and the log returns the
invalid_reference_iderror code. The cause is that the model did not correctly align the field IDs in the data source, and the reference verification configuration was not enabled. - When accessing a large model privately deployed on Volcano Cloud, connection failures may occur. The model test interface displays an error, returning the
connection_refusedstatus code. The cause is that the correct private model API address and authentication key were not configured, and local port access permissions were not opened.
How to Confirm Configuration Is Complete
- Upload a standard auto parts BOM list document, trigger a model call, check whether running logs include the
session_idfield, and verify normal passage of the session ID. - Generate a complete due diligence report, verify that part numbers and inspection report numbers referenced in the report match uploaded data sources, and confirm absence of invalid references.
- Configure access parameters for the Volcano Cloud private large model, click the test connection button, and confirm return of the
connection_successstatus code. - Edit the classify node of the workflow, check whether the model dropdown menu includes the accessed large model, upload a compliance certification file to trigger node operation, and confirm correct identification of the file type.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.