What This Type of Data Looks Like
Data for intelligent vehicle due diligence reports primarily comes from official public announcements from automakers, Ministry of Industry and Information Technology (MIIT) motor vehicle product announcements, third-party authoritative testing institution reports, and dealer filing information. The update rhythm adjusts with new vehicle launches, annual facelifts, and compliance requirements. The core parameter update frequency is no less than quarterly.
Document structure primarily consists of structured parameter lists, including fields such as Vehicle Identification Number (VIN), curb weight, wheelbase, driving range, powertrain type, after-sales service network distribution, and others. Physical parameters must be labeled with standard units including kilograms, kilometers, milliliters. Date fields use the YYYY-MM-DD format.
Constraints Imposed by These Characteristics on Model Access and Configuration
Structured parameters for vehicle due diligence reports are numerous and tied to fixed units. This requires preserving the correspondence between fields and units during model access, so targeted document parsing rules must be configured. The context length after splicing multi-source data is generally long, so the model context window parameter must be adjusted to avoid truncation of critical information. Data update frequency adjusts with industry trends, so a matching knowledge base synchronization cycle must be configured to ensure the timeliness of retrieved content. In addition, Vehicle Identification Number (VIN) serves as the unique retrieval anchor point, so the model must support accurate matching of document content related to VIN codes.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Individual vehicle due diligence report documents often exceed 3000 characters; splicing multiple documents requires adapting to the model context window to prevent truncation of critical parameters |
RAG_RECALL_TOP_K | Top 8–12 results | Vehicle data has numerous fields, so a sufficient number of parameter documents must be recalled to cover retrieval needs and avoid missing core configuration items |
PARSE_TABLE_ENABLE | Enabled | Vehicle due diligence reports contain a large number of parameter tables; enabling this setting preserves the correspondence between fields and units, improving the accuracy of parameter extraction |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual vehicle testing reports often include high-definition images and multi-page parameter lists; raising the upload limit supports importing complete documents |
SYNC_CRON_EXPRESSION | 0 0 2 * * ? | Automaker compliance data is updated daily; synchronizing daily at 2 AM ensures knowledge base timeliness and aligns with industry data update cycles |
SIMILARITY_THRESHOLD | 0.75–0.85 | Vehicle parameter fields have high similarity; setting a reasonable threshold filters irrelevant documents and improves retrieval accuracy |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After deploying a 70B parameter model, generating vehicle due diligence reports takes too long, and the interface returns a timeout error. Cause: The
maxContextparameter was not adjusted, and too many vehicle documents were included in the context inference, exceeding the model's load limit. - Phenomenon: A query page embedded via iframe fails to display properly in a mini-program, and the console returns a 403 status code. Cause: The cross-origin access whitelist was not configured, and the domain name verification rules for the mini-program environment were not adapted.
- Phenomenon: When using a locally deployed model, fields such as powertrain type and driving range in the generated due diligence report lack units. Cause: The table parsing configuration was not enabled, and unit correspondence information in the document was not preserved, causing the model to fail to correctly map parameters and units.
How to Confirm Proper Configuration
- Upload a vehicle testing report document, check whether the parsing result preserves the correspondence between fields and units in the table, and verify that the
PARSE_TABLE_ENABLEconfiguration is active. - Submit a Vehicle Identification Number as a retrieval keyword, check whether the number of recalled documents meets expectations, and verify that the
RAG_RECALL_TOP_KandSIMILARITY_THRESHOLDconfigurations match the scenario requirements. - Initiate a due diligence report generation request, check whether the response time meets business expectations, and verify that the
maxContextandMODEL_MAX_TOKENSconfigurations adapt to the model's load. - Configure a scheduled synchronization task, check whether the knowledge base updates the latest automaker compliance data according to the preset cycle, and verify that the
SYNC_CRON_EXPRESSIONconfiguration is correct.
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.