What this category’s data looks like
Data sources for automotive service intelligent due diligence reports include archived information from vehicle registration authorities, work orders uploaded by offline maintenance shops, vehicle condition inspection reports from online second-hand car trading platforms, and claim ledgers from insurance institutions.
Update rhythms fall into three categories: basic vehicle archived information is updated monthly, maintenance and claim data is updated in real time as work orders are submitted, and second-hand car inspection reports are updated alongside transaction milestones.
The document structure is split into two parts: structured fields and unstructured attachments. Structured fields include VIN identification code, driving mileage (unit: kilometers), annual inspection validity period, number of claims, and maintenance item list. Unstructured attachments include maintenance photos and scanned copies of claim settlement documents.
What constraints these characteristics impose on deployment and upgrade
Multi-source heterogeneous data sources require cross-system field alignment configuration during deployment to avoid chaotic data entry.
Mixed real-time and scheduled update rhythms require layered synchronization scheduling rules. These rules distinguish between scheduled pulls of basic archived information and real-time pushes of maintenance work orders.
Fixed units for structured fields require unit validation logic during deployment to prevent incorrect numerical entries.
The variety of sizes and formats of unstructured attachments require pre-configured upload file size limits and supported parsing formats to avoid parsing failures.
During the upgrade phase, old field formats of existing data sources must be compatible. Dynamic mapping for new maintenance classifications must also be supported to prevent parsing errors in existing data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Maintenance photos and scanned claim settlement documents for automotive service due diligence reports typically do not exceed 500 MB per file. Reserved redundancy is required for total size of batch uploads |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing unstructured attachments requires processing multi-page scanned documents. A longer timeout period prevents interruptions during large file parsing |
maxContext | 8000–12000 characters | After concatenating structured fields and unstructured summaries of automotive due diligence reports, the total length typically does not exceed 10000 characters, which adapts to model input limits |
recall count | Top 8 entries | Associated data for automotive due diligence reports (such as historical records with the same VIN) typically does not exceed 10 entries. Excessive recall increases resource usage |
similarity threshold | 0.75–0.85 | High thresholds are required for exact VIN code matching to avoid mistakenly recalling data for similar but non-identical vehicles |
INITIAL_ROOT_PASSWORD | Custom strong password based on business scenario | Local deployment requires securing background management permissions to prevent leakage of default initial passwords |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Mistakes
- Symptom: An "Initial password verification failed" prompt appears when logging into the background management interface, and initial configuration cannot be completed. Cause: The
INITIAL_ROOT_PASSWORDparameter was not set correctly during deployment configuration, or the default initial password was used, resulting in permission verification failure. - Symptom: After deploying a 32B-level large model, when concurrent access exceeds the set threshold, a "CUDA out of memory" error occurs, or inference response times out. Cause: Resource allocation parameters were not adjusted based on concurrent access volume and model video memory usage, and sufficient inference buffer space was not reserved.
- Symptom: After configuring a local large model service connection, the knowledge base fails to recall valid content, or returns empty results. Cause: The model service address and port parameters were not filled correctly, resulting in a failure to connect to the model service.
How to Confirm Configuration is Complete
- Upload automotive maintenance photos and claim settlement documents that comply with the
UPLOAD_FILE_MAX_SIZEconfiguration, and check that the upload and parsing processes have no errors. - Simulate access requests at the set concurrent volume, observe server video memory and CPU usage metrics, and confirm that resource usage meets expectations.
- Input test data with a known VIN code, verify the number and similarity of recall results, and confirm that they match the configured recall count and similarity threshold.
- Log into the background management interface using the custom initial password, confirm that the permission configuration takes effect, and that knowledge base and model management functions work normally.
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.