What This Category’s Data Looks Like
Data for vehicle intelligent due diligence reports mainly comes from public announcements of vehicle manufacturers, the Ministry of Industry and Information Technology motor vehicle announcement directory, motor vehicle registration system archives, third-party testing institution reports, and second-hand vehicle circulation data. Updates are triggered by new vehicle launches, recall events, and annual inspection cycles, with no fixed schedule. Document structures include fields such as vehicle identification number (VIN), curb weight, wheelbase, power parameters, compliance inspection items, historical maintenance records, and recall records. Units include standard measurement identifiers such as kilograms, millimeters, and kilowatts. Some reports are in scanned PDF format.
Constraints on the Retrieval and Recall Link
Data includes unique identifier fields, requiring balanced weighting between precise field matching and semantic recall during retrieval. Document length varies widely, from short parameter entries to full inspection reports. Adaptive segmentation strategies are needed to prevent semantic fragmentation. Update nodes are not fixed, so incremental synchronization support is required to meet non-fixed cycle update needs. Some documents use scanned formats, so a pre-processing OCR parsing workflow is necessary to extract text content. Multi-dimensional combined retrieval demands that recall results cover multiple types of information to avoid missing key compliance or parameter content.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length span of vehicle due diligence documents, balancing semantic integrity and retrieval accuracy |
chunk_overlap | 100–150 characters | Prevents semantic fragmentation after long document segmentation, retaining context association |
recall_top_k | 8–12 entries | Covers multi-dimensional content such as vehicle parameters, compliance records, and recall information, avoiding redundancy or omission |
similarity_threshold | 0.75–0.85 | Filters low-relevance non-target vehicle documents, accurately matching retrieval needs |
parse_pdf_ocr_enable | true | Processes scanned inspection report PDFs, extracting text content embedded in images |
incremental_sync_mode | Sync by file hash | Avoids repeated parsing and storage for batch-uploaded vehicle documents |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After batch uploading 100 vehicle due diligence documents, nearly half show training exception status. Cause: No reasonable single-batch upload limit is configured, exceeding the system's parallel processing capacity causes some tasks to fail to complete.
- Phenomenon: The error
invalid configuration parameter name "hnsw.max_scan_tuple"is returned during knowledge base search. Cause: A vector database private parameter unsupported by the platform is configured, and official compatible configuration items are not used. - Phenomenon: Scanned motor vehicle inspection reports return empty text results after retrieval. Cause: The
parse_pdf_ocr_enableconfiguration is not enabled, so embedded detection data and parameter information in images cannot be extracted.
How to Verify Successful Configuration
- Upload a single scanned vehicle inspection report, verify that the parsed text contains the detection content embedded in the image, confirming that the OCR configuration takes effect.
- Initiate a retrieval that includes the vehicle's unique identifier field, verify that returned results preferentially match the target vehicle's documents, confirming that the field weight configuration is reasonable.
- Batch upload multiple vehicle due diligence documents, verify that all tasks complete without exception status, confirming that the batch upload configuration adapts to the current scale.
- Initiate multiple rounds of regular retrieval, verify that response speed meets business usage requirements, confirming that segmentation and recall configurations do not occupy excessive system resources.
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.