What the data for this use case looks like
Data for vehicle intelligent due diligence reports comes from official automaker announcements, dealer inventory ledgers, third-party test institution reports, and second-hand vehicle transaction transfer records. Update frequency varies by source: new vehicle announcements are updated monthly, inventory ledgers are updated weekly, and test reports are updated immediately upon test completion. Documents are mostly multi-page mixed formats, including structured vehicle configuration tables, long-form compliance descriptions, and on-site vehicle photos and test images. Core fields include VIN code, vehicle model, emission standard, cruising range (unit: km), battery capacity (unit: kWh), transaction price (unit: ten thousand yuan). Some reports include exclusive fields such as battery cycle count and vehicle identification number check digit.
What constraints do these characteristics impose on the document parsing and chunking link
Multi-source mixed document formats require the parsing process to adapt to structured tables, long text, and embedded images simultaneously, to prevent information loss from single parsing rules. VIN code as the unique vehicle identifier requires chunking to use it as a boundary, to avoid mixing parameter information across vehicles. Documents from different sources have unit differences, such as cruising range marked in both km and miles, so unit unification must be completed during parsing. Embedded test images must be bound to corresponding text blocks, to avoid separating image descriptions from their entities. Long-form compliance descriptions have fixed formats, so chunking must retain contextual logic to ensure key information is not truncated.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunk_mode | entity_based | Vehicle due diligence reports take vehicles as core entities, group and chunk by VIN code to avoid mixing information across vehicles |
max_chunk_size | 800–1200 characters | Adapt to the average length of vehicle parameter tables and descriptive text, retain contextual association logic |
parse_image_in_doc | enabled | Extract alt text or OCR content of on-site vehicle photos and test report images in due diligence reports, associate them with corresponding text blocks |
chunk_overlap | 100–150 characters | Ensure contextual cohesion of long-form compliance descriptions, avoid losing key logic after chunking |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapt to large test reports containing multi-page images and structured tables, avoid parsing timeouts |
enable_table_parse | enabled | Extract structured table data such as vehicle configurations and transaction prices in due diligence reports for precise chunking |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Chunking results include mixed vehicle parameters across multiple VIN codes. Cause: The
chunk_modeparameter is not set toentity_based, and default character-based chunking leads to contextual breaks. - Phenomenon: When users click to copy parsed text blocks, a prompt displays "Unable to use browser automatic copy, please manually copy the content below". Cause: Cross-origin resource sharing rules are not configured, and the browser same-origin policy blocks automatic copy requests.
- Phenomenon: The knowledge base backend shows reports with images have been uploaded, but the model cannot call associated image information during question answering. Cause: The
parse_image_in_docconfiguration is not enabled, and image-associated text or OCR content is not extracted.
How to confirm the configuration is correct
- Users upload a single vehicle due diligence report, review the parsed chunk list, and confirm that each chunk only contains vehicle information corresponding to a single VIN code.
- Users initiate a knowledge base question answering request, enter a query including image descriptions, and confirm that the model can associate image content in the document to generate a response.
- Users upload a single large test report with more than 50 pages, check the execution status of the parsing task, and confirm that no timeout error is triggered.
- Users click the copy button for chunk content, and confirm that the automatic copy function triggers normally, with no same-origin policy related prompts.
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.