What Data for This Category Looks Like
The data used for automotive service intelligent due diligence reports comes from three main sources: offline store maintenance systems, second-hand vehicle trading platform archives, and official manufacturer quality inspection documents. Update cycles vary by source. Maintenance records are generated in real time with each service. Annual summary reports are archived quarterly. Most documents follow fixed templates, with standard fields including vehicle identification number (VIN), maintenance item details, replacement part models, driving mileage, customer identity information, and others. Mileage is always measured in kilometers. Part information often includes original equipment manufacturer (OEM) numbers. Some reports mix text and scanned image formats.
Constraints for Parsing and Chunking
Mixed multi-source formats, fixed template structures, and varying document lengths create multiple constraints for the document parsing and chunking process. Mixed text and scanned image formats require the parsing module to support both native text extraction and OCR recognition. This prevents missing maintenance records stored in images. Fixed field templates require preserving field associations during chunking. Do not arbitrarily split consecutive paragraphs that span VIN codes or driving mileage values. Splitting these breaks data correspondence. Single-service reports and quarterly summary reports have significant length differences. Chunking strategies must adapt to different document lengths to avoid over-chunking long reports or creating redundant chunks for short reports. Documents in different export formats need unified parsing rules. This ensures consistent extraction of fields such as part models and customer information.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_OCR_ENABLE | Enabled | Automotive service reports often include scanned maintenance record images, requiring OCR to extract text content from images |
CHUNK_MAX_SIZE | 800–1200 characters | Automotive service reports contain long paragraphs of maintenance details. This range preserves field associations while avoiding over-chunking |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Quarterly summary reports have large content volumes, requiring sufficient parsing time to avoid mid-process interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Annual archived full-store maintenance summary reports have large file sizes, requiring support for large-capacity file uploads |
SIMILARITY_THRESHOLD | 0.75–0.85 | This range is needed to distinguish similar content from different maintenance records of the same vehicle model, avoiding recall of irrelevant passages |
RECALL_TOP_N | Top 8 entries | Automotive service reports have strong field associations. A small number of precise recalls can cover the core information required for due diligence |
The parameter values provided on this page are common recommended starting points for configuration setup. 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 Errors
- Phenomenon: Scanned automotive maintenance reports have empty fields or garbled extracted text after parsing. Cause: The
PARSE_OCR_ENABLEconfiguration is not enabled. Only native text extraction is used, which cannot recognize maintenance records in images. - Phenomenon: Multiple store maintenance reports with identical names are uploaded, but search results cannot distinguish VIN codes for corresponding vehicles. Cause: Document metadata extraction configuration is not enabled. No unique identifier is bound to each file, so chunked content lacks source markers.
- Phenomenon: Entering a specific part model keyword fails to return matching chunked content in the knowledge base search. Cause:
CHUNK_MAX_SIZEis set too small. Long paragraphs containing part models are split into multiple unrelated small chunks, preventing the keyword from matching the full context.
How to Confirm Proper Configuration
- Upload a scanned maintenance report. Confirm parsed results include driving mileage and part models from the image. This verifies the
PARSE_OCR_ENABLEconfiguration is enabled. - Upload multiple due diligence reports for different vehicles. Confirm chunked content includes each report’s unique VIN code or file name identifier. This verifies document metadata extraction configuration is enabled.
- Enter a specific maintenance project keyword. Test that recalled chunks cover complete associated fields. Adjust chunk length and similarity threshold to align with business requirements.
- Upload a quarterly summary report. Wait for parsing to complete, then review the task status. Confirm no parsing timeout-related errors occurred.
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.