Data Characteristics of This Category
Commercial vehicle intelligent due diligence report data sources include vehicle registration certificates, road transport permits, annual maintenance ledgers, second-hand vehicle sales contracts, GPS operation logs, and more. Update frequency varies by document type: maintenance ledgers update with each maintenance operation, operation logs sync daily, and sales contracts are static documents generated once. Most documents are multi-page PDFs or structured tables mixed with unstructured notes. Fields include VIN, total mass, axle count, operation mileage (unit: km), annual inspection validity period, freight qualification number, and others. Some documents include trailer matching parameters and fuel consumption data.
Constraints Imposed on Document Parsing and Chunking
Commercial vehicle due diligence documents are often hundreds-page long ledgers, with mixed structured tables and unstructured notes. This places requirements on parsing capabilities for long files. Some fields such as VIN and freight qualification number have strong industry uniqueness. Parsing must accurately match field boundaries to avoid misalignment. Fields including operation mileage and total mass have fixed units. Chunking must retain the binding between units and numerical values to prevent separation after splitting. Document formats vary widely across sources. Parsing logic must adapt to different document layouts to avoid consistency issues across documents.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Commercial vehicle maintenance ledgers often reach hundreds of pages. The default 300-second timeout is insufficient for parsing. 600 seconds covers most long-document parsing needs |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single commercial vehicle operation log PDF may exceed 500 MB. Sufficient space must be reserved to support large file uploads |
chunk_size | 800–1200 characters | Fields and notes in commercial vehicle documents are mostly short paragraphs. This range retains the association between fields and context, avoiding overly fragmented or overly long chunks |
chunk_overlap | 100–150 characters | Table rows and notes in long ledgers have contextual associations. Overlapping characters preserve information continuity across chunks |
PARSE_ENGINE | pdf-marker | Commercial vehicle documents often contain complex tables and layouts. pdf-marker better preserves the hierarchical structure of structured content |
RETRY_PARSE_TIMES | 2 times | Large file parsing occasionally encounters engine errors. Retrying 2 times reduces the parsing failure rate |
The parameter values provided on this page are conventional recommendations used as a starting point 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 settings.
Three Common Misconfigurations
- Phenomenon: When calling pdf-marker to parse long commercial vehicle documents, tasks remain in a waiting state for a long time and eventually return a timeout error. Cause: No parsing task queuing mechanism is configured. When multiple large files are processed simultaneously by a single engine, resource exhaustion causes timeouts.
- Phenomenon: Calling doc2x to parse a multi-page commercial vehicle maintenance ledger PDF may return an error, while parsing small-page documents works normally. Such situations vary widely. It is recommended to confirm based on statistics or testing with your own samples. Cause: doc2x has an implicit limit on the maximum number of pages per file. Commercial vehicle documents exceed this threshold, triggering errors.
- Phenomenon: After locally deploying pdf-marker version 4.9.0, the call page returns the error Cannot read properties of undefined (readi). Cause: The local deployment configuration of pdf-marker fails to correctly associate with the FastGPT API address, or dependency package version incompatibility causes field reading failures.
How to Verify Proper Configuration
- Upload a 100-page commercial vehicle maintenance ledger PDF, check the parsing task duration, and confirm the duration does not exceed the configured
PARSE_FILE_TIMEOUT_SECONDSvalue. - Review the parsed chunk results, verify that core fields such as VIN and total mass are fully bound to their units, and that no separation of numerical values and units occurs after splitting.
- Upload a commercial vehicle operation log file larger than 500 MB, confirm that uploads are not restricted by
UPLOAD_FILE_MAX_SIZE, and that parsing tasks start normally. - Submit multiple large files for simultaneous parsing, confirm that tasks execute in queue order, and that no direct timeouts occur.
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.