What the data for this category looks like
Core data for this category is generated around the VIN of a single complete vehicle. Data sources include ERP/MES systems, VIN and configuration systems, tightening tools, EOL inspection lines, and quality traceability systems. Daily documents cover production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, repair records, and consistency release documents. Data is generated in real time per vehicle, then summarized and archived per shift. Document structures include structured fields such as VIN, torque value, and part batch number, plus long-text process descriptions. Some documents contain embedded tables and watermark information.
What constraints do these characteristics impose on document parsing and chunking
The data characteristics of this category create multiple constraints for document parsing and chunking. First, full-process documents for a single complete vehicle can total hundreds of pages. Large files require adjusted upload and parsing time limits. Second, data uses VIN as the unique identifier. Chunking must preserve the association between VIN and corresponding process and inspection data to avoid mixing information across vehicles. Third, real-time generated tightening data and offline inspection reports require fast parsing. Chunking rules must balance reasonable splitting of short fields and long texts. Fourth, mixed-model production leads to differing document structures across vehicle types. Custom parsing rules must be supported to adapt to work instruction formats for different models.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Vehicle final assembly documents include short fields and long process descriptions. This range balances per-chunk information completeness and retrieval efficiency |
chunkSplitter | Split by title + paragraph | Final assembly documents have clear workstation and process titles. This splitting method preserves the integrity of operational logic |
customPdfParse | Enabled | Final assembly PDFs include embedded tightening data tables and VIN watermarks. Custom parsing can extract structured core fields |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Full-process inspection reports for a single complete vehicle can reach hundreds of pages, requiring support for large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Parsing large PDF files requires longer processing time to prevent task timeout interruptions |
chunkSettingMode | Custom mode | Work instruction structures vary significantly across different vehicle models. Custom mode adapts to specialized scenarios |
Three Common Configuration Mistakes
- Scenario: When deploying FastGPT v4.17.0 locally, parsing large PDF files returns a timeout error. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default timeout duration is insufficient to parse hundreds of pages of inspection reports. - Scenario: After integrating pdf-marker, parsing tasks remain in a waiting state with no results for an extended period. Cause: No file parsing queuing mechanism was configured. Concurrent multiple tasks exceed the system's processing limit.
- Scenario: Calling doc2x to parse a hundreds-of-page PDF returns an error, but small files parse normally. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters were not adjusted. Large files trigger parsing size limits or timeout errors.
How to Confirm Configuration is Correct
- Upload the full-process inspection report PDF for a single complete vehicle, confirm the upload progress bar completes normally, and no size limit prompt appears.
- Review the parsed text chunks, confirm that core fields such as VIN and torque value are fully extracted and not incorrectly split.
- Submit parsing tasks for multiple large files, confirm tasks execute in sequence, and no concurrent timeout errors occur.
- Enter the FastGPT backend configuration page, verify that parameters such as
chunkSizeandPARSE_FILE_TIMEOUT_SECONDSmatch the preset configuration.
The values listed are common starting points and should be evaluated against the corresponding operational samples.