What Data for This Category Looks Like
Business data for complete vehicle final assembly typically comes from ERP, MES systems, VIN and configuration systems, tightening tools, EOL inspection lines, and quality traceability systems. Daily documents include production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, repair records, and consistency release documents. Data updates occur per vehicle, in real time, or per shift. Documents use VIN codes as the single vehicle main line, and include configuration fields, torque values, inspection item numbers, part batch numbers, and other fields. Units include newton meters, millimeters, qualification markers, and similar units.
What Constraints These Characteristics Impose on Document Parsing and Chunking
The VIN code as the single vehicle main line requires avoiding mixing data across vehicles during chunking. Mixing data will cause matching errors when generating check and maintenance Q&A. Structured fields across multiple document types, such as torque values and inspection items, must retain field associations. Random splitting will break parameter logic. For batch data updated in real time or per shift, limit single chunk length to avoid context overflow, which impacts subsequent retrieval matching. Additionally, format differences across documents—such as the table structure of tightening data and multi-page association in EOL reports—require chunking rules to adapt to non-general document structures. General template chunking logic cannot meet the parsing needs of this category.
How to Configure Settings
This configuration is built for FastGPT v4.17.0, and adapts to document parsing requirements for complete vehicle final assembly scenarios.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of single-station operation paragraphs in assembly work instructions, prevents splitting associated torque parameters |
chunkSplitter | Split by paragraph + VIN marker | Matches single vehicle main line data, prevents mixed chunking of cross-VIN configuration information |
customPdfParse | Enable structured field extraction | Retains numerical fields and corresponding units from tightening data and inspection reports, avoids losing key parameters with plain text parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading batch offline inspection reports and VIN lists for per-shift single batches |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for large VIN lists and multiple batches of repair records |
chunkSettingMode | Manual configuration | Adjusts chunking rules for non-general structures of vehicle assembly documents, adapts to VIN association logic |
Three Common Misconfigurations
- An API call to upload images for parsing returns a parameter error. The cause is failing to use multipart/form-data format to transfer file fields, and only submitting the file path in JSON format.
- Parsed document chunks include cross-VIN mixed data. The cause is failing to configure
chunkSplitterwith VIN markers, resulting in failure to distinguish single vehicle main line information during chunking. - Parsed documents for a specified VIN cannot be retrieved. The cause is failing to correctly configure chunk storage rules, resulting in split document chunks not being stored in the corresponding retrieval index.
How to Verify Proper Configuration
- Upload a single assembly work instruction, and check if the chunking result retains complete single-station operation steps and supporting torque parameters.
- Upload a batch VIN list, confirm that each parsed data block is associated with a unique VIN code, with no cross-vehicle data mixing.
- View the parsing log, confirm that no errors related to
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSare triggered. - Call the retrieval interface for a specified VIN, confirm that the chunked results of the corresponding assembly document can be obtained, with no retrieval failures.
The values provided are common starting points and should be evaluated against the reader's own samples.