What data looks like for this use case
Data sources for this use case include ERP/MES systems, VIN and vehicle configuration systems, supplier batch management systems, tightening tool terminals, EOL inspection lines, and quality traceability systems. Updates are generated in real time per individual vehicle and synced in batches per shift. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, and compliance release documents. Document fields include VIN codes, part numbers, torque parameters, inspection results, and some documents use digital signatures or encrypted formats.
Constraints on Document Parsing and Chunking
Each individual vehicle requires matching hundreds of configuration and part details. Document chunks must retain associated fields such as VIN codes and part numbers to avoid splitting critical associated information, which would prevent subsequent Q&A from linking vehicles to their corresponding assembly requirements. Frequently updated real-time documents must support batch upload scenarios, so single-file parsing duration must be controlled to avoid production cycle disruptions from parsing timeouts. Encrypted PDFs with digital signatures and structured tightening data tables must support parsing of non-standard formats and structured content to ensure core parameters can be extracted. Document batch differences from mixed-line production require chunks to retain contextual associations to avoid cross-batch data confusion that would impact quality traceability.
Configuration Guidelines
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the field density of assembly work instructions and inspection reports, retains complete contextual information for single-station operations |
chunkSplitter | Split by paragraph and VIN keywords | Avoids splitting the association between VIN codes and their corresponding assembly requirements, adapts to the document structure of mixed-line production |
customPdfParse | Enable structured parsing mode | Adapts to encrypted PDFs with digital signatures and tightening data tables, extracts structured fields |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Covers parsing duration for large-volume offline inspection reports, avoids timeout failures |
chunkSettingMode | Custom mode | Adapts to chunking rules grouped by VIN, meets the associated data requirements for complete vehicle final assembly |
Three Common Configuration Errors
- Symptom: After uploading a PDF document with a digital signature, the parsing result is empty or fails to identify core content. Cause: The encrypted PDF parsing mode under
customPdfParseis not enabled. The default parsing logic cannot handle signed encrypted formats. - Symptom: The knowledge base reading link returned by the API displays a prompt that only .txt and .m format files are supported after being clicked. Cause: The uploaded document failed format validation, or an exception occurred during the format conversion stage of the parsing process.
- Symptom: The uploaded document parses normally, but the large model-generated response does not include image content from the document. Cause: The embedded parsing configuration for document images is not enabled, or contextual information associated with images was not retained during chunking.
How to Verify Correct Configuration
- Upload an assembly work instruction with a digital signature, check whether the parsing result includes core fields such as VIN codes and torque parameters.
- Upload a large-volume offline inspection report, confirm that the parsing task does not trigger a timeout error.
- After configuring the chunking rules, randomly select a document and check whether the chunking result retains the associated information between VIN codes and their corresponding assembly requirements.
- Enable the image parsing configuration, upload a document containing assembly diagrams, and confirm that the parsing result includes text descriptions associated with the images.
The values provided here are common starting points and should be evaluated against the reader's own samples.