What this category of data looks like
This category serves change management teams composed of R&D, process, quality, production, and procurement departments at manufacturing enterprises. Daily processed documents include ECN/ECR, change assessment documents, drawings, BOM version records, process verification reports, production switch plans, inventory disposal forms, and customer approval documents. Data sources cover PLM, ERP, MES systems, supplier materials, verification test results, and customer special requirements. Updates occur irregularly, triggered each time a change is initiated. Documents must include core fields such as chain impact descriptions for design, materials, and processes, old and new version switching points, and verification status.
What constraints do these characteristics impose on the "document parsing and chunking" link
Documents in this category have diverse types and complex structures. They include mixed content such as structured tables, scanned drawings, and long-text change descriptions. Document formats exported from different source systems are inconsistent. The parsing link must support multi-format adaptation. Core information in change documents is highly correlated. Chunking operations must retain the context of change clauses and associated verification content. This prevents splitting of business logic. Some documents such as large BOM lists and multi-page process verification reports have large file sizes. The system must adapt to large-file parsing and chunking workflows. The system must also accurately identify key fields such as version numbers and customer approval status. This ensures subsequent retrieval accuracy.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the configuration logic for maximum paragraph depth, matches the typical paragraph length of ECN/ECR documents, and retains complete context for change impact analysis |
chunkSplitter | Split by title + paragraph | Matches the hierarchical structure of change documents, avoids splitting core change clauses |
customPdfParse | Enable OCR mode | Handles non-editable PDF files such as scanned process drawings and verification reports with handwritten annotations |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large BOM lists and multi-page process verification reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates long-duration parsing workflows for complex verification reports |
chunkSettingMode | Manual configuration | Adjusts chunking rules separately for different types of change documents |
Three common mistakes
- Symptom: Parsing results omit tabular dataset content, or the interface cannot select tabular datasets for parsing. Cause: Parsing configuration for structured tables is not enabled, and the default chunking logic does not recognize table fields.
- Symptom: Parsing tasks terminate early and return a timeout prompt. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and parsing time for large process verification reports exceeds the default threshold. - Symptom: Chunked content splits change association logic, making it impossible to retrieve complete change impact analysis paragraphs. Cause: The
chunkSizesetting is too small, splitting continuous content associated with material changes and process adjustments in ECN documents.
How to confirm the configuration is correct
- Upload a typical ECN document. Check if all change clauses and associated BOM version information are included in the parsing results of FastGPT v4.17.0.
- Upload a scanned process drawing. Confirm that the parsing results retain annotations and dimension markings on the drawing.
- Review the uploaded file size limit configuration. Confirm it matches the set value of
UPLOAD_FILE_MAX_SIZE. - Adjust the
chunkSizeparameter. Compare whether the chunked results retain complete context for change impact analysis.
The values provided are common starting points and should be evaluated against the samples applicable to the specific use case.