What Data Looks Like for This Category
Daily documents for the general mechanical equipment assembly category include assembly process cards, BOMs, torque records, gap adjustment records, commissioning reports, factory inspection sheets, and packing lists. Data sources include PLM/ERP systems, parts inspection data, assembly tool collection terminals, test benches, MES systems, and customer technical agreements. Documents are updated once per device or per order. A single order’s document set includes multiple structured and unstructured files. Some fields use professional units: torque values are measured in newton-meters, gap adjustment values in millimeters. BOMs include associated fields such as material codes, quantities, and suppliers.
What Constraints Do These Characteristics Impose on the Document Parsing and Chunking Link
The multi-variety, order-based assembly nature of this category creates varying document structures for single orders. General chunking rules cannot adapt to all document types. Differences in document formats across multiple sources require the parsing module to have flexible adaptation capabilities, avoiding field loss due to format incompatibility. The high frequency of single-order updates requires the parsing and chunking process to be efficient, reducing repeated processing time. The presence of professional fields and units requires retaining contextual associations during chunking, avoiding splitting that disrupts the logical integrity of processes or inspection items. This differs from part machining categories, which focus primarily on single-dimensional size data.
How to Set the Configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density of assembly process cards and BOMs, avoids splitting that disrupts process or material associations |
chunkSplitter | Split by paragraph + heading | Matches the hierarchical structure of assembly documents, retains complete context of process steps and inspection items |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Covers parsing time for large-volume commissioning reports and test bench logs |
customPdfParse | Enabled | Adapts to encrypted or non-standard PDF formats exported from PLM, retains original field layout |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates full assembly document packages for single orders, including multiple inspection sheets and log files |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different document types (BOM, torque records) to adapt to multi-format input |
Three Common Misconfigurations
- Symptom: Empty result returned after calling the file parsing interface, with split-related errors in logs. Cause: The
customPdfParseconfiguration is not enabled, or thechunkSplitterparameter is configured incorrectly, making it impossible to adapt to non-standard PDF formats exported from PLM. - Symptom: Parsed file content is not passed to the knowledge base after Docker deployment. Cause: The file storage volume for the parsing module is not mounted correctly, or the
UPLOAD_FILE_MAX_SIZEsetting is too small, causing large files to be truncated. - Symptom: Timeout error occurs when parsing large-volume commissioning reports. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting value is lower than the actual parsing time, failing to cover the processing cycle for complex documents.
How to Confirm Configuration Is Valid
In FastGPT v4.17.0, configuration effectiveness can be confirmed through the following actionable steps:
- Upload a typical assembly process card PDF, check whether the parsed text retains core fields such as process numbers and operation requirements, with no obvious split breaks.
- Upload a single 100–200 MB commissioning report, verify that parsing time falls within the preset
PARSE_FILE_TIMEOUT_SECONDSrange, with no timeout errors. - Check whether the complete content of the uploaded document appears in the knowledge base index list, with no missing fields or format confusion.
- Compare chunking results for different document types (BOM, torque records) to confirm that the chunking rules adapt to the current business’s document structure.
The values provided here are common starting points and should be evaluated against the reader’s own samples.