What the data for this category looks like
The core data for this category comes from dyeing and finishing workshops, fabric enterprises' dyeing, setting, laboratory and quality departments. Daily documents include dyeing formulas, chemical weighing records, dye vat curves, sample confirmation reports, color difference reports, setting process sheets, sewage ledgers, and more. Data sources cover customer color samples, formula libraries, dye and chemical batches, dye vat control equipment, colorimeters, setting machines, and environmental monitoring systems. Update frequency varies with production batches; new documents are generated per vat, per batch, and per shift. Most documents contain structured parameters, curve data, and qualitative judgment content. Fields often have clear units such as temperature, concentration, batch number, and emission limits.
What constraints these characteristics impose on document parsing and chunking
The mixed structure of structured parameters and curve data in documents for this category requires chunking to retain the contextual association between parameters and their corresponding curves. This prevents loss of the ability to restore complete process logic after splitting. Fields with clear units must have parameters and units bound during parsing, to avoid incorrect identification of process parameters after separation. High-frequency batch documents need fast parsing workflows to avoid timeouts that disrupt production schedules. Format differences from exports across multiple devices require parsing logic to support diverse document structures, while ensuring consistent parsing of data collected across devices.
How to set configurations
The following configurations are formulated based on the parameter rules of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the combined length of process parameters and curve data in dyeing and finishing documents, avoiding loss of contextual association after splitting |
chunkSplitter | Split by paragraph + table | Matches the mixed text paragraphs and process table structures in documents, preserving the integrity of data within tables |
customPdfParse | Enable MinerU adaptation mode | Adapts to curve charts and structured parameters in PDF documents, improving parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports upload requirements for large-volume documents such as single-batch dye vat curves |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for complex process documents, avoiding parsing failures due to timeouts |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different types of dyeing and finishing documents, adapting to diverse document structures |
Three common mistakes
- Symptom: Some process tables in uploaded knowledge base documents cannot be chunked correctly, with chunking results retaining only single table rows or scattered cells. Cause:
chunkSplitteris not configured to a table-first splitting mode. The default chunking logic prioritizes splitting by character length, which breaks the structural integrity of tables. - Symptom: Some docx-format dye and chemical records or sample confirmation documents cannot be parsed, showing a parsing failure status after upload. Cause: The parsing plugin for office documents is not enabled, or the complex embedded styles of the document exceed the basic parsing capabilities.
- Symptom: When enabling
customPdfParsefor MinerU adaptation, Docker deployment environments prompt out-of-memory GPU errors or fail to recognize GPU hardware. Cause: GPU mapping parameters are not configured in the Docker startup command, or the CUDA version does not match the MinerU adaptation version, causing the parsing service to fail to call hardware acceleration.
How to confirm configurations are correct
- Upload a single typical dye vat curve document, and check if the parsed chunking results retain complete temperature-time curve paragraphs and corresponding parameters.
- Upload documents in different formats, and confirm that process sheets in docx and PDF formats can be parsed normally, with no field loss or format disorder.
- Adjust the
chunkSizeparameter, compare the contextual integrity of chunking results, and confirm that the parameter adjustment matches the document structure characteristics. - Check the logs of the Docker-deployed parsing service, confirm that the GPU hardware is properly recognized, with no out-of-memory GPU error reports.
The values provided are common starting points and should be measured against the reader's own samples.