What Data for This Category Looks Like
Daily documents for lithium-ion cell manufacturing include batching records, coating and rolling parameters, winding or lamination records, liquid injection records, formation and grading data, and OCV and safety test reports. Data sources cover raw material batch ledgers, MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory testing systems. Data updates are synchronized in real time per battery batch and per device. Document structures include structured parameter tables, long curve text, and short field labels. Professional fields include slurry solid content, rolling thickness, dew point temperature, OCV voltage, and other content with dedicated units.
What Constraints Do These Characteristics Impose on the Document Parsing and Chunking Link?
The multi-source heterogeneous document structure, high-frequency real-time updates, and specialized field properties of this category impose multiple constraints on the document parsing and chunking process. Documents that mix long text curves and short parameters must retain semantic context to avoid splitting critical parameter associations. Real-time synchronized batch data must be chunked by production batch boundaries to prevent cross-batch data confusion. Specialized fields with dedicated units must be fully preserved to avoid separating units from values after parsing. Test reports with embedded charts must support structured extraction to ensure complete retention of parameter text associated with charts.
How to Configure Settings
The following configuration items apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the complete content of a single set of coating/rolling parameters plus associated batch number, avoiding splitting critical parameter associations |
chunkSplitter | Chunk by paragraph + semantics | Adapts to the document structure that mixes long curve text and short fields, preserving semantic integrity between parameters and context |
customPdfParse | Enabled | Supports extraction of embedded charts and structured tables from OCV test reports, preventing loss of visual data |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Adapts to parsing 100+ page formation and grading data PDFs, preventing timeout truncation of large files |
chunkSettingMode | Custom mode | Allows separate configuration of chunk boundaries for batch fields, adapting to the differentiated structure of multi-source heterogeneous documents |
Three Common Misconfigurations
- Issue: After uploading a coating parameters PDF, the parsed result does not display the embedded rolling parameter charts. Cause: The
customPdfParseconfiguration is not enabled. The default parsing only extracts plain text and does not retain embedded visual data. - Issue: After batch uploading batching record Excel files, the slurry solid content field for some batches appears empty. Cause: Chunking was not performed by batch boundaries, splitting the association between cross-row raw material batch numbers and parameters, causing the parsing tool to fail to match the corresponding fields.
- Issue: Unable to switch third-party document parsing tools, and custom parsing configurations do not take effect. Cause: The associated parameters for
customPdfParsewere not configured correctly. The built-in parsing engine is the only one enabled by default.
How to Verify a Correct Configuration
- Upload an OCV test report PDF with embedded charts, check if the parsed result includes the parameter text and label content corresponding to the charts.
- Upload a single coating and rolling parameter Excel file, verify that the parsed chunks include the complete combination of a single set of rolling thickness, operating speed, and corresponding batch number.
- Submit a formation and grading data file with more than 100 pages, confirm that the parsing task does not return a timeout error.
- Switch
chunkSettingModeto custom mode, adjustchunkSize, then verify that the chunked text does not split critical parameter pairs.
The values provided are common starting points and should be measured against the reader's own samples.