What the industry’s data looks like
Quality traceability-related data for the pulp and paper industry typically comes from wood pulp and waste paper batches, DCS/QCS systems, on-machine measuring equipment, laboratories, energy and water treatment systems. Daily documents include pulp preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, sheet break and product change records. Update frequencies include real-time, per shift, and per roll. Data fields include pulp ratio, grammage, moisture content, thickness, and more. Some documents include time-series coordinates or roll number identifiers.
What constraints do these characteristics impose on the document parsing and chunking step
Document types from multiple sources and formats require the parsing module to support structured tables, time-series records, and embedded charts. It must avoid losing key anchor points such as roll numbers and batch numbers. Documents updated in real time or at high frequency must be chunked by time slices or document boundaries to prevent cross-batch data mixing that reduces traceability accuracy. A single document may contain quality data for one roll or multiple rolls of paper. Chunking rules must match the document’s pagination or header/footer markings. This ensures the contextual relevance of each chunk aligns with quality analysis logic.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the information density of structured tables and time-series records in pulp and paper documents, and avoids including cross-batch data in a single chunk |
chunkSplitter | Split by paragraph + header/footer | Matches the pagination logic of pulp preparation sheets and paper machine operation logs, and retains contextual anchor points such as roll numbers and batch numbers |
customPdfParse | Enabled | Adapts to parsing embedded charts in online quality curves and laboratory report PDFs, and retains original numerical coordinates |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the parsing time of single large paper machine operation logs, and prevents timeout interruptions |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different document types such as pulp preparation sheets and sheet break records, to support multi-format document scenarios |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the maximum file size limit for single-roll paper machine operation logs, and supports uploading large documents |
Three common mistakes
- Uploaded PDF parsing results in empty numerical fields such as pulp ratio and grammage. The cause is that
customPdfParseis not enabled. The built-in default parser cannot extract raw numerical data from embedded quality curves. - Timeout errors occur when parsing a single paper machine operation log larger than 500 MB. The cause is that
PARSE_FILE_TIMEOUT_SECONDSis not adjusted. The default timeout threshold is insufficient to cover the parsing time of large documents. - Chunking results include quality data for different roll numbers. The cause is that the roll number in the header is not used as a split anchor point. Configuring
chunkSplitterto split by fixed characters leads to mixed context.
How to verify proper configuration
- Upload a single pulp preparation sheet PDF, and check whether key fields such as batch number and pulp ratio are retained after parsing.
- Upload a single 500 MB paper machine operation log, and confirm that no timeout error occurs during the parsing process.
- Review the chunking results, and confirm that each chunk only contains quality data for a single roll of paper or content from the same batch of a single document.
- When testing a call node, the uploaded file can be directly passed to the large model context without going through the system's default text parsing process.
The values listed above are common starting points and should be evaluated against the reader's own samples.