What the data for this category looks like
Technical documents for the pulp and paper industry cover wood pulp and waste paper batch ledgers, DCS/QCS system data, on-machine measurement records, laboratory test reports, energy and water treatment system reports, and more. Daily documents include pulp preparation sheets, beating records, paper machine operation logs, online quality curves, sheet break and product change records, and more. Update frequencies fall into three categories: real-time, per shift, and per roll. Document structures include structured fields such as pulp ratio, beating degree, grammage, and moisture content, time-series data, tables, and text embedded in images. Most fields have dedicated associated units.
Constraints on document parsing and chunking
Real-time updated online quality curves require chunking to preserve time-series continuity, to avoid splitting that breaks data associations. Batch documents updated per shift or per roll need to adapt to varying content lengths. Chunking must not be too small to increase indexing load, nor too large to lose business details. Combined multiple fields from structured pulp preparation sheets must retain their corresponding relationships during parsing. Otherwise, logical verification for subsequent cross-review cannot be supported. Large-volume paper machine historical records require longer parsing time. Resource usage of the chunking and parsing process must be controlled to avoid timeouts or parsing failures.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density and business logic association length of pulp preparation sheets and paper machine operation records |
chunkSplitter | By paragraph + semantic boundary | Preserves time-series and business context of online quality curves and sheet break records |
customPdfParse | Enabled | Accurately extracts embedded tables and image text from laboratory reports and quality curves |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large files such as single-roll paper machine records |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates batch uploads of historical documents from multiple shifts |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types (pulp preparation sheets / quality curves) |
Three common misconfigurations
- Symptom: After upgrading to FastGPT v4.17.0, uploading pulp preparation sheet CSV files throws an error, prompting abnormal chunking parameters. Cause: Default chunking parameters from older versions do not adapt to the field density of pulp and paper industry documents. Post-upgrade default parameter changes cause mismatches.
- Symptom: When attempting to connect a minerU document parsing node, parsing results fail to match the field format of pulp and paper industry documents. Cause: The node configuration does not adapt to the structured field rules of pulp and paper industry documents, and the general parsing template is used.
- Symptom: When calling the knowledge base file collection creation API, not specifying PDF parsing parameters causes embedded table fields to be lost. Cause: Corresponding parsing parameters are not configured in the API request, and the system's default simplified parsing rules are used.
How to verify correct configuration
- Upload a single pulp preparation sheet CSV file, check if parsed chunks fully retain the corresponding relationships between fields such as pulp ratio and beating degree.
- Upload a PDF file containing time-series quality curves, check if chunks preserve the time axis and numerical associations of the curves, with no content split breaks.
- Call the FastGPT v4.17.0 knowledge base file collection creation API, pass a test document and specify the
customPdfParseparameter, verify that returned chunked results include embedded table content. - Upload batch historical paper machine records within 1000 MB, check that parsing tasks do not throw timeout errors and chunk counts meet expectations.
The values provided here are common starting points, and should be evaluated against the actual samples in use.