What Data for This Category Looks Like
Daily documents for this category include pulp preparation sheets, beating records, paper machine operation logs, laboratory reports, sheet break and product change records, and similar items. Data sources include wood pulp and waste paper batch ledgers, DCS/QCS systems, paper machine online measurement equipment, laboratory test results, and energy and water treatment systems. Data update frequencies cover three dimensions: real-time, per shift, and per roll. Most documents are structured tables and recorded text with professional parameters. Fields include material identifiers, operating parameters, and test values, with units matching the metering standards of the corresponding production links.
What Constraints These Characteristics Impose on Document Parsing and Chunking
The data characteristics of this category impose three constraints on document parsing and chunking. First, the span of data update frequencies is large. Real-time paper machine operation logs need to adapt to high-frequency parsing requirements, while documents generated per shift or per roll need to support batch processing. Second, documents are mainly structured tables and text with professional parameters. When chunking, table row and column associations must be retained, and complete business records split across pages must be avoided. Third, fields and units vary significantly across different documents. Adaptation is needed for the structures of different document types such as pulp preparation sheets and laboratory reports, to avoid losing business-related context, while meeting the requirement of integrating multi-source production data for bidding documents.
Configuration Settings
The following configurations are based on FastGPT v4.17.0, adapted to the document parsing needs of the pulp and paper category.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
customPdfParse | Enabled | Adapt to structured PDF documents for pulp and paper, such as operation logs with tables and laboratory reports. Enhanced parsing capabilities are required to extract complete table structures |
chunkSize | 800–1200 characters | Match the length of single-segment business parameters for documents in this category, avoid splitting complete test records or pulp preparation plans across pages |
chunkSplitter | Split by paragraph + table boundaries | Adapt to the mixed format of structured tables and paragraphs in pulp and paper documents, retain context associations between tables and related business text |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handle batch parsing of per-shift or per-roll documents, avoid parsing timeout caused by large single-document data volume |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types such as operation logs and laboratory reports, to adapt to differentiated business field structures |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Support batch upload of large-volume documents such as multi-roll paper machine operation logs or monthly pulp preparation ledgers |
Three Common Misconfigurations
- Symptom: After enabling enhanced PDF parsing, calling the MinerU interface fails, returning connection timeout or 404 error. Cause: The Docker port for locally deployed MinerU is not properly mapped, or the MinerU API address configured in FastGPT does not match the actual deployment address.
- Symptom: Parsed table data fields are empty or formatted incorrectly, making it impossible to extract complete parameters from pulp preparation sheets or paper machine operation logs. Cause: The
customPdfParseconfiguration is not enabled. The default parsing engine cannot accurately recognize structured table and professional parameter formats in pulp and paper scenarios. - Symptom: Some files fail to parse when uploading multiple documents in batch, returning a file upload limit error. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration is not adjusted, exceeding the default file upload limit, making it impossible to process large-volume monthly ledgers or multi-roll operation logs.
How to Verify Correct Configuration
- Log in to the knowledge base settings page of FastGPT v4.17.0, confirm that the enhanced PDF parsing option is checked, and the MinerU API address is filled with the locally deployed Docker container IP and port.
- Upload a typical paper machine operation log PDF, wait for parsing to complete, and check the extracted table content to confirm that the row and column structure is complete and no fields are missing.
- Manually adjust
chunkSizeto the 800-character and 1200-character ranges, upload a pulp preparation sheet document, and check whether the chunking results retain complete pulp preparation parameter combinations. - Upload a single monthly pulp preparation ledger document with a volume of approximately 800 MB, confirm that no timeout error is triggered during the parsing process.
The values provided here are common starting points and should be evaluated against the reader's own samples.