What the data for this category looks like
Business data for the pulp and paper category originates primarily from preparation, pulping, papermaking, quality, and energy departments. Daily documents include stock preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, and sheet break and grade change records. Data sources cover wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, laboratory systems, energy and water treatment systems. Update frequencies fall into three tiers: real-time, shift-based, and per-roll. This category is defined by high-speed continuous web production. Most documents relate to stock properties, paper machine operating conditions, quality metrics, and sheet break events. Fields mostly include numeric operating parameters, batch identifiers, and timestamps. Units include tons, meters, grams per square meter, seconds, and others.
What Constraints These Characteristics Impose on Document Parsing and Chunking
High-speed continuous web production means documents such as paper machine operation logs and online quality curves contain frequently updated real-time segments. Chunking must avoid splitting continuous operating time-series data. Heterogeneous documents from multiple sources—such as tabular stock preparation sheets and curve-based quality reports—require parsing logic that adapts to different format field extraction. This prevents loss of key inspection and maintenance-related information like stock ratios and grammage. Based on update rhythms by batch, shift, and roll, chunking must retain contextual links. It must not split stock preparation and beating records from the same batch into different chunks. Sheet break and grade change records have strong contextual ties. Chunking must retain operating data before and after events to support complete context for maintenance Q&A.
The following configurations are based on FastGPT v4.17.0.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the length of single-segment operating data from paper machine operation logs and online quality curves, avoids splitting continuous time-series information |
chunkSplitter | By paragraph + semantic boundary | Adapts to structured paragraphs in stock preparation sheets and beating records, and semantically linked segments in quality curves, retains full logical integrity of documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing durations for single-roll paper machine operation logs and batch laboratory reports, prevents parsing timeouts for large documents |
customPdfParse | Enabled | Adapts to PDF format parsing for online quality curves, retains curve coordinates and numerical labels, prevents loss of key inspection data after parsing |
chunkSettingMode | Manual configuration | Adjusts chunking rules separately for different document types (such as stock preparation sheet tables and sheet break record text), adapts to category-specific document structures |
Three Common Configuration Mistakes
- Duplicate chunks are automatically deleted after custom chunking is applied, leading to disordered index order. This occurs when the system’s default document deduplication logic is not disabled, or when custom chunk retention mode is not enabled in
chunkSettingMode. - Timeout errors trigger when parsing large laboratory reports, with status prompts related to
PARSE_FILE_TIMEOUT_SECONDS. This happens when the configured timeout duration is insufficient to match the parsing time required for a single large document. - After uploading structured tabular documents such as stock preparation sheets, formatted table data cannot be returned via the API, only plain text fragments. This occurs when parsing optimization configurations for tabular documents are not enabled, leading to incomplete field extraction.
How to Verify the Configuration Is Correct
- Upload a typical paper machine operation log PDF. Check if the parsed chunks retain continuous operating time-series segments, with no obvious split paragraphs.
- Upload multiple stock preparation sheets and beating records from the same batch. Verify that chunks aggregate related document content logically, with no cross-batch chunks.
- Call the API to upload a single stock preparation sheet document. Check if the returned parsing status field correctly matches parsing in progress, ready, or failed statuses.
- Upload a PDF document containing online quality curves. Check if the parsed text includes key inspection information such as curve coordinates and numerical values, with no missing fields.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.