What data for this category looks like
Daily documents for basic chemical continuous production include operation procedures, process index cards, DCS trends, laboratory reports, interlock bypass records, special work permits, shift logs, and more. Data sources cover DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring, energy metering devices, and raw material tank data platforms. Update frequencies include real-time (such as DCS trends), per shift (such as shift logs and special work permits), and daily (such as laboratory reports). Document structures include structured forms, time-series data, and plain text records. Most fields have clear units, such as temperature (℃), pressure (MPa), and flow rate (m³/h).
What constraints do these characteristics impose on document parsing and chunking
The multi-type, high-frequency update document characteristics of this category create multiple constraints for the parsing and chunking process. First, real-time time-series documents such as DCS trends require retention of data continuity. Chunking must not destroy the integrity of a single time-series interval. Second, structured forms such as interlock bypass records require extraction of field metadata. Parsing must not lose core form information. Third, enterprise intranet data sources have access restrictions. The system must adapt to intranet pull rules. Fourth, large batches of files require adaptation to longer parsing times and reasonable chunk granularity. This avoids parsing interruptions or oversized chunks that harm retrieval efficiency.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density of process index cards and laboratory reports. Avoids splitting critical parameter associations, and balances retrieval accuracy |
chunkSplitter | Paragraph + fixed character boundary | Adapts to time-series blocks of DCS trends and structured content of work permits. Retains logical coherence of text |
customPdfParse | Enable structured parsing mode | Adapts to form field extraction for interlock bypass records and special work permits. Retains metadata such as approver and tag number |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for shift log summary PDFs with 1000+ pages or batch LIMS reports. Avoids mid-process interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Adapts to monthly DCS trend summary files for batch uploads. Meets upload requirements for large-volume documents |
chunkSettingMode | Manual configuration | Adjusts chunking rules separately for shift log data in Excel format. Avoids splitting complete single shift records |
Three common configuration errors
- Intranet Confluence pages return no content after parsing. The cause is failure to configure intranet domain whitelists or proxy access rules. This prevents FastGPT from pulling intranet resources.
- Large-volume PDF or Excel documents fail parsing due to timeout. The cause is
PARSE_FILE_TIMEOUT_SECONDSset lower than actual parsing time. This does not reserve sufficient time for multi-page or multi-row structured content processing. - Chunked data from 10,000-row Excel laboratory reports is too large per chunk. The cause is failure to adjust
chunkSplitterrules for table-based documents. Using the default character-based chunking logic leads to incomplete splits.
How to verify correct configuration
- Upload a typical interlock bypass record PDF. Check if the parsed result retains structured fields such as approver, interlock tag number, and effective time.
- Upload a shift log Excel file with over 1,000 rows. Verify that chunked results do not split complete single shift records.
- Configure an intranet Confluence data source. Test pulling a single page of process procedure. Confirm the parsed result includes core process parameter content from the page.
- In the FastGPT v4.17.0 environment, adjust
chunkSizeto 800 characters. Upload a process index card. Confirm that chunked text does not split critical temperature and pressure parameter combinations.
The values provided are common starting points. They should be evaluated against the reader's own samples.