This document is based on FastGPT v4.17.0.
What the data for this category looks like
Business data for cement clinker and grinding comes from online composition analysis systems, DCS control systems, laboratories, kiln and grinding equipment, raw fuel ledgers, and energy metering devices. Document types include raw material batching plans, kiln operation records, test reports, quality control notices, equipment inspection records, and energy reports. Update frequencies cover real-time, shift-based, and daily. Documents contain proportional fields (such as limestone ratio %), temperature fields (such as kiln cylinder temperature ℃), flow fields (such as feed rate t/h), and multi-column tabular composition test data. Document structures vary significantly across types.
What constraints these characteristics impose on document parsing and chunking
Cross-system data from multiple sources and widely varying document structures require the parsing pipeline to adapt to documents with different update frequencies. Real-time equipment inspection records require finer-grained chunking to ensure retrieval timeliness. Daily or monthly energy reports can use slightly larger chunks to reduce retrieval overhead. Multi-column tabular test data requires the parsing pipeline to support complete table structure recognition to avoid missing fields. Differences in hierarchical structures across document types require chunking logic to match natural boundaries between headings and paragraphs, avoiding truncation of critical business information.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed structure of cement clinker-related documents, balancing information completeness and retrieval accuracy |
chunkSplitter | Split by paragraph + heading | Matches the hierarchical structure of raw material batching plans and kiln operation records, avoiding truncation of critical business parameters |
customPdfParse | Enable full table recognition mode | Adapts to multi-column composition data tables in test reports and quality control notices, avoiding missing fields |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses parsing time for kiln operation records and monthly energy reports with more than 10 pages |
UPLOAD_FILE_MAX_SIZE | 100 MB | Meets upload requirements for large annual raw fuel ledgers and quarterly energy reports |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types such as inspection records and monthly reports |
Three common mistakes
- Scenario: After uploading a test report PDF, the parsing result only displays the first three columns of table data. Cause: Full table recognition mode for
customPdfParseis not enabled, and only default visible columns are extracted. - Scenario: After uploading a kiln operation record with more than 10 pages, the parsing node shows a timeout status. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual parsing time of the document. - Scenario: Chunked content for a raw material batching plan is truncated mid-batching ratio. Cause: The
chunkSizesetting is too small, and heading-based chunking logic is not enabled.
How to verify correct configuration
- Upload a typical test report PDF, and confirm the parsing result includes all composition data columns in full.
- Upload a kiln operation record with more than 10 pages, and confirm parsing time does not exceed the value set for
PARSE_FILE_TIMEOUT_SECONDS. - Upload short-duration equipment inspection records and long-duration monthly energy reports separately, and verify chunk lengths adapt to different document types.
- Check the total size of uploaded documents, and confirm it does not exceed the
UPLOAD_FILE_MAX_SIZElimit.
The values provided here are common starting points, and should be evaluated against the reader's own samples.