What the data for this category looks like
Core data for this category comes from cement enterprise mining, raw material, calcination, grinding, quality, and energy departments. It covers documents including raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, energy reports, and more. Data sources include online composition analysis systems, DCS control systems, laboratory equipment, kiln and mill operation terminals, raw and fuel ledgers, and energy metering devices. Update cycles cover real-time, shift-based, and daily updates. Documents include fields such as composition content, temperature parameters, strength indicators, with units including MPa, tons, hours, and other industrial standard units.
What constraints do these characteristics impose on the "document parsing and chunking" link?
The multi-source, multi-update-cycle, and multi-format characteristics of this category impose clear constraints on document parsing and chunking. Real-time data from online composition analysis and DCS systems uses short entry formats, so it must be split into smaller chunks to ensure retrieval timeliness. Shift-based inspection records and daily laboratory reports use long paragraph structures, so context coherence must be preserved. Scanned kiln operation logs and archived reports require OCR parsing support, and table documents must retain complete fields and values. Documents with different update frequencies need distinct metadata tags to prevent expired data from being included in real-time retrieval results.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Balances contextual completeness for both long paragraphs and short entries in process documents, and adapts to the varying lengths of raw material batching plans and inspection records |
chunkSplitter | Split by paragraph + heading | Preserves the chapter structure and operational step coherence of process documents, avoiding splits that disrupt batching logic and parameter associations |
customPdfParse | Enable OCR enhancement mode | Recognizes text and hand-drawn parameter curves in scanned kiln operation logs, preventing empty parsing results |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to the parsing time required for large laboratory reports and quarterly energy reports, preventing task termination due to mid-process timeout |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading collections of multiple archived documents for enterprises, meeting storage needs for quarterly data summaries |
chunkSettingMode | Custom mode | Sets differentiated chunking parameters for different document types (such as inspection records and batching plans) to optimize retrieval accuracy |
This page was written based on FastGPT v4.17.0.
Three common mistakes
- Symptom: After uploading scanned kiln operation logs, the parsing result only contains blank placeholders. Cause: OCR enhancement mode for
customPdfParseis not enabled, so printed text and hand-drawn parameters in scanned documents cannot be recognized. - Symptom: When bulk importing raw material batching Excel files, the data processing step returns an empty list. Cause:
chunkSettingModeis not configured to adapt to table documents, and the default chunking logic incorrectly splits table rows into meaningless independent entries. - Symptom: When executing process standard Q&A, retrieval results include both the latest and expired shift-based inspection records. Cause: Metadata tags are not added to documents with different update frequencies, and no post-chunking filtering rules are set, leading to expired data being retrieved.
How to confirm the configuration is correct
- Upload a single raw material batching plan PDF, review the parsed chunking results, and confirm that chapter headings and corresponding paragraphs are fully retained.
- Upload scanned equipment inspection records, verify that recognized text and parameter curves from OCR have no obvious missing content or garbled characters.
- Upload a quarterly energy report collection larger than 1000 MB in size, confirm that the parsing task does not trigger a timeout error and generates valid chunks.
- Import laboratory reports in Excel format, check that the chunked table data retains complete fields and corresponding values such as composition content and detection time.
The values provided are common starting points and should be evaluated against the reader's own samples.