What this category of data looks like
The data for coal mine production declaration materials and file review mainly comes from mine scheduling systems, safety supervision department submission portals, and internal archive storage. Update cycles follow declaration periods or file archive nodes, with no fixed high-frequency updates. Document formats include mixed types such as docx declaration forms, pdf safety supervision reports, and excel production ledgers. The page count of individual documents varies widely, ranging from a few pages of temporary declarations to dozens of pages of annual files. Documents contain structured fields including mine numbers, mining horizons, gas monitoring data, and approval document numbers. Units cover professional measurement standards such as tons, cubic meters, ppm, and workdays.
What constraints these characteristics impose on document parsing and chunking
Mixed-format documents require parsing tools to support both text-based and scanned files. Otherwise, some content will be lost. The wide page count range of individual documents means chunking rules must balance the integrity of short files and the readability of long files. Avoid overly fragmented or overly long chunking. The presence of structured fields and professional measurement units requires that chunking preserves field associations. Do not split solely by character length, as this will break data logic. Irregular updates and large files require the parsing process to have flexible timeout handling capabilities and configuration space for large files.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 1000–1500 characters | Coal mine declaration materials have relatively high content per page. This avoids overly fragmented chunking that loses contextual associations |
chunkSplitter | Split by paragraph + heading | Declaration materials have clear chapter headings and logical paragraphs, which supports structured chunking for professional documents |
customPdfParse | Enable OCR mode | Covers scanned file archives, extracts text and data embedded in images |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Adapts to the parsing time of large archives, prevents parsing failures due to timeouts |
chunkSettingMode | Manual configuration | Adjust chunking rules for coal mine-specific document structures, adapts to logical associations between professional fields and units |
Three common mistakes
- Scenario: After uploading docx or excel files, retrieval result accuracy is low. Cause: Metadata extraction is not configured for structured fields, and chunking breaks the association between fields and corresponding data.
- Scenario: After uploading scanned pdf archives, text content within images cannot be extracted. Cause: OCR mode for
customPdfParseis not enabled, only basic text parsing logic is used. - Scenario: Parsing annual archives with more than 50 pages triggers a parsing timeout error. Cause: The set value of
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual required parsing time.
How to confirm the configuration is complete
- Upload a single typical monthly declaration material, check the parsed chunking results, confirm that chunks match the document's chapter and paragraph structure.
- Upload a scanned pdf of downhole working condition reports, check if parsed results include text content from images.
- Upload a single annual archive with more than 100 pages, confirm that the parsing process does not trigger a timeout error.
- Enter a search query containing a mine number or approval document number, confirm that returned results include metadata information for the corresponding fields.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.