Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Mining and mineral processing document data mainly comes from mining design plans, mineral processing flow reports, safety and compliance regulations, daily production reports, equipment inspection records, and material consumption ledgers. The update rhythm varies by scenario: design documents are updated phase by phase as projects progress, production-related reports are updated daily or per shift, and inspection records are generated in real time. Document formats include dozens-of-page long text reports, structured material parameter tables, and forms with specialized fields. Fields cover grade, recovery rate, drilled meters, tonnage, and more. Common units are meters, tons, cubic meters, and percentage.
What constraints do these characteristics impose on document parsing and chunking
Long text reports for mining and mineral processing account for a large share, with individual documents often exceeding 50 pages. Direct chunking easily breaks professional logic, so matching the semantic coherence of long documents is required. Structured material tables contain multi-dimensional parameters. Conventional text chunking will destroy the field associations of tables, so targeted measures to retain table integrity are needed. Specialized technical terms and units appear frequently. This requires retaining context associations during chunking to avoid semantic breaks after splitting. Real-time generated inspection records are small in volume but updated frequently. They need to adapt to fast parsing workflows for short documents while avoiding excessive parsing timeouts.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic unit length of mining and mineral processing long documents, avoiding splitting of professional paragraphs |
chunkSplitter | Split by paragraph + heading hierarchy | Matches the chapter structure of mining and mineral processing documents, retaining professional logical coherence |
customPdfParse | Enabled | For PDF documents with many structured tables and specialized terms, retains format and field integrity |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of dozens-of-page long reports, avoiding parsing failure due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading and parsing of large mining design plans, covering the volume of all document categories |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types (long reports, tables, inspection records) to adapt to scenario requirements |
Three common errors
- Phenomenon: After uploading a mineral processing flow PDF, the parsing node has no log output, and the status shows timeout. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the parsing time of the long document exceeded the default threshold. - Phenomenon: After importing a material consumption table, only the first 3 columns of data are captured, and the remaining fields are empty. Cause:
customPdfParsewas not enabled, and the default parsing mode cannot recognize the structured table format dedicated to mining and mineral processing. - Phenomenon: After batch uploading inspection records, some short documents have missing unit information in the parsing results. Cause: The
chunkSizesetting is too small, splitting parameter lines with units into independent segments and losing context associations.
How to confirm the configuration is correct
- Upload a long mining and mineral processing document, check whether the parsed chunks are split according to the document's chapter logic.
- Upload a structured material table, check that the parsing result retains the complete row and column and field structure.
- Upload a large document, check that the parsing process does not trigger a timeout error.
- Adjust the chunking parameters and then upload inspection records, check that the parsing result does not lose unit and parameter associations.
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.