Internal Office and Employee Services for Steel Smelting: Document Parsing and Chunking

Reference only; verify against the target FastGPT release and deployment documentation.

What the data for this use case looks like

Data related to internal office and employee services for steel smelting comes from sources including internal enterprise smelting process specifications, equipment operation and maintenance logs, safety inspection records, employee on-the-job training manuals, team performance appraisal forms, material consumption ledgers, and more. Update rhythms fall into three categories: operation and maintenance logs and inspection records are updated per shift or daily; process specifications and training manuals are updated quarterly or annually; material ledgers are updated in real time with production batches. Document structures include long plain text, multi-page scanned PDFs, structured Excel spreadsheets, mixed text-image reports, and others. Fields and units include smelting temperature (℃), smelting duration (hours), equipment operating pressure (MPa), employee ID numbers, inspection times, material batch numbers, and other professional information.

What constraints these characteristics impose on the document parsing and chunking link

Diverse document types and professional parameters require that long process descriptions and structured tables retain logical connections, so chunking must not excessively split content. Real-time updated operation logs and inspection reports demand high parsing efficiency, so parsing time must be controlled. Older scanned process manuals require OCR parsing to avoid losing professional data from images. Differences in update frequencies across documents require parsing configurations to support flexible adjustments, to adapt to both batch and real-time processing needs. Preserving professional fields and their units directly affects retrieval accuracy of the subsequent knowledge base, so parsing must avoid losing the correspondence between fields and units.

How to set the configuration

Three Common Misconfigurations

• A scanned safety inspection report is uploaded, and the model returns no valid content, with the parsing result only containing blank pending verification text. This occurs because the OCR enhancement mode for customPdfParse is not enabled, and only the underlying blank information of the scanned document is extracted.
• An Excel equipment parameter table is imported, and the correspondence between column names and values in the parsing result is lost, with table content split into unordered text blocks. This occurs because structured table retention rules are not configured under chunkSettingMode, leading to damage to the table structure.
• A single smelting process manual larger than 500 MB is attempted to be uploaded, and the upload request fails directly. This occurs because UPLOAD_FILE_MAX_SIZE is not adjusted to a suitable value in advance, exceeding the default upload limit.

How to confirm the configuration is correct

• A scanned process manual is uploaded, and the parsing result is checked for complete table parameters and text descriptions to confirm that customPdfParse is correctly enabled.
• An Excel equipment ledger is imported, and the parsed table column names are verified to correspond one-to-one with the original data to confirm that the structured parsing configuration takes effect.
• A long PDF document of around 100 MB is uploaded, and timeout errors are checked for after parsing is complete to confirm that the PARSE_FILE_TIMEOUT_SECONDS value is reasonable.
• A question is submitted for a single PDF process document, and the model's returned content is checked to cover the complete process logic of the document to confirm that the chunkSize and chunkSplitter configurations adapt to the document type.

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.