What the data for this category looks like
Internal office and employee service data for shipbuilding primarily comes from project construction specifications, equipment operation and maintenance manuals, design drawings, internal office notices, and employee training materials. Document update rhythms adjust with project progress and equipment iterations. Daily office notices update frequently. Professional construction documents update in phases alongside project stages. Documents typically have long paragraphs, densely nested tables, and compliance documents with digital signatures. Fields include hull number, construction date, equipment model, man-hours, and pressure values. Associated units include professional engineering units such as hours, tons, and MPa.
What constraints these characteristics impose on document parsing and chunking
Long documents and dense tables require parsing tools that support complex format recognition, to prevent table content from being split or lost. Compliance documents with digital signatures require special handling. The default parsing engine cannot read encrypted content. Documents with dense technical terms need to retain contextual connections, to avoid chunking breaking the semantic integrity of process steps and equipment parameters. Uploading and parsing large single documents takes significant time. File size limits and timeout parameters must be adjusted to avoid task interruptions.
Configuration Settings
These configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the long paragraphs and professional tables of shipbuilding documents, retains complete semantics of a single process description or single bill of materials list |
chunkSplitter | By paragraph + heading | Matches the chapter structure of shipbuilding documents, avoids splitting continuous assembly processes or content linked to equipment parameters |
customPdfParse | Enable pdf-marker parsing | Adapts to complex nested tables and compliance PDFs with digital signatures, improves recognition integrity for professional documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers upload requirements for large single construction specifications or equipment manuals |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the time requirements for long document parsing, avoids mid-parsing timeout interruptions |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for the professional characteristics of shipbuilding documents, adapts to non-general business scenarios |
Three common mistakes
- Recognized content is empty after uploading a PDF with a digital signature | The compliance PDF parsing mode of
customPdfParseis not enabled, and the default parsing engine cannot read document content encrypted with a digital signature - Doc2x tool call reports a file read failure |
UPLOAD_FILE_MAX_SIZEis not configured to accommodate large shipbuilding documents, or the locally deployed pdf-marker is not correctly connected to the FastGPT runtime environment - Chunked results contain incomplete process steps |
chunkSizeis set too small, splitting continuous assembly process descriptions and causing broken semantic coherence
How to verify proper configuration
- Upload a ship equipment manual PDF with a digital signature, check if the parsed text includes equipment model and technical parameter content
- Upload a construction specification document with more than 500 pages, confirm that the parsing progress does not show timeout errors and that parsing time meets expectations
- Review chunked content fragments, confirm that each chunk contains a complete single-section process description or single bill of materials table
- Check the file upload limit in the FastGPT backend, confirm that
UPLOAD_FILE_MAX_SIZEis set larger than the size of commonly uploaded documents
The values given 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.