What this category of data looks like
The data for iron and steel smelting declaration materials and file review mainly comes from process specification documents, environmental impact assessment declaration forms, quality inspection files, equipment operation and maintenance logs, and monthly production reports. Data update cycles cover three scenarios: real-time process logs, monthly declaration submissions, and quarterly file archiving. Document formats include formatted PDF official files, structured Excel quality inspection tables, and plain text operation logs. Common fields include furnace number, batch number, smelting temperature (unit: ℃), harmful element content (unit: ppm), and other industrial parameters.
What constraints do these characteristics impose on document parsing and chunking
The data characteristics of this category impose multiple constraints on document parsing and chunking. First, document formats include PDFs with complex tables, structured tables, and plain text logs. The parsing link must accurately identify industrial parameters and associated text within tables, and avoid splitting bound fields such as furnace number and smelting temperature. Second, data updates cover real-time, monthly, and quarterly scenarios. Large-volume files for batch declarations require appropriate upload and parsing thresholds. In addition, dedicated industrial parameters have fixed units. Chunking must retain the contextual association between parameters and units to prevent the separation of values and units after parsing.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of long text paragraphs and structured parameter blocks in iron and steel smelting documents, avoiding splitting bound industrial parameter groups |
chunkSplitter | Split by paragraph + fixed character threshold | Adapts to mixed-format documents, prioritizes splitting by natural paragraphs, and triggers character threshold splitting when insufficient, retaining parameter context |
customPdfParse | Enable table structured parsing mode | Improves table field recognition accuracy for PDF quality inspection sheets and process documents with multi-column parameters in declaration materials |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large-volume PDF files for batch quarterly file archiving, avoiding interception of compliant files during the upload phase |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the parsing time requirements for large-volume process documents and multi-page files, avoiding mid-parsing timeout interruptions |
chunkSettingMode | Manual configuration mode | Flexibly adjusts chunking parameters for different types of declaration materials and files, adapting to differences between structured and unstructured documents |
Three common mistakes
- Phenomenon: After uploading some iron and steel smelting PDF declaration files, the parsing result shows empty content, while some files can be recognized normally. Cause: Some PDFs use encrypted fonts or embed non-standard industrial symbols, and the default parsing engine cannot correctly extract text content.
- Phenomenon: After modifying a Java interface document to a TXT suffix and importing it, the parsing result has no valid data entries. Cause: The plain text format cannot retain the structured comments and parameter naming rules of the document, and the default chunking logic cannot identify valid fields of industrial interface documents.
- Phenomenon: After the pushdata API is called in
chunkmode to upload data, the interface displays the indexing status for an extended period. Cause: Chunking parameters are configured too large, or the total length of uploaded documents exceeds the parallel processing threshold of the current cluster, resulting in indexing task pile-up.
How to confirm correct configuration
A single typical iron and steel smelting quality inspection PDF document is uploaded, and fields including furnace number, smelting temperature and other entries in the parsing result are verified as complete, with no empty content. The pushdata API is called to upload test data, and the indexing status is confirmed to be updated within a reasonable period, with no long-term pile-up prompts. After the chunkSplitter configuration is adjusted, a long-text process document is imported, and the chunking result is checked to retain the contextual association between parameters and units. A file larger than the conventional volume is uploaded, and confirmation is made that the upload phase is not intercepted and the parsing process does not trigger a timeout.
The values provided are common starting points and should be evaluated against the relevant sample datasets.
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.