What this category of data looks like
Policy and regulatory basis data for the iron and steel smelting field mainly comes from official policy documents released by the Ministry of Industry and Information Technology, the State-owned Assets Supervision and Administration Commission of the State Council, and the China Iron and Steel Industry Association, as well as internal production compliance documents of central and state-owned enterprises. Document update rhythm is adjusted irregularly with the release of new national standards and industry regulations. Internal system documents are updated quarterly or annually. Document structures include document numbers, issuing authorities, effective dates, clauses and subclauses. Most documents include structured tables such as capacity indicators, energy consumption limits, and pollutant emission requirements. Fields include policy numbers, clause content, and quantitative indicators. Quantitative indicators mostly use industry-specific units such as kg standard coal/ton and m³/hour.
What constraints do these characteristics impose on the document parsing and chunking link?
Structured tables in policy documents will be broken apart by default chunking. Complete table rows or blocks must be preserved. Special units are tied to quantitative indicators, so chunking must not split associated content. Irregularly updated documents require incremental parsing to avoid repeated full processing. Clause hierarchies of long documents must be retained to match user queries for specific policy clauses. These characteristics require the document parsing link to prioritize preserving format and structured content. Chunking rules must adapt to industry-specific document structures, rather than using generic plain text chunking logic.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the clause length of iron and steel policy documents, balances context integrity and chunk granularity |
chunkSplitter | By Markdown heading + table row | Avoid splitting title hierarchy and structured table content of policy documents |
customPdfParse | Enabled | Adapts to official policy PDF formats including document numbers, stamps, and complex tables, improving parsing accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large iron and steel industry policy compilation PDFs take longer to parse, preventing mid-task timeout failures |
chunkSettingMode | Manual mode | Supports separate configuration of chunking rules for tables, adapting to industry-specific structured data |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Mistakes
- Phenomenon: After uploading multi-column iron and steel policy Excel attachments, chunking results are scattered and chaotic. Cause: Custom table parsing is not enabled, and default character-based chunking breaks the association between table rows and units.
- Phenomenon: When batch uploading policy documents, previously parsed files are repeatedly loaded and parsed. Cause: The full parsing switch is not turned off, and the system defaults to reprocessing all uploaded files.
- Phenomenon: After uploading scanned policy image files, the model cannot recognize policy text within the images. Cause: The OCR parsing module is not configured, and FastGPT does not support direct parsing of image format documents by default.
How to Confirm Configuration is Complete
- Upload an official iron and steel industry policy PDF, and verify that the parsed text retains the document number, clause hierarchy and table structure.
- Upload an Excel policy attachment with a capacity indicator table, and check that chunking results are split by complete rows or table blocks.
- Upload a single policy attachment, and confirm that the knowledge base only adds parsing results for the currently uploaded file, with no repeated loading of historical files.
- Upload a policy compilation file over 100 MB, and confirm that the parsing task does not trigger a timeout error.
The values provided are common starting points and should be evaluated against relevant sample data.
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.