Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The business process documents for steel smelting mainly originate from production scheduling systems, safety management platforms, equipment maintenance ledgers, and business approval workflow forms. The update rhythm falls into three categories: production scheduling orders are updated daily, safety operation procedures are revised quarterly, and equipment maintenance records are archived monthly. Most documents include fixed headers, hierarchical chapters, and structured tables. Fields cover furnace temperature, operation duration, consumable usage, approval node status, and other related items, using standard industrial production units of measurement.
What constraints do these characteristics impose on the document parsing and chunking link
Fixed fields and units require that parsing tools do not destroy associated data within tables, otherwise parameter misalignment will occur in subsequent business guidance. Hierarchical chapters and approval nodes require chunking logic to recognize structured separators, to avoid splitting complete operation processes. Large monthly ledger PDFs may exceed conventional file sizes, requiring adjustments to upload and parsing parameter limits. Frequently updated documents require fast parsing to support real-time business guidance needs.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the typical length of a single process segment or single table in steel smelting business documents, avoiding splitting complete operation nodes |
chunkSplitter | Segment by chapter numbers and table separators | Adapts to the hierarchical structure and structured tables in documents, preserving the integrity of field associations |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Meets the parsing time requirements for large ledger PDFs under 1000 MB |
customPdfParse | Enabled | Correctly identifies encrypted forms, nested tables, and industrial drawing layouts commonly found in steel smelting PDFs |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the upload requirements for large documents such as monthly production ledgers |
trainingType | Question-and-answer pair exclusive mode | Targets preset question-and-answer pair documents, ensuring that returned content exactly matches the original response text |
Three Common Configuration Mistakes
- Phenomenon: When uploading a steel smelting ledger PDF of over 10 MB, the interface prompts
timeout of 360000ms exceeded. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default timeout duration is insufficient for large documents containing multi-page tables. - Phenomenon: Split complete operation processes appear in the parsed chunks, causing the business guidance to fail to display coherently. Cause: The
chunkSplitterwas configured with a universal line break, without matching chapter numbers or approval node separators in the document, and no merging logic for overly long segments was applied. - Phenomenon: The content returned by knowledge base question answering does not use the original response text from the question-and-answer pairs. Cause: The
trainingTypewas not configured as question-and-answer pair exclusive mode, causing the system to perform secondary processing on the content and failing to return the preset response verbatim.
How to confirm the configuration is correct
- Upload a single steel smelting monthly production ledger PDF under 500 MB, check whether the parsing task completes within the set
PARSE_FILE_TIMEOUT_SECONDSwithout errors. - Select a document segment containing a complete equipment maintenance process, verify that the chunking results do not split key operation nodes.
- After enabling
customPdfParse, check whether the table fields in the parsing results are complete and the format matches the original document structure. - Upload a preset question-and-answer pair document, initiate relevant business inquiries, and confirm that the returned content exactly matches the original response text.
The values provided are common starting points and should be measured against corresponding 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.