What this category of data looks like
Steel smelting-related documents and materials mainly originate from on-site production process specifications, quality inspection ledgers, equipment maintenance records, bidding and compliance documents. Update frequency adjusts based on production tasks and compliance review requirements, with no fixed cycle. The page count and content volume of single documents vary widely. Document structures include business tables with fixed headers, long paragraphs of process descriptions, and compliance documents with nested multi-level directories. Fields include smelting temperature, component proportion, equipment operating duration, batch number, etc., all using industrial standard measurement units.
Constraints on document parsing and chunking from these characteristics
Business tables with fixed headers can cause broken data associations if split during chunking. Long paragraphs of process descriptions require precise context matching to retain operational logic. Compliance documents with multi-level directories need to retain their hierarchical structure to avoid information loss. Business fields with industrial units lose their measurement meaning if split, which invalidates subsequent retrieval and business verification. Single documents may contain dozens of pages of production ledgers, so the parsing and chunking link needs to adapt to large-volume files and long text, while retaining the association between fields and units to avoid post-parsing data being unable to restore the original business logic.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to long process description paragraphs and table blocks with fields in steel smelting documents, avoiding splitting that breaks business logic |
chunkSplitter | By paragraph + title anchor | Matches multi-level title and paragraph structures in documents, retaining contextual associations for process steps and compliance clauses |
customPdfParse | Enable table structured parsing | Adapts to the fixed format of a large number of composition detection tables and energy consumption ledgers in smelting documents, restoring the association between fields and units |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for single-batch production ledgers and large compliance documents, avoiding upload errors |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time required for long-text parsing and structured processing, avoiding mid-process timeout interruptions |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types (process documents/quality inspection reports) to adapt to differentiated business data structures |
Three Common Mistakes
- Phenomenon: A 413 error is prompted during file upload, and parsing fails. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration is not adjusted, and the default value is insufficient to accommodate the volume of single-batch smelting ledgers or large compliance documents. - Phenomenon: Calling the API to upload a document shows a successful call, but no parsing result is returned. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration is not set to a sufficient duration, long-text parsing is not completed before returning an empty result, or the structured parsing rule ofcustomPdfParseis not enabled. - Phenomenon: Doc documents and images in multi-level directories on Feishu cannot be fully parsed and retrieved. Cause: The title anchor matching rule of
chunkSplitteris not enabled, and structured parsing for the corresponding document type is not enabled, resulting in missing hierarchical structures and embedded resources.
How to Verify Proper Configuration
- Upload a single smelting ledger document that meets the configuration limit, check that there are no errors during the upload process, and verify that the
UPLOAD_FILE_MAX_SIZEconfiguration takes effect. - Parse a process document that includes multi-level titles and tables, check that the complete title hierarchy and table fields are retained in the chunking results, and confirm that the
chunkSplitterandcustomPdfParseconfigurations are correct. - Call the API to upload a test document, wait for the configured timeout duration, then check the returned results to confirm that the parsed content is complete and includes business fields.
- View the parsed chunked content, confirm that fields with units such as temperature and component proportion are not split and lost, and verify that the
chunkSizeconfiguration adapts to the document structure.
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.