What the data for this category looks like
Data for this category mainly comes from engineering quality and safety supervision systems, site real-name registration platforms, supervision logs, and test reports. The update frequency is weekly. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Most documents contain fields such as inspection date, hidden danger level, rectification requirements, and acceptance standards. Some documents include specific parameters such as site locations and test values. Supported formats include PDF, Excel, plain text, and other types.
What constraints do these characteristics impose on the document parsing and chunking link
Weekly batch-updated documents require a stable batch parsing workflow. Multiple document types have significant structural differences. Some are paragraph-style documents such as supervision logs and plans. Others are tabular documents such as test reports and hidden danger rectification notices. Some documents include structured parameters such as site locations and test values. Chunking must avoid splitting that breaks data relevance. Parsing pressure from batch uploads may cause timeouts. Adjust the timeout threshold accordingly. The party building learning Q&A scenario requires chunked content to retain sufficient context. This ensures complete engineering supervision logic and party building-related requirements can be associated during Q&A. It avoids Q&A deviations caused by information breaks.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents contains structured parameters and paragraph text. This range balances content completeness and retrieval accuracy |
chunkSplitter | Split by paragraph + title | Most daily documents contain hierarchical titled content such as supervision plans and inspection records. Splitting by title preserves the logical structure of the document |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Documents with large content such as batch-uploaded test reports and completion acceptance supervision reports require sufficient time to complete parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some completion acceptance supervision reports include a large number of site images and test data, so large file upload support is required |
customPdfParse | Enabled | Engineering PDF documents often contain tables and encrypted content. Custom parsing improves table extraction accuracy |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types to adapt to parsing needs of multi-format content |
Three Common Configuration Errors
- Phenomenon: After uploading tabular documents such as hidden danger rectification notices, some fields in the parsing result are empty or data is missing. Cause: The
customPdfParseconfiguration is not enabled, and the default parsing engine cannot fully extract tabular structured data. - Phenomenon: After batch uploading completion acceptance supervision reports, the interface displays a request failure, and the log returns a timeout status code. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient to process large-capacity engineering documents. - Phenomenon: After uploading a test report Excel file, the training dataset has incomplete data. Cause: No dedicated chunking rules are configured for tabular documents, and the default chunking logic splits associated data across cells.
How to Verify Correct Configuration
- Upload a single completion acceptance supervision report PDF under 100 MB. Confirm whether the parsing status is completed within the preset timeout threshold.
- Upload a test report Excel file containing multiple tables. Verify whether the parsed data fields are consistent with the original document.
- After adjusting the
chunkSizeparameter, randomly extract a section of document content. Confirm that the chunked content does not destroy core information relevance. - Batch upload 5 weekly updated supervision inspection records. Verify whether all batch parsing tasks are successfully completed.
The values listed here are common starting points, and should be evaluated against samples relevant to the actual deployment.
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.