What the Data for This Category Looks Like
Daily data for this category originates from highway maintenance management systems, bridge and tunnel monitoring systems, road patrol management systems, and traffic flow monitoring data, with a daily update frequency. Typical documents include maintenance plans, patrol logs, defect disposal orders, road-related construction permits, and road property damage compensation forms. Most documents are structured tables or forms with fixed fields. These fields cover road section identifiers, defect parameters, disposal processes, permit numbers, and more. Common business measurement units include length, area, and time.
Constraints for Document Parsing and Chunking
Daily batch-updated business data requires parsing processes with stable batch processing capabilities. Mixed imports of multiple document types need parsing rules adapted to different formats. Structured ledgers have strong field correlations. During chunking, complete binding relationships between road sections, defects, and disposal records must be retained to avoid breaking business logic via splitting. Large-volume monthly summary files may exceed default parsing durations. Adjust timeout configurations to ensure processing completeness.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the length of single business records in patrol logs and defect disposal orders, avoiding splitting that breaks the correlation between road sections and defects |
chunkSplitter | Paragraph + semantic boundary | Aligns with the natural paragraph structure of maintenance documents, retaining complete business context for road-related construction permits and compensation forms |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates batch-imported monthly summary files, preventing task interruptions due to insufficient processing time |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports importing multiple large ledger files exported from systems in a single batch |
customPdfParse | Enabled | Accurately extracts form fields from road-related construction permits, avoiding missing key information caused by generic PDF parsing |
trainingType | Structured document training | Adapts to the structured data format of maintenance ledgers, preventing non-document content from being mistakenly included in the parsing process |
Three Common Mistakes
- Phenomenon: Importing an Excel-format maintenance ledger results in empty data processing output. Cause: Custom parsing configuration for structured tables is not enabled. The default parsing logic cannot recognize the specific business field columns of the ledger.
- Phenomenon: Batch importing monthly maintenance plan PDF files causes parsing task timeout failures. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The default timeout duration is insufficient for processing large-volume summary documents. - Phenomenon: Defect records from adjacent road sections are merged into a single chunk in the chunking result. Cause:
chunkSplitteris set to split by fixed characters, and semantic boundary splitting is not enabled. This truncates business-related units.
How to Confirm Configuration Is Set Correctly
- Upload a single daily patrol log document. Review the parsed chunk list to confirm each chunk contains a complete single patrol record.
- Upload an Excel ledger file exported from the system that is under 100 MB. Check that data processing completes within 600 seconds with no timeout errors.
- Enable
customPdfParse, then upload a road-related construction permit PDF. Confirm core fields such as permit number and construction road section are extracted in the parsing result. - Batch import three documents of the same type. Confirm the number of chunks matches the number of business records in the documents, with no omissions or duplicates.
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.