What the data for this category looks like
Daily documents for this category include maintenance plans, patrol logs, defect disposal forms, road construction permits, road damage compensation forms, and similar items. Data mainly comes from highway maintenance management systems, bridge and tunnel monitoring systems, road patrol systems, and traffic flow monitoring data, with a daily update frequency. Most document structures combine fixed forms and free text. Some patrol logs include on-site photos of defects. Fields include facility ID, defect type, disposal deadline, compensation amount, and similar items. Units include kilometers, square meters, yuan, and similar units.
What constraints these characteristics impose on the document parsing and chunking link
High-frequency daily data requires the parsing process to have stability and timeliness, to avoid timeouts that disrupt work order flow. The document structure combining fixed forms and free text requires chunking logic to balance structured field extraction and reasonable truncation of unstructured content. Patrol logs with attached photos require parsing to distinguish text and image content, to avoid mixed parsing that causes chunking confusion. Compliance review requirements for road construction permits and road damage compensation forms require chunking to retain complete context of key approval nodes, to avoid splitting of key information.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the text length of patrol logs and defect disposal forms, avoid splitting single defect information into different chunks |
chunkSplitter | Split by paragraph + heading | Match the heading hierarchy of maintenance plans and patrol logs in documents, retain complete context |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Handle single large maintenance ledger or batch-imported patrol logs, avoid task failure due to parsing timeout |
customPdfParse | Enabled | For road construction permit documents with attached photos, retain the original image embedding structure, avoid over-parsing via OCR |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapt to monthly maintenance data summary files imported in batches, meet the capacity requirements for single uploads |
Three common configuration errors
- Enabling
customPdfParsewhen importing PPT or DOC documents results in no effect, and the parsing result is the same as when it is not enabled. The reason is thatcustomPdfParseonly applies to PDF format documents, and does not trigger the corresponding parsing logic for other office document formats. - Parsing Excel tables cannot obtain sheet names, and the chunked content only includes the text of the default sheet. The reason is that the default parsing logic does not configure sheet extraction rules for structured tables, and the corresponding configuration items are not enabled.
- Images and text in patrol logs are chunked together, resulting in inability to accurately match image information during subsequent retrieval. The reason is that the separate parsing logic for images and text is not configured, and chunking is not split by content type.
How to confirm the configuration is correct
- Upload a typical patrol log document, check the parsed chunked content, confirm that the length of each chunk meets expectations, and no single defect information is split.
- Upload an Excel maintenance ledger with multiple sheets, confirm that the parsing result includes the names and corresponding content of all sheets.
- Upload a road construction permit PDF containing images, check whether the parsing result retains the original embedding position of the images, and is not replaced by OCR text.
- Upload a large document under 500 MB, confirm that the parsing task is completed within 180 seconds, and no timeout error occurs.
The values provided here 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.