Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data for this category primarily comes from the road transport administration management system, the national public service platform for dynamic information of road transport vehicles, and the vehicle and practitioner database. The update frequency is daily.
Commonly processed documents include operation license application materials, vehicle technical files, driver qualification materials, safety production management systems, and official road transport operation license documents. Most document structures contain standardized approval fields, vehicle and personnel information entries, and system clause paragraphs. Some archive documents have multi-page nested attachment structures. Most fields involve identification content such as qualification numbers, valid periods, and business scopes.
What constraints do these characteristics impose on the document parsing and chunking link
High-frequency daily updated data requires the parsing process to have stable batch processing capabilities, to avoid impacting overall approval efficiency due to single-file parsing timeouts.
The scenario of mixed processing of multiple document types requires chunking logic adapted to different document structures, to avoid splitting cross-page vehicle qualification information into separate chunks.
Document structures with both standardized approval fields and unstructured system clause text require chunking rules that balance the integrity of structured fields and the semantic coherence of unstructured text.
Vehicle and personnel data have strong correlation, so context association between adjacent entries must be retained during chunking, to prevent information breaks in subsequent verification links.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed structure of standardized fields and unstructured text in road transport-related documents, avoiding semantic dispersion from overly long single chunks or loss of context from overly short chunks |
chunkSplitter | Split by paragraph + title | Matches clearly segmented content such as system clauses and qualification entries in documents, and retains the integrity of relevant information in the approval process |
UPLOAD_FILE_MAX_SIZE | 20 MB | Covers most enterprise-submitted archive files of 10–15 MB, avoiding large file upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time when batch processing multiple vehicle technical files, preventing process interruption due to timeout |
customPdfParse | Enabled | For multi-page nested vehicle technical file PDFs, enable custom parsing mode to retain page-level association |
Three common mistakes
- Phenomenon: A timeout error occurs when parsing a 15 MB Word document, and the interface displays a parsing timeout prompt. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to a value suitable for large files, and the default parameter cannot cover the parsing time of batch archives. - Phenomenon: After importing Excel-format driver qualification materials, multiple lines of content are merged into a single chunk. Cause: The separation rule of
chunkSplitteris not configured correctly, and the custom chunking logic of splitting by line is not enabled. - Phenomenon: After parsing the imported PDF-format road transport operation license document, the qualification number field is empty. Cause: The
customPdfParsemode is not enabled, and the default parsing logic cannot recognize the structured printed fields on the license.
How to confirm the configuration is correct
- Upload a single 15 MB Word-format vehicle technical file, and check whether the parsing progress is completed within the preset timeout period.
- Import Excel-format driver qualification materials, and check whether each record corresponds to an independent chunk in the chunking result.
- Upload a multi-page nested PDF-format operation license application, and check whether the parsed chunks retain the context association between pages.
- Batch upload 5 documents of different types, confirm that all files have completed parsing without abnormal errors.
The values listed are common starting points and should be evaluated 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.