What this category of data looks like
The core documents for this category include business license application forms, vehicle technical files, driver qualification materials, safety production management systems, road transport business licenses, and more. Data sources are the road transportation administration system, the national road transport vehicle dynamic information public service platform, and the vehicle and personnel database. The update frequency is daily. Documents contain both structured form fields, such as business entity name and vehicle license plate number, and coherent or non-coherent text content. The length of a single document ranges from several pages of application materials to dozens of pages of system compilations. Fields cover qualification numbers, vehicle parameters, business site information, and other related content.
What constraints do these characteristics impose on document parsing and chunking
Data sources cover multiple government systems and enterprise-owned documents. Input formats include PDF and Word-exported application forms and system texts. Parsing logic must support multiple formats. Daily batch processing requires the parsing process to have stable timeout fault tolerance and batch scheduling capabilities. This prevents single-document processing delays from impacting overall review efficiency. Documents include both structured qualification fields and coherent system texts. When chunking content, avoid splitting across semantic units. Retain the association between structured fields and their corresponding explanatory texts to ensure information integrity during approval and verification. Single document lengths vary widely. Support dynamic adjustment of chunking boundaries to fit materials of different lengths. This avoids semantic fragmentation from overly short chunks or context limit breaches from overly long chunks.
How to configure the settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed length of structured fields and unstructured text in road transport documents, avoiding splitting key qualification information and coherent system paragraphs |
chunkSplitter | Split by semantic paragraphs | Matches the semantic units of coherent texts such as safety systems and application descriptions, avoiding damage to the complete logic required for approval |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for long documents such as dozens of pages of safety system compilations, avoiding timeout interruptions during batch processing |
customPdfParse | Enable basic PDF parsing mode | Adapts to standard PDF documents exported from government systems, no additional model training required to recognize structured form fields |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates batch upload requirements for multi-page vehicle technical files and annual review materials |
chunkSettingMode | Manual configuration | Flexibly adjust chunking parameters for different document types, such as application forms and safety systems, to adapt to business scenario differences |
Three common misconfigurations
- Out-of-vRAM error occurs after deployment, and GPU hardware cannot be detected. Cause: Container GPU mounting parameters are not configured correctly, or the CUDA version does not match the model version required by the PDF parsing dependency, leading to driver loading failure.
- Image domain names are lost in Markdown files generated after importing Word documents. Cause: Resource path binding configuration during document parsing is not enabled. The domain name is automatically attached only during the native Word import process. Resource association information is not retained after conversion to Markdown.
- Request timeout errors occur during batch parsing. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to a value suitable for long documents. This causes parsing of single large-volume documents to exceed the default time limit.
How to confirm the configuration is correct
- Upload a single typical document, such as a 50-page safety production management system, and check the parsed chunk results. Confirm that key fields such as vehicle license plate number and qualification certificate number are not split.
- Check the FastGPT v4.17.0 container logs or parsing task status. Confirm that GPU hardware is correctly detected, and there are no error messages related to out-of-vRAM or driver loading failure.
- Upload documents in different formats, including PDF and Word, to verify the integrity and format consistency of parsed text. Confirm that multi-format adaptation works normally.
- Adjust the
chunkSizeparameter and re-parse the same document. Confirm that chunk length changes as expected with the configuration.
The values provided here are common starting points and should be measured against your 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.