Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The business data for this category mainly comes from the road transport administration 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 business license applications, vehicle technical files, driver qualification materials, safety production management systems, road transport business licenses, etc. Document structures contain fields such as operating entity name, vehicle license plate number, qualification certificate number, business scope, and safety management system clauses. Some documents are in scanned format, including vehicle photos and scanned content of personnel credentials.
What constraints these characteristics impose on the document parsing and chunking link
Multi-source data leads to inconsistent document formats, including plain text PDFs, Excel spreadsheets, and scanned image documents. Parsing configurations must support multiple input formats. High-frequency daily updated data requires an efficient chunking process to avoid delaying approval efficiency due to long parsing time. Documents contain a large number of business-specific fields, so the association between fields must be retained during chunking, and splitting must not damage the integrity of business logic. Text extraction from scanned documents relies on specialized parsing capabilities; regular text parsing cannot obtain valid business information from images.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Business documents are mostly long texts with multiple fields. This range balances semantic integrity and retrieval accuracy, avoiding excessive chunk size that disperses associated information, or excessive small chunk size that damages business logic |
chunkSplitter | Split by paragraph + semantic boundary | Business documents contain structured fields and non-management system text. Splitting by semantics preserves the integrity of associated content such as operating entity and vehicle information |
customPdfParse | Enabled | Adapt to the text extraction requirements of scanned PDF documents, and resolve parsing gaps for image-scanned format documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large aggregated PDFs of vehicle technical files may contain multiple scanned pages. The default timeout duration cannot complete full parsing. This duration covers the parsing process of most large documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapt to the needs of transport enterprises to batch upload vehicle archives and practitioner materials, avoiding single-batch imports being blocked due to oversized files |
chunkSettingMode | Manual configuration | Different document types (such as license applications and archive materials) have different chunking requirements. Manual configuration can accurately adapt to the parsing requirements of various business documents |
Three common mistakes
- Uploading a scanned PDF document results in an empty parsing result. The cause is that the
customPdfParseconfiguration is not enabled, and the image text extraction function is not turned on, so business information in the scanned content cannot be recognized. - When searching Excel documents, the number of returned results is less than the source file. The cause is that
chunkSizeis set too small, resulting in loss of some associated row data during splitting, or failure to correctly identify merged cell fields in Excel, leading to some business information not being indexed. - Parsing a large PDF document returns a timeout error. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient to complete the full parsing process of multi-page scanned documents.
How to confirm the configuration is set correctly
- Upload a scanned PDF test document, and check whether the parsed text content completely extracts the text information in the image.
- Upload an Excel test file containing multiple business fields, search for specified business fields, and verify that the number of returned results matches the source file.
- Upload a large PDF document, wait for the parsing to complete, and confirm that no timeout error occurs.
- Adjust the
chunkSizeparameter, compare the retrieval semantic integrity under different configurations, and confirm that it meets the logical association requirements of business documents.
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.