What the data for this use case looks like
The data typically originates from the "One Thing" special zone of the national government service platform, local integrated government service platforms, electronic certificate repositories, and departmental approval systems, and is updated daily. Commonly processed documents include cross-departmental service item lists, one-time notification sheets, cross-departmental application forms, material transfer forms, parallel approval opinions, completion receipts, and similar materials. Most documents contain cross-departmental item association fields, material requirement lists, processing workflow nodes, departmental authority divisions, and other content. No unified fixed format exists, and some documents embed tables or external attachment links.
What constraints these characteristics impose on document parsing and chunking
The scattered nature of cross-departmental association fields requires parsing processes to identify association relationships across pages and documents. Non-standardized document formats require parsers to adapt to multiple types of layouts. Daily updated data volume creates demand for batch processing, requiring chunking logic to adapt to different document lengths. Documents with dynamic processing statuses such as material transfer forms and completion receipts require retention of contextual associations to avoid semantic breaks after chunking. Documents with embedded tables require accurate extraction of table fields to avoid damage to table data integrity during chunking.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of documents such as cross-departmental service item lists and one-time notification sheets, meets the input length requirements of common embedding models, and avoids splitting cross-departmental semantic association units |
chunkSplitter | Separate by paragraphs + headings | Adapts to the heading hierarchy and paragraph structure of multiple types of government service documents, and retains the contextual integrity of item associations and material requirements |
customPdfParse | Enable embedded table parsing mode | Adapts to structured table data embedded in cross-departmental application forms and material transfer sheets, and accurately extracts field content |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large-volume documents such as batch-imported cross-departmental service item lists and multi-department approval opinion collections |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses time consumption requirements during batch parsing of multiple documents, and avoids interrupting the parsing process due to timeout |
chunkSettingMode | Manual configuration | For non-standardized government service document formats, allows flexible adjustment of chunking rules to adapt to different business document types |
Three Common Configuration Errors
- After a CSV-format cross-departmental service item list is uploaded, the parsing result only extracts data from the first and second columns. This occurs because the corresponding table parsing configuration is not enabled, and the default parsing logic only recognizes basic text paragraphs.
- When a large-volume multi-department approval opinion collection is uploaded, a 413 error is triggered. This occurs because the
UPLOAD_FILE_MAX_SIZEconfiguration value is smaller than the actual uploaded file size, exceeding the system's allowed maximum upload limit. - When an external pdf-maker parser is configured, the key setting entry cannot be found. This occurs because the corresponding parameter items are not configured in the system settings'
customPdfParsemodule.
How to Verify Correct Configuration
- A cross-departmental service item list including cross-departmental association fields is uploaded, and the parsed chunks are checked to confirm they retain complete item association semantics without mid-chunk semantic breaks.
- A cross-departmental application form with embedded structured tables is uploaded, and the parsing result is verified to fully extract all fields and content within the table without omissions or misalignment.
- A large-volume document fitting the business scenario is uploaded, and the upload and parsing processes are confirmed to not trigger errors, with the task completing normally.
- The
chunkSizeconfiguration is adjusted, and chunking results under different values are compared to confirm they meet the semantic splitting 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.