What the data for this category looks like
Data for this category comes from government information disclosure platforms, official archive systems, document management systems, and departmental business ledgers. Commonly processed documents include government information disclosure application forms, correction notification letters, third-party opinion solicitation letters, response letters, service receipts, and administrative reconsideration response materials. Document structures cover fields such as applicant information, application content, correction items, response key points, and relief channels. Data updates have no fixed cycle. New content is generated as applicants submit applications and internal handling processes advance.
What constraints these characteristics impose on the document parsing and chunking link
Diverse document types and obvious structural differences require parsing tools to adapt to government documents of different formats, and avoid splitting key business fields. Data from multiple sources may include scanned documents, so high-precision OCR recognition must be supported to ensure text integrity. No fixed update frequency means the parsing process must adapt to scenarios where single or a small number of documents are processed at one time. Business handling has extremely high accuracy requirements. Errors in the parsing link may lead to errors in subsequent ledger summary and report generation, affecting administrative relief processes.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the paragraph length of government documents, avoid splitting core fields such as relief channels and applicant information |
chunkSplitter | Split by paragraph + heading | Match the hierarchical structure of government documents, preserve the integrity of correction items and response key points |
customPdfParse | Enable high-precision mode | Adapt to documents such as scanned service receipts and reconsideration materials, resolve OCR recognition errors |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Cover the parsing time of large archive scanned documents, avoid parsing timeout interrupting handling processes |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapt to batch document packages exported from official archive systems, avoid upload being blocked |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different types of government documents, meet the precise needs of individual case handling |
Three common errors
- The interface displays an
OCR Erroralert, and the parsed text lacks content from official seals or handwritten signature areas. This occurs when thecustomPdfParsehigh-precision mode is not enabled. Non-standard layout areas of scanned government documents cannot be recognized normally. - After uploading an Excel-format business ledger in the workflow, application statistics data in the table cannot be read. This occurs when the Excel file parsing rule is not enabled in the system configuration, or file reading permission is not configured in the workflow node.
- Parsing tasks time out, and FastGPT returns a connection timeout prompt. This occurs when the
PARSE_FILE_TIMEOUT_SECONDSsetting value is lower than the actual parsing time of large archive scanned documents. The parsing service is interrupted by the system before completion.
How to confirm the configuration is complete
- Upload a scanned response letter, and check whether the parsed text includes complete core fields such as relief channels and response document numbers.
- Upload a batch document package within 100 MB, and confirm that the upload and parsing processes can be completed normally.
- Adjust
chunkSizeto different ranges, and verify that the chunking results preserve the integrity of single-segment business content. - Check the switch status of
customPdfParse, and confirm that the parsing results of scanned documents have no obvious recognition gaps.
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.