What this category of data looks like
This category of data comes from government information disclosure platforms, agency archive systems, document management systems, and departmental business ledgers. Update frequency is irregular, aligned with individual application and archiving progress. Typical documents include standard forms such as government information disclosure application forms, supplementary notice letters, response letters, service receipt forms, and administrative reconsideration response materials. Some archived documents are scanned copies or format files stored on internal network systems. Document structures include fixed fields such as applicant information, application items, public attribute judgment, response basis, and relief channels. The length of individual documents varies widely, ranging from a few pages to hundreds of pages.
Constraints Imposed by These Characteristics on the Document Parsing and Chunking Link
This category of document characteristics imposes three core constraints on the parsing and chunking link: First, document sources include public network platforms, internal network systems, and scanned copies. The parsing module must support public network addresses, internal network permission configurations, and OCR recognition. Second, documents are standardized but have uneven lengths. Chunking must preserve semantic connections between fields, and avoid splitting critical legal basis and response logic. Third, archived documents may have format compatibility issues. Custom parsing rules must be supported to adapt to documents from different storage systems. Parsing failures directly affect the compliance of subsequent drafting links, so a fault-tolerant mechanism is required.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Application disclosure documents are mostly standardized content. Each chunk must cover complete application elements and response logic to avoid splitting critical clauses |
chunkSplitter | Hierarchical splitting by title + paragraph | Documents have clear title hierarchies such as applicant information, application items, and response basis. Splitting by this structure preserves semantic integrity |
customPdfParse | Enable OCR mode | Some archived documents are scanned copies. OCR can accurately extract printed or handwritten document content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large archived PDFs such as annual response compilations take longer to parse. This avoids interrupting the parsing process due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets upload requirements for multi-page archived documents, covering document sizes for most scenarios |
chunkSettingMode | Manual configuration | Different application scenarios such as application form parsing and response letter drafting require adjustment of chunking parameters. Manual configuration allows flexible adaptation |
Three Common Mistakes
- Failure to enable parent-child chunking functionality, resulting in broken hierarchical associations for long documents. Phenomenon: When parsing multi-page response letters, cross-paragraph legal basis and factual connection information are lost. Cause: Application disclosure responses require consistent reference to regulations and specific facts. Missing parent-child chunking will destroy the logical integrity of the context.
- Unable to extract content from internal network Confluence pages, returning empty text. Phenomenon: After uploading an internal Confluence link, the parsing result only contains the link address or is completely empty. Cause: Internal network access permissions or custom parsing rules are not configured. FastGPT only supports public network address parsing by default.
- Triggering incomplete input prompts when parsing large PDF documents. Phenomenon: After uploading an archived PDF with more than 500 pages, the large language model returns a prompt indicating incomplete input. Cause:
PARSE_FILE_TIMEOUT_SECONDSis set too short. The document does not complete full parsing before subsequent processing is triggered, resulting in missing input data.
How to Verify Proper Configuration
- Upload a standard government information disclosure application form PDF, check whether the parsed text completely extracts core fields such as applicant information and application items
- After configuring
PARSE_FILE_TIMEOUT_SECONDSto 600 seconds, upload an archived PDF with more than 500 pages, confirm that the parsing status does not show a timeout - Test parsing of internal network Confluence pages, confirm that page content can be extracted normally after custom parsing rules are enabled
- After enabling the parent-child chunking function, check whether the chunking results retain the association between titles and corresponding paragraphs
The values listed here are common starting points, and should be validated against the user'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.