What the data for this category looks like
Documents in this category include service guides, application forms, commitment letters, acceptance notices, correction notices, administrative licensing decision documents, and license certificates. Data is sourced from the national integrated government service platform item database, administrative licensing item list, departmental business approval systems, and electronic certificate repositories. Updates occur daily. Most documents follow legally fixed formats, with standardized fields such as item name, applicant information, legal basis clauses, and approval document numbers. Some documents retrieved across departments require format alignment across multiple systems.
What constraints these characteristics impose on the "document parsing and chunking" workflow
Differences in document formats from cross-system sources increase parsing adaptation costs. Daily updated batch materials require parsing processes with high throughput. Legally fixed format documents contain indivisible key clauses, such as setting basis and application conditions. Chunking must retain field associations to avoid splitting critical legal requirements. Electronic certificates retrieved across departments require accurate matching of subject information and item numbers. The parsing link must identify unique identifier fields in documents to prevent association errors. Parsing errors in approval materials directly lead to overdue completion or reconsideration risks. Therefore, the accuracy of parsing and chunking must meet the rigor requirements of legal review.
Configuration settings
Under FastGPT v4.17.0, the following configuration items can directly adapt to the requirements of this category:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of long paragraphs such as legal clauses and application information in administrative licensing documents, avoiding splitting key legal requirements |
chunkSplitter | Split by paragraph | Matches the legal format structure of documents, retains field associations within paragraphs, and aligns with the reading and review logic of approval materials |
customPdfParse | Enabled | Performs enhanced parsing for form fields and stamp areas of government PDF documents, improving recognition accuracy of key identifiers such as document numbers and subject information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of batch multi-page approval file materials, avoiding entire batch task failures caused by timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of entire batches of licensing file materials, meeting the batch processing needs of daily approval archiving |
chunkSettingMode | Manual configuration | Allows adjustment of chunking parameters for different types of approval documents, adapting to format differences from cross-system sources |
Three common configuration errors
- Enabling
customPdfParsewithout effective results, where the parsing result cannot recognize form fields. The cause is failing to enable the dependent components for PDF enhanced parsing in the system backend, or failing to specify the document format as a government PDF template. - Returning parameter errors when calling the API to pull the document library. The cause is confusing the calling specifications of the FastGPT open API and internal interfaces, and failing to pass the correct configuration parameters as required by the documentation.
- Key approval document number fields being split into different text chunks after chunking. The cause is that
chunkSizeis set too small, and the paragraph-first chunking logic is not enabled, resulting in mandatory splitting of legal identifier fields.
How to confirm the configuration is correct
- Upload a standard administrative licensing application form PDF, and check whether core fields such as applicant information, item name, and legal basis are fully extracted in the parsing result.
- View the running logs of the parsing task, and confirm that no timeout errors occur due to insufficient
PARSE_FILE_TIMEOUT_SECONDSsettings. - Randomly sample chunked text chunks, and check whether legal document numbers or key approval clauses are split.
- Call the batch import API, upload test materials under 100MB, and confirm that the parsing task completes normally with no field loss.
The values listed above are common starting points and should be evaluated 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.