Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Typical data for this category comes from daily office and approval workflows at government service centers and administrative approval bureaus. Document types include service guides, application forms, acceptance notices, correction notices, and more. Data sources include the national integrated government service platform item database, departmental business approval systems, and electronic certificate repositories. Updates occur daily. Documents contain structured fields such as legal item identifiers, material list entries, and approval process nodes. They also include unstructured text content such as correction notices and approval opinions.
What constraints these features place on document parsing and chunking
Administrative licensing and approval documents have diverse sources and update frequently. New approval materials and item updates each day require the parsing link to support real-time response. Documents include both structured fields and unstructured text. Chunking must avoid breaking the binding relationship between legal items and their corresponding materials. This ensures accuracy when matching subsequent handling conditions. Approval document formats vary across departments. Parsing logic must adapt to official documents such as forms and stamped pages. This prevents key identifier information from being lost during chunking.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Administrative licensing documents contain both short fields and long text. This range balances the integrity of structured fields and the semantic coherence of long text |
chunkSplitter | Split by legal item identifier + paragraph | Administrative licensing documents center on legal items. Splitting by item identifiers ensures each chunk corresponds to a single approval item, avoiding confusion across items |
customPdfParse | Enabled | Most administrative licensing documents are official PDFs with stamps and forms. Enabling custom PDF parsing preserves parsing integrity for form fields and stamped areas |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers upload requirements for large electronic certificate packages and batch approval materials, adapting to batch submission scenarios in government scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large approval material parsing requires sufficient time, to avoid parsing failure due to timeout |
chunkSettingMode | Manual mode | Allows adjustment of chunking parameters for different types of administrative licensing documents such as service guides and approval decisions, adapting to scenario-based needs |
Three common errors
- An "offset out of range" error is prompted when a PDF file upload reaches 90% progress. This occurs when
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSare not adjusted. Large file parsing may experience timeout or insufficient memory allocation. - Documents uploaded via the create file collection API have inconsistent chunking results compared to documents uploaded directly via the platform. This occurs when
chunkSettingModeandchunkSplitterparameters are not unified. Default parameters differ across upload channels. - No parsed data is present after importing administrative licensing documents via a custom URL. This occurs when the custom URL cannot access government intranet resources, and
customPdfParsehas not been enabled to adapt to official PDF formats.
How to confirm the configuration is correct
- Upload a single official PDF approval material larger than 100 MB. Check that the parsing progress completes without errors.
- Upload the same service guide document via the platform and via the API separately. Compare the entry consistency of the chunking results.
- After enabling
customPdfParse, check that the parsed results retain the legal item number field in the form. - View the parsing log. Confirm that
PARSE_FILE_TIMEOUT_SECONDSdoes not trigger timeout errors.
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.