What this category of data looks like
Data sources for administrative licensing and approval scenarios include the national integrated government service platform item database, administrative licensing item lists, departmental business approval systems, and electronic certificate databases. The update frequency for these sources is daily. Commonly processed documents include service guides, application forms, commitment letters, acceptance notices, correction notification letters, administrative licensing approval decisions, and license certificates. These documents have clear fields such as item numbers, legal basis clauses, and handling conditions. Some documents include structured tables and legal texts in formal official document format.
Constraints on the document parsing and chunking link
The daily data update requirement means the parsing and chunking process must adapt to format differences from different system exports. This prevents parsing failures from format incompatibility. Document types range from structured tables to official documents. Parsing tools must retain title hierarchy and paragraph integrity. They must not arbitrarily split legal basis clauses. Legal basis fields must be kept exactly as their original text. Split chunks by clause as the smallest unit. Otherwise, this will disrupt subsequent legal matching for Q&A. Parsing complex government documents takes significant time. Set a reasonable timeout threshold to avoid data synchronization interruptions from timeouts.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Legal basis paragraphs of administrative licensing documents are usually hundreds of characters long. This range retains the integrity of a single basis clause. |
chunkSplitter | Split by title + paragraph | Administrative licensing documents have clear item titles and basis paragraphs. This splitting method preserves contextual association. |
customPdfParse | Enabled | Government PDF documents often contain legal texts in official formats. Custom parsing retains original typesetting and field structure. |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Parsing complex government PDFs or batch list documents takes significant time. This range covers most scenarios. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading batch administrative licensing item lists or batch electronic certificate files. |
chunkSettingMode | Manual configuration | Separate adjustment of chunking rules for the legal basis of administrative licensing documents is required. This avoids damaging clause integrity with generic chunking. |
Three Common Mistakes
- Symptom: After uploading a government PDF, FastGPT reports a timeout. The parsing service log shows parsing succeeded. Cause: The
PARSE_FILE_TIMEOUT_SECONDSvalue is set too small. It does not adapt to the long parsing time caused by complex typesetting of government PDFs. - Symptom: Some model calls do not automatically parse uploaded government documents. Cause:
customPdfParseis not enabled, andchunkSettingModedoes not match the title structure of government documents. Parsing trigger conditions are not met. - Symptom: After uploading an Excel-format administrative licensing application form, the parsed result does not include field content from the table. Cause: No chunking rules for structured tables are configured. The default chunking does not recognize the table data structure.
How to Confirm the Configuration is Correct
- Upload a standard administrative licensing service guide PDF. Check if parsed chunks retain complete legal basis paragraphs, with no cross-clause splitting.
- Check the system parsing log. Confirm that parsing time does not exceed the set
PARSE_FILE_TIMEOUT_SECONDSthreshold. - Upload an Excel-format administrative licensing application form. Check if the parsed result includes all field content from the table.
- Call different models to initiate Q&A. Confirm that the model can generate corresponding answers based on parsed chunk content.
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.