Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data sources include social insurance management systems, tax social insurance premium collection systems, market entity registration information, and shared public security and civil affairs data. The update frequency is daily. Commonly processed documents include insurance registration forms, premium base declaration forms, staff change forms, social insurance premium verification notices, and personal benefit records. Most documents contain structured fields such as unit name, unified social credit code, insured person's name, ID number, and premium base. Some documents include tabular batch staff information, mostly in PDF or Excel format.
What constraints do these characteristics impose on the "document parsing and chunking" link
Daily updated batch documents require the parsing process to support high-frequency batch processing. Multiple types of structured documents need to retain associations between fields, to avoid splitting core information such as identity and premium base across chunks. Documents from cross-system sources have inconsistent formats, so the system must adapt to different header structures. The accuracy of parsing core fields directly impacts subsequent business. When chunking, avoid truncating key associated information, and control the length of individual chunks to fit the context window of subsequent Q&A.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Insurance registration-related documents contain associated fields. Context must be retained for unit and staff information to avoid splitting core associations across chunks |
chunkSplitter | Split by title + paragraph + table block | Documents include tabular batch staff information. Splitting by semantic blocks preserves the complete data structure within tables |
customPdfParse | Enabled | Official standardized insurance registration forms have fixed headers. Custom parsing accurately extracts structured fields and prevents omissions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Single unit annual premium declaration materials may include multiple pages of staff change forms. This setting adapts to large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing batch staff change forms takes significant time. This setting reserves sufficient parsing duration |
chunkSettingMode | Manual mode | Chunking rules need adjustment for different document types to adapt to the differentiated document structures of insurance registration and premium verification |
Three common mistakes
- Phenomenon: Table fields are empty after parsing. Cause:
customPdfParseis not enabled, and custom parsing rules are not configured for the fixed headers of insurance registration forms. - Phenomenon: Parsing task triggers timeout errors. Cause:
PARSE_FILE_TIMEOUT_SECONDSis set lower than actual parsing duration, without accounting for processing time for batch documents. - Phenomenon: Insured staff identity information and premium base are split across different chunks. Cause:
chunkSizeis set too small, failing to retain context associations for associated fields.
How to confirm the configuration is correct
- Upload a single standardized insurance registration form, check if the parsed text fully retains the header and complete information for a single staff member.
- Upload a 100MB batch staff change form, confirm the parsing task completes within 600 seconds without triggering timeout errors.
- Adjust
chunkSizeto 1000 characters, upload a premium base declaration form, and verify if the chunking results retain the association information between the unit and corresponding staff's premium details. - Call the file upload API, pass the
fileparameter according to documentation requirements, and confirm the parsed chunked data includes correct structured fields.
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.