What Data for This Category Looks Like
Entities including big data bureaus and government service data administration bureaus maintain this category of data. Daily documents include public data resource catalogs, data opening lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data sources cover local public data opening platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. Update frequency follows a monthly schedule. Document structures primarily use structured table lists, paired with text descriptions and interface parameter descriptions. Fields include data item names, opening conditions, interface addresses, desensitization rules, update cycles, and additional relevant fields.
Constraints for Document Parsing and Chunking
Batch data files updated monthly require parsing workflows to support bulk import scenarios, avoiding single parsing timeouts. Documents mix structured tables and short text descriptions. Chunking processes must preserve the full integrity of tables, to prevent splitting data items and causing inconsistencies between catalogs and actual data. Some documents use scanned image formats. Parsing workflows must support OCR text extraction, otherwise content from legacy existing catalogs will be lost. Sensitive fields such as desensitization rules require retained contextual associations. Chunking must not separate fields from their corresponding descriptions, to prevent post-parsing data leakage risks. Interface document parameters and call restrictions must be fully preserved, otherwise errors will occur during open interface configuration.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Public data catalog documents typically contain complete descriptions of single data items. This range preserves full contextual integrity for a single data item |
chunkSplitter | Split by paragraph + table | This category of documents mixes text descriptions and structured table lists. Tables must be retained as independent chunk units during splitting |
customPdfParse | Enable OCR mode | Adapt to scanned image format public data catalogs from legacy existing stocks, to fully extract scanned text content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Cross-departmental data catalog files for monthly bulk imports have relatively large capacity. Sufficient timeout time is required to complete full parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapt to large data list files exported monthly, to avoid import failures due to file size limits |
chunkSettingMode | Custom mode | This category of documents has wide structural variation. Chunking rules can be adjusted for different document types such as catalogs and interface documents |
Three Common Configuration Mistakes
- Symptom: Parsing results lack table data, or table datasets cannot be selected in the interface. Cause: Structured table recognition configuration is not enabled, or chunking rules forcibly skip table content.
- Symptom: Incomplete content is returned after parsing a file, or a parsing timeout error is triggered. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to an adapted value, or a single file exceeds theUPLOAD_FILE_MAX_SIZElimit. - Symptom: Incorrect chunking logic results from misunderstanding chunking parameters. Cause: Parameters are not adjusted based on the structured field characteristics of public data catalogs, and pure text paragraph splitting logic is mistakenly applied to documents containing structured tables.
How to Verify Correct Configuration
- Upload a single typical public data resource catalog PDF, and check if the parsing result includes complete table data items and corresponding descriptive text.
- Adjust the
chunkSizeparameter to different ranges, upload a test document, and compare if chunking results retain the full context of a single data item. - Upload a test file larger than conventional size, and confirm the parsing process does not trigger timeout or file size limit errors.
- After enabling OCR mode, upload a catalog list in scanned image format, and check if scanned text content can be fully extracted.
The values provided are common starting points and should be evaluated against the reader’s own sample documents.
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.