What Data in This Category Looks Like
This category of data comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. The update frequency is monthly. Commonly processed documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Most document structures include fixed fields such as data item codes, open subjects, desensitization methods, interface addresses, and update cycles. Some documents contain structured tables and interface parameter descriptions.
Constraints for Document Parsing and Chunking
Batch documents updated monthly require parsing processes to efficiently handle multiple files and adapt to fixed format templates. Multiple document types (plain text, tables, interface documents) require matching different parsing rules to prevent incorrect splitting of structured fields. Fixed fields require retaining field relevance during chunking, to avoid separating data items from their corresponding desensitization rules and interface information. Parameters and call restrictions in interface documents must be fully preserved, to prevent truncation of key configuration information during chunking. The uniqueness of data items requires that original data can still be linked via their codes after chunking. This ensures that public data information referenced in party building learning Q&A aligns accurately.
Configuration Settings
These settings apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field length of public data catalog documents and the paragraph structure of interface documents, avoids splitting key data item information |
chunkSplitter | Split by title + paragraph | Matches the fixed title structure of public data catalogs, retains the connection between data items and their corresponding descriptions |
customPdfParse | Enabled | Adapts to formatted PDF files including desensitization rules and interface documents, preserves table and field layout |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to large catalog files updated in monthly batches, prevents task failure from parsing timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of full public data catalog files updated monthly |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types (catalogs, interface documents) to fit varied content structures |
Three Common Configuration Mistakes
- Symptom: Table fields are missing after PDF parsing, and the parameter list of interface documents is truncated. Cause: The
customPdfParseconfiguration is not enabled. The default parsing logic cannot retain structured tables and long paragraph content. - Symptom: Parsing tasks do not respond for an extended period after triggering, and eventually return a timeout error. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual parsing time of large catalog documents, and does not fit the file size of monthly batch updates. - Symptom: Imported CSV-format data open lists only display the first two columns of content. Cause: Corresponding chunking rules are not configured for structured data catalogs, and the default logic does not recognize the association between multiple column fields.
How to Verify Proper Configuration
- Upload a single public data catalog PDF file, check if all table fields and title levels are retained in the parsed content.
- Upload batch catalog files updated monthly, confirm that parsing task duration falls within the range set for
PARSE_FILE_TIMEOUT_SECONDS. - Import a CSV-format data open list, confirm that all column contents can be loaded and parsed normally.
- Adjust the
chunkSizeparameter, verify that chunking results do not truncate the association between data item codes and their corresponding open conditions.
The values provided here are common starting points. They should be evaluated against samples relevant to the specific use case.
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.