What this category of data looks like
This category of data mainly comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. Commonly processed documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data is updated monthly. Most documents contain structured fields such as data item names, open entities, desensitization requirements, interface addresses, update cycles, and authorization boundaries. Some documents are table-formatted datasets or structured interface description files.
What constraints do these characteristics impose on document parsing and chunking
Monthly updated batch documents need a stable batch parsing process to avoid repeated configuration adjustments. Structured table documents require accurate recognition of row and column associations. This stops chunking from breaking the binding logic between data items and their corresponding desensitization rules and open conditions. Interface documents and desensitization rules use specialized government terminology. Chunking must preserve terminology integrity to avoid semantic fragmentation. Documents from multiple sources have large format differences. The system must support tables, plain text, interface descriptions, and other formats. Chunking must also not split sensitive information units that include authorization boundaries.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic unit length of government documents, avoiding splitting the association between data items and their corresponding rules |
chunkSplitter | Split by title + paragraph | Adapts to the hierarchical structure of public data catalogs, retaining the binding relationship between data items and open conditions |
customPdfParse | Enable table recognition mode | Adapts to PDF table documents such as desensitization rules and data open lists, accurately extracting row and column data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large catalog files uploaded in monthly batches, avoiding upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large structured documents, preventing timeout interruptions |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types (catalogs, interface documents, desensitization rules) to ensure semantic integrity |
Three common errors
- Phenomenon: After importing an Excel-formatted public data open list, the knowledge base cannot return table content during retrieval. Cause: The table recognition mode of
customPdfParseis not enabled, causing table data to be incorrectly split or ignored. - Phenomenon: Association information between data items and desensitization rules is lost in chunked document fragments. Cause: The
chunkSizesetting is too small, splitting semantic units that contain complete business logic. - Phenomenon: Parsing tasks time out and fail when uploading monthly updated catalog files in batches. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than actual parsing time, failing to adapt to the processing needs of large structured documents.
How to confirm the configuration is correct
- Upload a single typical document, such as a data open list Excel file, and check if the parsed text fully retains the row and column association information of the table.
- Batch upload 3 to 5 monthly updated catalog files, and verify that all parsing tasks complete without timeout errors.
- Retrieve fragments that include data items and their corresponding open conditions, confirming that chunked content does not split key associated information.
- Adjust the
chunkSizeparameter, compare chunking results under different values, and select the configuration that retains semantic integrity.
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.