What data for this category looks like
Daily documents for this category include territorial spatial master plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Data sources are the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics data, and remote sensing images. The update frequency is annual. Most documents contain spatial coordinate annotations, constraint indicator values, regional population and economic statistics fields. Some files include multi-column planning tables and embedded remote sensing image screenshots, with complex structural hierarchies. Cross-document integration of multi-source data is often required.
What constraints these characteristics impose on the document parsing and chunking link
Multi-source cross-document data updated annually requires the parsing module to support batch reading of planning files in different formats. Documents containing spatial coordinates, multi-column planning tables, and embedded remote sensing images require adaptation to non-standard table structures and spatial text chunking logic, to avoid losing contextual association of spatial data during splitting. The requirement for multi-document integrated tabulation requires retaining cross-file source identifiers during chunking, to ensure that corresponding data fields can be matched during subsequent summary work, and guarantee the accuracy of planning compilation and evaluation.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of spatial indicators and table paragraphs in planning documents, avoiding splitting that disrupts the context of spatial data |
customPdfParse | Enabled | Supports parsing planning PDF documents with embedded remote sensing images and multi-column layouts, retaining graphic-text association relationships and binding storage |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Addresses time-consuming requirements for large-volume remote sensing image-associated PDFs and batch parsing of multiple documents |
chunkSplitter | By paragraph + heading hierarchy | Matches the chapter structure of planning documents, avoiding splitting of planning indicator content across chapters |
chunkSettingMode | Manual configuration | Adjusts chunking rules for the special structure of spatial planning documents, adapting to multi-source data integration needs |
Three common mistakes
- After uploading a planning PDF containing remote sensing images, the parsing result does not include the supporting explanatory text of the images. The cause is that the
customPdfParseconfiguration is not enabled. The default parsing mode cannot extract the supporting explanatory text of embedded images. - The fields of multi-column planning tables are disordered after parsing, and the correct column correspondence cannot be identified. The cause is that non-standard table layouts are not adapted, and the chunking logic of
chunkSplitteris not adjusted to retain the table structure. - A timeout error occurs during batch parsing of annual planning documents, and the task is terminated. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSsetting value is too low, failing to cover the parsing time of large-volume remote sensing image-associated documents.
How to confirm the configuration is correct
- Upload a single planning PDF containing embedded remote sensing images, and check whether the parsing result includes the supporting explanatory text of the images and spatial coordinate fields.
- Import a planning table document with a multi-column layout, and verify that the parsed table column order is consistent with the original document, with no field misalignment.
- Upload a single planning document with a volume exceeding 500MB, and confirm that the parsing task does not trigger a timeout error.
- Batch upload 3 to 5 planning documents in different formats, and check whether the chunking result retains the chapter hierarchy and source identifier of each document.
The values provided are common starting points and should be evaluated 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.