What Data for This Category Looks Like
The core data for this category comes from the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics, and remote sensing images. The update frequency is once per year. Commonly processed documents include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Document structures include multi-chapter hierarchies, spatial coordinate fields, planning indicators such as construction land scale, and unstructured public opinion text. Some documents embed nested tables and remote sensing image annotations.
Constraints on Document Parsing and Chunking From These Characteristics
Annually updated documents are usually large. Some planning reports include multi-source spatial data attachments, which demand specific parsing durations and resource usage. Document hierarchical structures tie closely to spatial indicators. Parsing processes must avoid separating chapter titles from corresponding planning constraint content. Both unstructured public opinion summary text and standardized planning indicators exist. Parsing adaptation must balance handling of general text and structured fields. Embedded nested tables and remote sensing image annotations require parsing logic to retain original typesetting and field associations. This prevents information loss that would reduce the accuracy of subsequent consultation responses.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to typical planning report paragraph lengths, and preserves contextual association between chapter titles and corresponding planning indicators |
chunkSplitter | Split by title + paragraph | Matches planning document hierarchical structures, and avoids separating three zones and three lines annotations and explanatory text into different chunks |
customPdfParse | Enabled | Optimizes parsing logic for nested tables and remote sensing image annotations embedded in planning reports, and retains complete original typesetting and fields |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses large-volume parsing requirements for annual updated single planning reports, and avoids interruptions from excessive time consumption |
chunkSettingMode | Manual mode | Creates custom chunking rules for special spatial fields in planning documents, to prevent constraint indicators from being incorrectly split |
Three Common Configuration Mistakes
- Symptom: Table content is lost after importing Word or PPT documents, even with
customPdfParseenabled. Cause:customPdfParseonly optimizes parsing for PDF formats. No general document parsing adaptation rules are configured. - Symptom: Only two columns of data appear in parsing results after uploading an Excel-format planning indicator table. Cause:
chunkSplitteris not adjusted to handle multi-column tables. The default chunking rule only recognizes single-column table content. - Symptom: Timeout error occurs when parsing a large special planning report. Cause:
PARSE_FILE_TIMEOUT_SECONDSis set lower than the actual parsing time for a single document. No time is reserved for processing spatial data.
How to Verify Proper Configuration
- Upload a single typical detailed planning report supported by FastGPT v4.17.0. View the parsed chunk list to confirm each chunk contains a complete chapter title and corresponding paragraph.
- Import an Excel-format planning indicator table. Verify that all data columns are retained after parsing, with no truncation or missing content.
- Adjust
chunkSizeto the 800-character and 1200-character boundary values. Confirm chunk length matches expectations, and that indicators such as construction land scale are not split across two chunks. - Upload a planning implementation evaluation report larger than 100MB. Confirm no timeout error occurs during parsing, and that
PARSE_FILE_TIMEOUT_SECONDSmeets actual parsing requirements.
The values provided here 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.