What this category of data looks like
This category of data mainly comes from the National Land Space Planning One Map, Project Approval and Supervision Platform, Current Land Use Database, and Ecological Protection Red Line Data. Commonly processed documents include land pre-examination and site selection opinions, construction land planning permits, construction engineering planning permits, red line diagrams, and planning conditions. Data updates occur irregularly. Most documents contain fields such as project site selection scope, land use scale, planning control requirements, and red line coordinates. Some drawing documents carry vector coordinates and spatial constraint information.
What constraints do these characteristics impose on the document parsing and chunking link
Data sources include spatial drawings and structured approval texts. The parsing link must adapt to both plain text approval documents and red line diagram documents with coordinates. Data updates occur irregularly. Batch processing of documents with different generation times must be supported to avoid format adaptation deviations. Fields such as land use scale, planning control requirements, and red line coordinates have strong correlations. Chunking must retain the contextual association of adjacent fields to avoid inability to restore approval logic after splitting. Some documents carry spatial constraint information. Chunking strategies must avoid destroying the integrity of spatial information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents contains precise approval fields. Chunk length must cover a single complete control requirement or plot information to avoid splitting key associated content |
chunkSplitter | By paragraph + title boundary | Most documents in this category carry approval hierarchy titles and paragraphized control requirements. Splitting by title and paragraph preserves logical integrity |
customPdfParse | Enabled | This category includes PDF documents with images such as red line diagrams. Enabling custom parsing supports extraction of mixed drawing and text content |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large red line diagrams or multi-page approval documents takes a long time. Sufficient processing time must be reserved |
chunkSettingMode | Manual configuration | Documents in this category have large structural differences. Manual configuration allows flexible adjustment of chunking rules to adapt to different document types |
Three Common Misconfigurations
- Symptom: Parsed documents lack red line coordinates or spatial constraint information. Cause: The
customPdfParseconfiguration is not enabled. Default parsing alone cannot extract embedded PDF drawing vector data. - Symptom: Uploaded planning condition table documents have no structured table content after parsing. Cause: Table recognition related settings are not enabled. Default parsing does not extract structured table data.
- Symptom: Parsing tasks trigger timeout failures. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting value is too small to complete complete parsing of large approval documents.
How to Confirm Configuration is Correct
- Upload a single land pre-examination and site selection opinion. Check if the parsing result in FastGPT v4.17.0 contains complete project site selection scope and land use scale fields.
- Upload a PDF document with a red line diagram. Confirm that after
customPdfParseis enabled, the parsing result contains the coordinate description information of the drawing. - Adjust
chunkSizeto1000 characters, upload a multi-page planning condition document, check if chunking retains the contextual association of adjacent control requirements. - Upload the largest specification approval document, confirm that the parsing task does not trigger a timeout, and verify that the
PARSE_FILE_TIMEOUT_SECONDSsetting meets processing requirements.
The values listed here are common starting points. They 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.